International Journal of Pharmaceutical Research and Allied Sciences IJPRAS
2024 Volume 13 Issue 2
Creative Commons License

Exploring Recent Updates on Molecular Docking: Types, Method, Application, Limitation & Future Prospects

Mahendra Kumar Sahu*✉ , Amit Kumar Nayak , Bisrat Hailemeskel , Ozan Emre Eyupoglu
  1. MATS School of Pharmacy, MATS University Raipur, CG, India.
  2. College of Pharmacy, Howard University, USA.
  3. Department of Biochemistry, School of Pharmacy, Istanbul Medipol University, Istanbul, Turkey.
Abstract

Molecular docking serves as a crucial computational tool in the realm of drug discovery and development, aimed at understanding the interactions between small molecules and target proteins. This study delves into the objective of elucidating the evolution of molecular docking techniques, their current applications, and potential future directions. Through a comprehensive review of the literature and analysis of recent advancements, this study provides insights into the methodologies employed in molecular docking studies. From the traditional rigid docking approaches to the more sophisticated flexible and ensemble docking methods, the evolution of techniques is discussed, highlighting their strengths and limitations. The past decade has witnessed significant strides in the field of molecular docking, with improvements in algorithms, scoring functions, and accessibility of computational resources. However, challenges persist, including accurate prediction of ligand binding affinities and consideration of protein flexibility. Additionally, the advent of hybrid approaches combining docking with molecular dynamics simulations presents exciting opportunities for more realistic modeling of ligand-protein interactions. In conclusion, molecular docking continues to be a cornerstone in structure-based drug design, facilitating the identification and optimization of lead compounds. Despite notable advancements, there remains a need for further refinement in docking methodologies to address current limitations and harness the full potential of computational techniques in accelerating the drug discovery process.  

Keywords: Molecular docking, Types, Application, Limitation

INTRODUCTION

Molecular docking is a computational technique that plays a pivotal role in drug discovery and design by predicting the preferred orientation of small molecules when bound to target proteins. This process aids in understanding the interactions between molecules and proteins, facilitating the development of new therapeutic agents. The history of molecular docking traces back to the 1980s, with continuous advancements in algorithms and computational power enhancing its accuracy and efficiency [1]. Today, molecular docking is a fundamental tool in pharmaceutical research, enabling the identification of potential drug candidates and the optimization of their binding interactions. This study provides a concise overview of the significance and evolution of molecular docking in the field of drug discovery [2].

It allows researchers to simulate and analyze the interactions between the drug candidate and the target protein, helping to identify potential binding sites and optimize the drug's binding affinity and specificity. Molecular docking is a valuable tool for screening large libraries of compounds and predicting their potential as drug candidates [3]. On the other hand, manual drug study involves a more traditional approach where researchers experimentally test and analyze the interactions between drug candidates and target proteins. This process often involves experiments, such as biochemical assays and crystallography, to understand the binding mechanisms and optimize the drug's efficacy. While molecular docking offers a faster and more cost-effective way to screen and analyze potential drug candidates, manual drug study provides more detailed and accurate information about drug-target interactions [4]. Both approaches have their strengths and limitations, and they are often used in combination to complement each other in the drug discovery process. Ultimately, the choice between molecular docking and manual drug study depends on the specific research goals, resources available, and the complexity of the drug-target interactions being studied [5].

The era of molecular docking in drug discovery can be traced back to the late 20th century, with significant advancements and milestones marking its evolution over the years. In the 1980s, the first algorithms for molecular docking were developed, laying the foundation for predicting the binding modes of small molecules to target proteins. These early efforts focused on understanding the structural and energetic aspects of molecular interactions to facilitate drug design. During the 1990s, advancements in computational power and algorithms led to the refinement of molecular docking techniques, improving the accuracy and efficiency of predicting ligand-protein interactions [6]. This era saw the development of various scoring functions and search algorithms to better simulate the binding process and identify potential drug candidates. In the early 2000s, molecular docking gained widespread recognition as a valuable tool in drug discovery, with pharmaceutical companies and research institutions incorporating it into their drug development pipelines [7].

This study delves into the objective of elucidating the evolution of molecular docking techniques, their current applications, and potential future directions. Through a comprehensive review of the literature and analysis of recent advancements, this study provides insights into the methodologies employed in molecular docking studies.

RESULTS AND DISCUSSION

The integration of molecular docking with other computational and experimental methods further enhanced its utility in rational drug design and optimization. Today, molecular docking continues to play a pivotal role in drug discovery, enabling researchers to screen large compound libraries, predict binding affinities, and optimize drug candidates with greater precision. The field of molecular docking is constantly evolving, with ongoing research focusing on improving accuracy, speed, and applicability to diverse biological targets. Overall, the history of molecular docking reflects a progressive journey of innovation and refinement, shaping its significance as a key computational tool in modern drug discovery efforts. There are several types of molecular docking methods commonly used in drug discovery and computational biology. Some of the main types of molecular docking methods are provided in Table 1.

Table 1. Types of molecular Docking their method, application & limitation

S. No.

Types of Molecular Docking

Method

Application

Limitation

References
  1.  

Rigid Docking
  1. Preparation
  1. Post-processing and Analysis
  1. Neglect of Flexibility

[8]
  1. Search Algorithm
  1. Protein-Protein Interactions
  1. Scoring Function Accuracy

[9]
  1. Scoring Function
  1. Enzyme Mechanisms
  1. Computational Cost

[10]
  1. Post-processing and Analysis
  1. Virtual Screening
  1. Treatment of Solvent Effects

[11]
  1.  

Flexible Docking
  1. Docking Algorithm
  1. Computational Cost
  1. Computational Cost

[12]
  1. Scoring Function
  1. Scoring Function Accuracy
  1. Scoring Function Accuracy

[13]
  1. Induced-Fit Modeling
  1. Conformational Sampling
  1. Conformational Sampling

[14]
  1. Post-processing and Analysis
  1. Modeling Receptor Flexibility
  1. Modeling Receptor Flexibility

[15]
  1.  

Induced Fit Docking
  1. Conformational Sampling
  1. Lead Optimization
  1. Computational Cost

[16]
  1. Docking Algorithm
  1. Fragment-Based Drug Design
  1. Scoring Function Accuracy

[17]
  1. Scoring Function
  1. Virtual Screening
  1. Conformational

[18]
  1. Post-processing and Analysis
  1. Understanding Binding Mechanisms
  1. Sampling

[19]
  1.  

Ligand-Based Docking
  1. Ligand Selection and Dataset Preparation
  1. Lead Identification
  1. Dependency on Reference Ligands

[20]
  1. Pharmacophore Generation
  1. Virtual Screening
  1. Limited Structural Information

[21]
  1. Similarity Search and Virtual Screening
  1. Lead Optimization
  1. Scoring Function Performance

[22]
  1. Scoring and Validation
  1. Bioisosteric Replacement
  1. Target Flexibility

[23]
  1.  

Protein-Protein Docking
  1. Preparation of Protein Structures
  1. Structural Elucidation
  1. Conformational Sampling

[24]
  1. Search Algorithm
  1. Drug Discovery
  1. Scoring Function Accuracy

[25]
  1. Scoring Function
  1. Functional Annotation
  1. Treatment of Protein Flexibility

[26]
  1. Post-processing and Analysis
  1. Virtual Screening
  1. Experimental Validation

[27]
  1.  

Blind Docking
  1. Grid Generation
  1. Target Identification
  1. Computational Cost

[28]
  1. Ligand Sampling
  1. Virtual Screening
  1. Sampling Limitations

[29]
  1. Docking Algorithm
  1. Fragment-Based Drug Design
  1. Scoring Function Accuracy

[30]
  1. Scoring Function
  1. Protein Function Prediction
  1. Treatment of Receptor Flexibility

[31]

 

Rigid docking

In this type, both the ligand (small molecule) and the receptor (target protein) are held fixed during the docking process. This method is faster but may not account for conformational changes in the protein upon ligand binding [32]. Rigid docking, also known as rigid-body docking or geometric docking, is a computational technique used in the field of molecular modeling to predict the binding mode and affinity between a ligand (small molecule) and a receptor (usually a protein) at the atomic level. This method assumes that both the ligand and the receptor maintain their rigid structures during the docking process, neglecting any conformational changes that may occur upon binding [33].

Principles of rigid docking

At its core, rigid docking relies on geometric complementarity between the ligand and receptor structures. The aim is to predict the most energetically favorable conformation of the ligand within the binding site of the receptor. This involves searching through the vast conformational space of the ligand to find the pose that maximizes favorable interactions such as hydrogen bonding, van der Waals forces, and hydrophobic interactions while minimizing steric clashes [34].

Methods of rigid docking

Several computational algorithms and software tools have been developed for rigid docking. These methods generally involve the following steps:

  1. Preparation: The receptor and ligand structures are prepared by removing water molecules, adding hydrogen atoms, assigning partial charges, and optimizing the geometry if necessary [35].
  2. Search algorithm: Docking algorithms employ various search strategies to explore the ligand's conformational space and find the optimal binding pose. Common search algorithms include geometric hashing, Monte Carlo methods, genetic algorithms, and systematic grid-based search.
  3. Scoring function: After generating candidate ligand poses, a scoring function is used to evaluate and rank these poses based on their predicted binding affinity [36]. Scoring functions typically consider factors such as geometric complementarity, electrostatic interactions, van der Waals forces, and desolation energy.
  4. Post-processing and analysis: Finally, post-processing techniques are applied to refine the predicted binding poses and analyze the intermolecular interactions. Visualization tools allow researchers to examine the docked complexes and identify key binding residues and interactions [37].

Applications of rigid docking

Rigid docking has diverse applications in drug discovery, structural biology, and molecular design:

  1. Drug discovery: Rigid docking is widely used in computer-aided drug design (CADD) to screen large libraries of chemical compounds and identify potential drug candidates that bind to a target protein with high affinity and specificity [38].
  2. Protein-protein interactions: Rigid docking can also be applied to predict the binding mode between two protein molecules, providing insights into protein-protein interactions and facilitating the design of protein inhibitors or modulators.
  3. Enzyme mechanisms: By docking small molecules into the active sites of enzymes, researchers can elucidate enzyme-substrate interactions and propose mechanisms of enzyme catalysis, aiding in rational drug design and enzyme engineering.
  4. Virtual screening: Rigid docking is an essential component of virtual screening workflows, where large databases of chemical compounds are screened computationally to prioritize compounds for experimental testing based on their predicted binding affinity to a target protein [39].

Limitations of rigid docking

While rigid docking is a valuable tool in molecular modeling, it has several limitations:

  1. Neglect of flexibility: Rigid docking assumes that both the ligand and receptor maintain rigid structures, neglecting conformational changes that may occur upon binding. This limitation can lead to inaccuracies in predicting binding affinities and modes, particularly for flexible ligands or receptors.
  2. Scoring function accuracy: The accuracy of rigid docking predictions depends heavily on the scoring function used to evaluate binding poses. However, current scoring functions may not capture all relevant molecular interactions accurately, leading to false positives or false negatives in the predictions.
  3. Computational cost: Docking simulations can be computationally intensive, especially when exploring large conformational spaces or performing high-throughput virtual screening. This limits the size of the systems that can be studied and requires efficient algorithms and parallel computing resources [40].
  4. Treatment of solvent effects: Rigid docking typically assumes a dry binding site and neglects the influence of solvent molecules on ligand-receptor interactions. However, solvent effects can significantly affect binding affinity and selectivity, necessitating more sophisticated modeling approaches. In conclusion, rigid docking is a powerful computational technique for predicting the binding mode and affinity of ligand-receptor interactions at the molecular level. Despite its limitations, rigid docking has found widespread applications in drug discovery, structural biology, and molecular design, helping researchers understand biomolecular recognition processes and accelerate the development of novel therapeutics. Continued advancements in docking algorithms, scoring functions, and computational resources promise to enhance the accuracy and efficiency of rigid docking simulations in the future [41].

 

Flexible docking

Flexible docking allows for flexibility in either the ligand, receptor, or both during the docking process. This accounts for conformational changes and can provide more accurate predictions of binding modes. Flexible docking, also known as flexible ligand docking or induced-fit docking, is a computational technique used in molecular modeling to predict the binding mode and affinity between a ligand and a receptor while accounting for flexibility in both the ligand and the receptor structures. Unlike rigid docking, which assumes that both the ligand and receptor maintain rigid conformations during binding, flexible docking considers the conformational changes that may occur in both the ligand and receptor upon binding [42].

Principles of flexible docking

The key principle behind flexible docking is to account for the dynamic nature of biomolecular structures and the induced-fit phenomenon observed in ligand-receptor interactions. When a ligand binds to a receptor, both the ligand and receptor may undergo conformational changes to optimize their interactions and achieve a stable bound complex. Flexible docking aims to predict both the binding pose and the conformational changes of the ligand and receptor, allowing for a more accurate representation of the binding process [43].

Methods of flexible docking

Methods typically involve the following steps:

Conformational Sampling: Unlike rigid docking, flexible docking involves sampling multiple conformations of both the ligand and receptor structures to explore the conformational space and identify energetically favorable binding poses. This can be achieved using techniques such as molecular dynamics simulations, normal mode analysis, or systematic conformational search algorithms.

  1. Docking algorithm: Docking algorithms in flexible docking often combine conformational sampling with traditional docking search strategies to predict the optimal binding pose of the ligand within the flexible binding site of the receptor. These algorithms may use scoring functions to evaluate the compatibility of each ligand conformation with the receptor and guide the search toward the most favorable binding poses.
  2. Scoring function: Scoring functions in flexible docking are crucial for assessing the energetics of ligand-receptor interactions and ranking the predicted binding poses. These scoring functions typically consider factors such as geometric complementarity, electrostatic interactions, van der Waals forces, solvation effects, and conformational strain.
  3. Induced-Fit modeling: In some flexible docking approaches, induced-fit modeling techniques are used to explicitly model conformational changes in the receptor upon ligand binding. This may involve flexible side-chain modeling, loop refinement, or even full protein backbone flexibility to capture the induced-fit effects accurately.
  4. Post-processing and analysis: After generating candidate ligand poses, post-processing techniques are applied to refine the predictions and analyze the intermolecular interactions. Visualization tools allow researchers to examine the docked complexes and identify key binding residues and conformational changes [32].

Applications of flexible docking

Flexible docking has diverse applications in drug discovery, structure-based drug design, and understanding molecular recognition processes:

  1. Drug discovery: Flexible docking is widely used in virtual screening and lead optimization to predict the binding affinity and selectivity of small molecule ligands to target proteins. By considering receptor flexibility, flexible docking can identify ligands that may not be captured by rigid docking methods, leading to more accurate predictions of binding affinity and efficacy.
  2. Protein-ligand binding mechanisms: Flexible docking can provide insights into the molecular mechanisms of protein-ligand interactions, including the induced-fit effects that occur upon ligand binding. By modeling conformational changes in the receptor, flexible docking helps elucidate the structural basis of ligand recognition and binding specificity.
  3. Enzyme inhibition: In the field of enzymology, flexible docking is used to predict the binding modes and mechanisms of enzyme inhibitors. By considering both ligand and receptor flexibility, flexible docking enables the design of potent and selective enzyme inhibitors with therapeutic potential [44].
  4. Virtual screening: Flexible docking is an essential component of virtual screening workflows, where large databases of chemical compounds are screened computationally to identify potential drug candidates. By accounting for receptor flexibility, flexible docking improves the accuracy of virtual screening predictions and enhances hit identification and lead optimization efforts.

Limitations of flexible docking

Despite its advantages, flexible docking has several limitations:

  1. Computational cost: Flexible docking simulations can be computationally expensive, especially when modeling large biomolecular systems or performing extensive conformational sampling. High computational costs limit the scalability of flexible docking approaches and require efficient algorithms and parallel computing resources [45].
  2. Scoring function accuracy: The accuracy of flexible docking predictions relies heavily on the scoring functions used to evaluate ligand-receptor interactions and rank binding poses. Current scoring functions may not accurately capture all relevant molecular interactions, leading to inaccuracies in the predicted binding affinity and pose [46].
  3. Conformational sampling: Conformational sampling is a critical aspect of flexible docking, as it determines the diversity of ligand and receptor conformations explored during the docking process. However, exhaustive conformational sampling can be challenging, particularly for large biomolecular systems with complex energy landscapes.
  4. Modeling receptor flexibility: Modeling receptor flexibility accurately remains a significant challenge in flexible docking. While some methods allow for explicit modeling of receptor flexibility, such as induced-fit docking, others rely on simplified representations of receptor flexibility or predefined conformational ensembles. In conclusion, flexible docking is a valuable computational technique for predicting the binding mode and affinity between ligands and receptors while accounting for flexibility in both structures. By considering conformational changes in the ligand and receptor, flexible docking provides more accurate predictions of binding affinity and enables the exploration of induced-fit effects in molecular recognition processes. Despite its limitations, flexible docking has diverse applications in drug discovery, structure-based drug design, and understanding biomolecular interactions, contributing to the development of novel therapeutics and molecular design strategies. Continued advancements in docking algorithms, scoring functions, and computational resources promise to further enhance the accuracy and efficiency of flexible docking simulations in the future [46].

 

Induced fit docking

Induced fit docking combines aspects of both rigid and flexible docking. It involves initial docking with rigid structures followed by refinement of the complex with flexible side chains or backbone movements to account for induced fit effects. It is a computational technique used in molecular modeling to predict the binding mode and affinity between a ligand and a receptor while explicitly considering conformational changes in both the ligand and receptor structures upon binding. Unlike rigid docking, which assumes that both the ligand and receptor maintain rigid conformations during binding, induced fit docking accounts for the dynamic nature of biomolecular interactions and the induced-fit phenomenon observed in ligand-receptor binding [47].

Principles of induced fit docking

The principle behind induced fit docking is to capture the dynamic nature of biomolecular interactions, where both the ligand and receptor may undergo conformational changes to optimize their interactions and achieve a stable bound complex. In induced fit docking, the binding process is considered as a two-step mechanism:

  1. Pre-docking conformational sampling: Initially, the ligand and receptor structures are subjected to conformational sampling independently to explore their respective conformational spaces. This involves generating multiple conformations of the ligand and receptor using techniques such as molecular dynamics simulations, normal mode analysis, or systematic conformational search algorithms.
  2. Docking with flexible receptor: In the docking step, the ligand is docked into the binding site of the receptor, allowing both the ligand and receptor to adjust their conformations simultaneously. The receptor structure is treated as flexible during the docking process, allowing it to undergo conformational changes to accommodate the bound ligand. This flexibility enables the receptor to adopt conformations that optimize interactions with the ligand, leading to a stable bound complex.

Methods of induced-fit docking

Induced-fit docking methods typically involve the following steps:

  1. Conformational sampling: Conformational sampling of both the ligand and receptor structures is performed to generate an ensemble of conformations representing their respective flexibility. This may involve techniques such as molecular dynamics simulations, normal mode analysis, or systematic conformational search algorithms [1].
  2. Docking algorithm: Induced fit docking algorithms combine conformational sampling with traditional docking search strategies to predict the optimal binding pose of the ligand within the flexible binding site of the receptor. These algorithms often use scoring functions to evaluate the compatibility of each ligand conformation with the ensemble of receptor conformations and guide the search toward the most favorable binding poses.
  3. Scoring function: Scoring functions in induced fit docking assess the energetics of ligand-receptor interactions and rank the predicted binding poses based on factors such as geometric complementarity, electrostatic interactions, van der Waals forces, solvation effects, and conformational strain. These scoring functions are essential for selecting the most stable and biologically relevant binding poses from the ensemble of docked complexes.
  4. Post-processing and analysis: After generating candidate ligand poses, post-processing techniques are applied to refine the predictions and analyze the intermolecular interactions. Visualization tools allow researchers to examine the docked complexes and identify key binding residues, conformational changes, and induced-fit effects.

Applications of induced fit docking

Induced fit docking has diverse applications in drug discovery, structure-based drug design, and understanding molecular recognition processes:

  1. Lead optimization: Induced fit docking is used in lead optimization to predict the binding affinity and selectivity of small molecule ligands to target proteins. By explicitly considering receptor flexibility, induced fit docking enables the identification of ligands that may not be captured by rigid docking methods, leading to more accurate predictions of binding affinity and efficacy.
  2. Fragment-based drug design: In fragment-based drug design, induced fit docking is employed to screen fragment libraries and predict the binding modes of small molecular fragments to target proteins. By considering receptor flexibility, induced fit docking facilitates the identification of fragment hits that can be optimized into high-affinity lead compounds.
  3. Virtual screening: Induced fit docking is an essential component of virtual screening workflows, where large databases of chemical compounds are screened computationally to identify potential drug candidates. By accounting for receptor flexibility, induced fit docking improves the accuracy of virtual screening predictions and enhances hit identification and lead optimization efforts [48].
  4. Understanding binding mechanisms: Induced fit docking provides insights into the molecular mechanisms of ligand-receptor interactions, including the induced-fit effects that occur upon ligand binding. By modeling conformational changes in the receptor, induced fit docking helps elucidate the structural basis of ligand recognition and binding specificity.

Limitations of induced fit docking

Despite its advantages, induced fit docking has several limitations:

  1. Computational cost: Induced fit docking simulations can be computationally expensive, especially when modeling large biomolecular systems or performing extensive conformational sampling. High computational costs limit the scalability of induced fit docking approaches and require efficient algorithms and parallel computing resources.
  2. Scoring function accuracy: The accuracy of induced fit docking predictions relies heavily on the scoring functions used to evaluate ligand-receptor interactions and rank binding poses. Current scoring functions may not accurately capture all relevant molecular interactions, leading to inaccuracies in the predicted binding affinity and pose.
  3. Conformational sampling: Conformational sampling is a critical aspect of induced fit docking, as it determines the diversity of ligand and receptor conformations explored during the docking process. However, exhaustive conformational sampling can be challenging, particularly for large biomolecular systems with complex energy landscapes.
  4. Modeling receptor flexibility: Modeling receptor flexibility accurately remains a significant challenge in induced fit docking. While some methods allow for explicit modeling of receptor flexibility, others rely on simplified representations of receptor flexibility or predefined conformational ensembles. In conclusion, induced fit docking is a valuable computational technique for predicting the binding mode and affinity between ligands and receptors while explicitly considering conformational changes in both structures [49]. By capturing the induced-fit effects that occur upon ligand binding, induced-fit docking provides more accurate predictions of binding affinity and enables the exploration of molecular recognition mechanisms. Despite its limitations, induced fit docking has diverse applications in drug discovery, structure-based drug design, and understanding biomolecular interactions, contributing to the development of novel therapeutics and molecular design strategies. Continued advancements in docking algorithms, scoring functions, and computational resources promise to further enhance the accuracy and efficiency of induced fit docking simulations in the future.

 

Ligand-based docking

In ligand-based docking, the docking process is guided by the properties of the ligand molecule rather than the receptor structure. This method is useful when the receptor structure is unknown or difficult to obtain. Ligand-based docking, also known as ligand-centric docking or structure-based pharmacophore modeling, is a computational technique used in molecular modeling to predict the binding mode and affinity of a ligand to a target receptor or enzyme without explicit consideration of the receptor structure. Unlike receptor-based docking methods, which require knowledge of the receptor structure, ligand-based docking relies solely on information derived from the ligand itself, such as its structure, chemical properties, and interactions with the target [50].

Principles of ligand-based docking

The principle behind ligand-based docking is to infer the binding mode and affinity of a ligand to a target receptor based on its similarity to known ligands with experimentally determined activities. Ligand-based docking methods exploit the concept of molecular similarity and pharmacophore mapping to identify key structural features and functional groups that are essential for ligand binding and biological activity. By comparing the molecular properties and spatial arrangement of ligands, ligand-based docking can predict the binding affinity and selectivity of new ligands to the target receptor.

Methods of ligand-based docking

This method typically involves the following steps:

  1. Ligand selection and dataset preparation: Ligand-based docking begins with the selection of a dataset comprising structurally diverse ligands with known activities against the target receptor. These ligands serve as reference compounds for generating pharmacophore models and similarity searches [51]. The dataset is typically curated from experimental databases or virtual screening libraries.
  2. Pharmacophore generation: Pharmacophore models are generated based on the spatial arrangement of key functional groups and molecular features that contribute to ligand binding and biological activity. Common pharmacophore features include hydrogen bond donors, acceptors, hydrophobic regions, and aromatic rings. Pharmacophore generation algorithms identify common features shared among active ligands and define their spatial relationships.
  3. Similarity search and virtual screening: Once the pharmacophore model is generated, it is used to search virtual compound libraries for molecules with similar chemical features and spatial arrangements. Ligand-based similarity search methods, such as 2D fingerprints, 3D pharmacophore overlays, or shape-based similarity, are employed to identify candidate ligands that match the pharmacophore model. Virtual screening ranks candidate ligands based on their similarity to known active compounds and predicts their binding affinity to the target receptor.
  4. Scoring and validation: Candidate ligands identified through virtual screening are further evaluated using scoring functions to estimate their binding affinity and selectivity. Scoring functions consider factors such as pharmacophore fit, molecular properties, and energy calculations to prioritize ligands for experimental testing. Validation of ligand-based docking models is essential to assess their predictive accuracy and reliability using benchmark datasets and experimental assays.

Applications of ligand-based docking

 Ligand-based docking has diverse applications in drug discovery, virtual screening, and lead optimization:

  1. Lead identification: Ligand-based docking is used to identify lead compounds with potential activity against a target receptor or enzyme. By searching virtual compound libraries for molecules with similar chemical features and biological activities to known ligands, ligand-based docking facilitates the discovery of novel lead compounds for further experimental testing.
  2. Virtual screening: Ligand-based docking is an integral part of virtual screening workflows, where large databases of chemical compounds are screened computationally to prioritize molecules for experimental testing. By exploiting molecular similarity and pharmacophore mapping, ligand-based docking enables the rapid identification of potential drug candidates with desired pharmacological profiles [52].
  3. Lead optimization: Ligand-based docking is used in lead optimization to guide the design of new compounds with improved binding affinity and selectivity. By analyzing the structure-activity relationships of known ligands and identifying key pharmacophore features, ligand-based docking helps optimize chemical scaffolds and functional groups to enhance ligand potency and drug-like properties [53].
  4. Bioisosteric replacement: Ligand-based docking facilitates bioisosteric replacement by identifying structurally similar compounds that can substitute key functional groups or chemical moieties in known ligands. By exploring chemical space and identifying analogs with similar pharmacological profiles, ligand-based docking aids in the design of novel ligands with improved efficacy and reduced off-target effects [54].

Limitations of ligand-based docking

Despite its advantages, ligand-based docking has several limitations:

  1. Dependency on reference ligands: Ligand-based docking relies on the availability of reference ligands with known activities against the target receptor. If suitable reference ligands are not available or if the dataset is biased or incomplete, ligand-based docking may produce inaccurate predictions [55].
  2. Limited structural information: Ligand-based docking does not require knowledge of the target receptor structure, making it suitable for targets with unknown or difficult-to-obtain structures. However, the lack of structural information may limit the accuracy and interpretability of ligand-based docking predictions, particularly for targets with complex binding sites or allosteric modulation [56].
  3. Scoring function performance: The accuracy of ligand-based docking predictions depends on the performance of scoring functions used to rank candidate ligands. Current scoring functions may not fully capture the complexity of ligand-receptor interactions, leading to false positives or false negatives in virtual screening results [57].
  4. Target flexibility: Ligand-based docking assumes that the target receptor adopts a rigid conformation during ligand binding, neglecting conformational changes or induced-fit effects that may occur upon ligand binding. This limitation may affect the accuracy of predictions, particularly for targets with flexible or dynamic binding sites. In conclusion, ligand-based docking is a valuable computational technique for predicting the binding mode and affinity of ligands to target receptors based on molecular similarity and pharmacophore mapping [58]. By exploiting the concept of molecular similarity and pharmacophore mapping, ligand-based docking enables the rapid identification of lead compounds, virtual screening of chemical libraries, and optimization of ligand potency and selectivity. Despite its limitations, ligand-based docking has diverse applications in drug discovery and lead optimization, contributing to developing novel therapeutics and molecular design strategies. Continued advancements in docking algorithms, scoring functions, and virtual screening methodologies promise to further enhance the accuracy and efficiency of ligand-based docking in the future [59].

 

Protein-protein docking

Protein-protein docking predicts the interactions between two protein molecules. It is essential for understanding protein complexes and signaling pathways. Protein-protein docking is a computational technique used in molecular modeling to predict the three-dimensional structure and interaction mode between two or more protein molecules [60]. It plays a crucial role in understanding the mechanisms of protein-protein interactions (PPIs), which are fundamental to various biological processes such as signal transduction, immune response, and gene regulation. In this essay, we will explore the principles, methods, applications, and limitations of protein-protein docking in molecular modeling [61].

 

Principles of protein-protein docking

The principle behind protein-protein docking is to predict the binding mode and affinity between two protein molecules by exploring their conformational space and identifying energetically favorable binding configurations. Protein-protein interactions are mediated by complementary surfaces on the protein molecules, where specific amino acid residues form intermolecular contacts such as hydrogen bonds, hydrophobic interactions, and electrostatic interactions. Protein-protein docking aims to model these interactions and predict the spatial arrangement of the interacting proteins in the bound complex [62].

Methods of protein-protein docking

Protein-protein docking methods typically involve the following steps:

  1. Preparation of protein structures: The first step in protein-protein docking is the preparation of protein structures, including the removal of water molecules, the addition of missing atoms, the assignment of partial charges, and the optimization of hydrogen bond networks [63]. The protein structures are often obtained from experimental techniques such as X-ray crystallography or NMR spectroscopy.
  2. Search algorithm: Protein-protein docking algorithms employ various search strategies to explore the conformational space of the interacting proteins and identify potential binding poses. Common search algorithms include geometric hashing, Monte Carlo methods, genetic algorithms, and stochastic optimization techniques [64]. These algorithms sample different orientations and conformations of the proteins and evaluate their compatibility based on geometric complementarity and intermolecular interactions [65].
  3. Scoring function: After generating candidate binding poses, a scoring function is used to evaluate and rank these poses based on their predicted binding affinity and stability. Scoring functions typically consider factors such as shape complementarity, electrostatic interactions, van der Waals forces, desolvation energy, and interface area. The scoring function aims to distinguish between native-like binding poses and non-specific interactions [66].
  4. Post-processing and analysis: Finally, post-processing techniques are applied to refine the predicted binding poses and analyze the intermolecular interactions. Visualization tools allow researchers to examine the docked complexes and identify key binding residues and interaction interfaces. Molecular dynamics simulations or energy minimization algorithms may be employed to further optimize the docked structures and explore their dynamic behavior [67].

Applications of protein-protein docking

Protein-protein docking has diverse applications in structural biology, drug discovery, and understanding disease mechanisms:

  1. Structural elucidation: Protein-protein docking is used to elucidate the three-dimensional structure of protein complexes and understand the molecular basis of protein-protein interactions. By predicting the binding mode and interface residues of interacting proteins, protein-protein docking provides insights into the mechanisms of complex formation and function [68].
  2. Drug discovery: Protein-protein docking plays a crucial role in structure-based drug design by identifying potential protein-protein interaction inhibitors or modulators. By targeting specific protein-protein interfaces involved in disease pathways, protein-protein docking enables the rational design of small molecules or peptides that disrupt pathological protein complexes and inhibit disease progression [69].
  3. Functional annotation: Protein-protein docking helps annotate the function of proteins by predicting their interaction partners and binding modes. By analyzing the protein-protein interaction network, protein-protein docking facilitates the identification of protein complexes involved in cellular processes and signaling pathways, providing valuable insights into protein function and regulation [70].
  4. Virtual screening: Protein-protein docking is an essential component of virtual screening workflows, where libraries of small molecules or peptides are screened computationally to identify potential protein-protein interaction inhibitors. By docking small molecules or peptides into the binding sites of target proteins, protein-protein docking enables the prioritization of candidate compounds for experimental testing based on their predicted binding affinity and interaction specificity [71].

Limitations of protein-protein docking

Despite its advantages, protein-protein docking has several limitations:

  1. Conformational sampling: Protein-protein docking involves searching through a vast conformational space to identify the optimal binding pose of interacting proteins. However, exhaustive conformational sampling can be computationally intensive, particularly for large protein complexes with flexible or dynamic regions [72]. Sampling limitations may fail to explore all relevant binding poses and identify the native-like complex structure.
  2. Scoring function accuracy: The accuracy of protein-protein docking predictions depends heavily on the scoring function used to evaluate binding poses. However, current scoring functions may not fully capture the complexity of protein-protein interactions, leading to inaccuracies in binding affinity predictions and false positives or false negatives in complex structure identification [73].
  3. Treatment of protein flexibility: Protein-protein docking often assumes that both interacting proteins maintain rigid conformations during binding, neglecting conformational changes or induced-fit effects that may occur upon complex formation. The treatment of protein flexibility remains a significant challenge in protein-protein docking, particularly for large protein complexes with flexible binding sites or allosteric regulation.
  4. Experimental validation: While protein-protein docking provides valuable insights into the structure and interaction modes of protein complexes, experimental validation is essential to confirm predicted binding poses and characterize the biological relevance of predicted interactions. Experimental techniques such as X-ray crystallography, NMR spectroscopy, or biochemical assays are needed to validate protein-protein docking predictions and elucidate the functional significance of protein-protein interactions. In conclusion, protein-protein docking is a powerful computational technique for predicting the three-dimensional structure and interaction mode between protein molecules [74]. By exploring the conformational space of interacting proteins and identifying energetically favorable binding poses, protein-protein docking provides valuable insights into the mechanisms of protein-protein interactions and enables the rational design of protein-protein interaction inhibitors for drug discovery and therapeutic intervention. Despite its limitations, protein-protein docking has diverse applications in structural biology, drug discovery, and functional annotation, contributing to our understanding of biological processes and disease mechanisms at the molecular level. Continued advancements in docking algorithms, scoring functions, and experimental validation techniques promise to further enhance the accuracy and utility of protein-protein docking in the future [75].

 

Blind docking

Blind docking involves docking a ligand to the entire surface of a receptor without specifying a binding site. This method is useful for exploring potential binding sites and interactions. Blind docking, also known as global docking or blind protein-ligand docking, is a computational technique used in molecular modeling to predict the binding mode and affinity between a ligand and a receptor without prior knowledge of the binding site on the receptor. Unlike traditional docking methods, which rely on information about the receptor structure to guide the docking process, blind docking explores the entire surface of the receptor to identify potential binding sites and predict the optimal binding poses of ligands.

 

Principles of blind docking

The principle behind blind docking is to search the entire surface of the receptor for potential binding sites and predict the binding mode and affinity of ligands to these sites. Blind docking does not require prior knowledge of the binding site location or structure, making it suitable for targets with unknown or flexible binding sites. Instead, blind docking algorithms sample a grid or mesh of points covering the entire receptor surface and evaluate potential binding poses at each point based on intermolecular interactions and scoring functions [76].

Methods of blind docking

These typically involve the following steps:

  1. Grid generation: The receptor structure is represented as a three-dimensional grid or mesh covering the entire surface of the protein. The grid spacing defines the resolution of the search space, with smaller spacing allowing for finer sampling but increasing computational cost. Grid generation may also involve masking or excluding regions of the receptor surface that are unlikely to accommodate ligand binding, such as solvent-exposed regions or areas with high conformational flexibility [77].
  2. Ligand sampling: A library of ligand conformations is generated or selected for docking into the receptor grid. Ligand sampling methods may include random sampling, systematic sampling, or fragment-based approaches [78]. The ligand conformations are typically generated by exploring the ligand's conformational space or by sampling from a database of known ligand structures.
  3. Docking algorithm: Blind docking algorithms search for potential binding poses of the ligand within the receptor grid using various search strategies, such as geometric hashing, Monte Carlo methods, genetic algorithms, or stochastic optimization techniques. The ligand conformations are systematically or randomly placed within the grid, and potential binding poses are evaluated based on geometric complementarity and intermolecular interactions with the receptor [79].
  4. Scoring function: After generating candidate binding poses, a scoring function is used to evaluate and rank these poses based on their predicted binding affinity and stability. The scoring function considers factors such as geometric complementarity, electrostatic interactions, van der Waals forces, desolvation energy, and interface area. The scoring function aims to distinguish between native-like binding poses and non-specific interactions [80].
  5. Post-processing and analysis: Finally, post-processing techniques are applied to refine the predicted binding poses and analyze the intermolecular interactions. Visualization tools allow researchers to examine the docked complexes and identify potential binding sites on the receptor surface [81]. Molecular dynamics simulations or energy minimization algorithms may be employed to further optimize the docked structures and explore their dynamic behavior.

Applications of blind docking

Blind docking has diverse applications in drug discovery, virtual screening, and understanding protein-ligand interactions:

  1. Target identification: Blind docking can be used to identify potential binding sites on a target protein and predict the binding mode and affinity of ligands to these sites. By exploring the entire surface of the receptor, blind docking enables the discovery of novel binding sites that may not be apparent from experimental structures or homology models [82].
  2. Virtual screening: Blind docking is an essential component of virtual screening workflows, where libraries of chemical compounds are screened computationally to identify potential drug candidates [83]. By exploring the entire receptor surface, blind docking enables the discovery of ligands with diverse chemical scaffolds and binding modes, leading to the identification of novel lead compounds for further experimental testing.
  3. Fragment-based drug design: Blind docking can be used in fragment-based drug design to screen libraries of small molecular fragments and predict their binding modes to the target protein. By exploring the entire receptor surface, blind docking facilitates the identification of fragment hits that can be optimized into high-affinity lead compounds through fragment linking or growing strategies [84].
  4. Protein function prediction: Blind docking can help predict the function of proteins by identifying potential ligand binding sites and characterizing the binding modes of ligands to these sites. By exploring the entire surface of the receptor, blind docking provides insights into the structural basis of protein-ligand interactions and aids in the annotation of protein function and regulation [85].

Limitations of blind docking

Despite its advantages, blind docking has several limitations:

  1. Computational cost: Blind docking simulations can be computationally intensive, especially when exploring the entire surface of the receptor and sampling a large number of ligand conformations. High computational costs limit the scalability of blind docking approaches and require efficient algorithms and parallel computing resources [86].
  2. Sampling limitations: Blind docking may suffer from sampling limitations, particularly in regions of the receptor surface with high conformational flexibility or solvent-exposed regions. Inaccessible binding sites or buried cavities may not be adequately sampled, leading to the failure to identify potential binding poses or binding sites [87].
  3. Scoring function accuracy: The accuracy of blind docking predictions depends heavily on the scoring function used to evaluate binding poses. However, current scoring functions may not fully capture the complexity of protein-ligand interactions, leading to inaccuracies in binding affinity predictions and false positives or false negatives in binding site identification [88].
  4. Treatment of receptor flexibility: Blind docking often assumes that the receptor maintains a rigid conformation during ligand binding, neglecting conformational changes or induced-fit effects that may occur upon ligand binding. The treatment of receptor flexibility remains a significant challenge in blind docking, particularly for targets with flexible or dynamic binding sites. In conclusion, blind docking is a powerful computational technique for predicting the binding mode and affinity between a ligand and a receptor without prior knowledge of the binding site location or structure [89]. By exploring the entire surface of the receptor, blind docking enables the discovery of novel binding sites and the prediction of binding modes for diverse chemical compounds. Despite its limitations, blind docking has diverse applications in drug discovery, virtual screening, and understanding protein-ligand interactions, contributing to the development of novel therapeutics and molecular design strategies. Continued advancements in docking algorithms, scoring functions, and computational resources promise to further enhance the accuracy and efficiency of blind docking simulations in the future.

 

CONCLUSION

 

Molecular docking has undergone significant evolution since its inception, transforming from a rudimentary tool to a sophisticated computational technique with widespread applications in drug discovery, structural biology, and molecular modeling. As we reflect on its past achievements and current state, we can also envision the future directions and potential advancements that will shape the field of molecular docking in the years to come.

Past achievements: In its early stages, molecular docking primarily focused on predicting the binding mode and affinity between small molecule ligands and target proteins. The development of scoring functions, geometric search algorithms, and empirical force fields laid the foundation for accurate docking predictions and enabled the virtual screening of compound libraries to identify potential drug candidates. These advancements revolutionized the drug discovery process, allowing researchers to screen large chemical databases computationally and prioritize compounds for experimental testing, thereby accelerating the drug development pipeline. Furthermore, molecular docking played a crucial role in elucidating the three-dimensional structures of protein-ligand complexes and understanding the molecular mechanisms of ligand binding. By providing insights into the interactions between small molecules and target proteins, docking studies have facilitated structure-based drug design efforts and rationalized the design of novel therapeutics with improved potency, selectivity, and pharmacokinetic properties.

 

Present state: In the present era, molecular docking has become an integral part of computational drug discovery workflows, complementing experimental techniques and guiding lead optimization efforts. The development of advanced docking algorithms, machine learning approaches, and high-performance computing resources has enabled more accurate and efficient docking simulations, allowing researchers to explore larger chemical spaces and tackle complex biological problems. Virtual screening, fragment-based drug design, and structure-based optimization are among the key applications of molecular docking in contemporary drug discovery projects. Virtual screening campaigns leverage docking simulations to identify lead compounds with potential activity against target proteins, while fragment-based approaches utilize docking to screen small molecular fragments and design high-affinity inhibitors through fragment linking or growing strategies. Additionally, molecular docking guides the optimization of lead compounds by predicting their binding modes, estimating binding affinities, and rationalizing structure-activity relationships. Moreover, molecular docking continues to contribute to our understanding of protein-ligand interactions and the structural basis of drug action. Docking studies elucidate the mechanisms of ligand recognition, protein dynamics, and allosteric regulation, providing valuable insights into the design of therapeutics targeting protein-protein interactions, enzyme inhibition, and receptor modulation.

Future directions: Looking ahead, the future of molecular docking holds great promise, driven by advancements in computational methodologies, data integration, and interdisciplinary collaborations. Several key areas are poised for significant development and innovation in the coming years. Integration of Machine Learning: Machine learning techniques, including deep learning and reinforcement learning, are increasingly being integrated into molecular docking workflows to enhance prediction accuracy, speed, and efficiency. By leveraging large datasets of protein-ligand complexes and chemical structures, machine learning algorithms can learn complex patterns and relationships in docking data, leading to more accurate scoring functions, better conformational sampling strategies, and improved virtual screening outcomes. Incorporation of Quantum Mechanics: Quantum mechanical approaches are being explored to improve the accuracy of docking predictions, particularly for systems involving metal ions, covalent inhibitors, or non-covalent interactions with high polarization effects. By incorporating quantum mechanical calculations into docking simulations, researchers can better capture the electronic structure and energetics of protein-ligand interactions, leading to more reliable binding affinity predictions and a deeper understanding of molecular recognition processes.

ACKNOWLEDGMENTS: The authors are grateful to the Department of Pharmaceutical Sciences, MATS University, Raipur, Chhattisgarh, India for providing an E-Library facility.

CONFLICT OF INTEREST: None

FINANCIAL SUPPORT: None

ETHICS STATEMENT: None

References
  1. Muhammed MT, Aki-Yalcin E. Molecular docking: Principles, advances, and its applications in drug discovery. Lett Drug Des Discov. 2024;21(3):480-95.
  2. Asiamah I, Obiri SA, Tamekloe W, Armah FA, Borquaye LS. Applications of molecular docking in natural products-based drug discovery. Sci Afr. 2023;20:e01593.
  3. Vidal-Limon A, Aguilar-Toalá JE, Liceaga AM. Integration of molecular docking analysis and molecular dynamics simulations for studying food proteins and bioactive peptides. J Agric Food Chem. 2022;70(4):934-43.
  4. Singh S, Baker QB, Singh DB. Molecular docking and molecular dynamics simulation. InBioinformatics 2022 Jan 1 (pp. 291-304). Academic Press.
  5. Agu PC, Afiukwa CA, Orji OU, Ezeh EM, Ofoke IH, Ogbu CO, et al. Molecular docking as a tool for the discovery of molecular targets of nutraceuticals in disease management. Sci Rep. 2023;13(1):13398.
  6. Yu Y, Lu S, Gao Z, Zheng H, Ke G. Do deep learning models really outperform traditional approaches in molecular docking? arXiv preprint arXiv:2302.07134. 2023.
  7. Li T, Guo R, Zong Q, Ling G. Application of molecular docking in elaborating molecular mechanisms and interactions of supramolecular cyclodextrin. Carbohydr Polym. 2022;276(2):118644.
  8. Crampon K, Giorkallos A, Deldossi M, Baud S, Steffenel LA. Machine-learning methods for ligand-protein molecular docking. Drug Discov Today. 2022;27(1):151-64.
  9. Zhang B, Li H, Yu K, Jin Z. Molecular docking-based computational platform for high-throughput virtual screening. CCF Trans High Perform Comput. 2022;4(1):63-74.
  10. Dong Y, Li M, Hao Y, Feng Y, Ren Y, Ma H. Antifungal activity, structure‐activity relationship and molecular docking studies of 1, 2, 4‐triazole Schiff base derivatives. Chem Biodiversity. 2023;20(3):e202201107.
  11. Han L, Zhao D, Li Y, Jin J, El-Kott AF, Al-Saeed FA, et al. Assessment of the anti-breast cancer effects of urolithin with molecular docking studies in the in vitro condition: Introducing a novel chemotherapeutic drug. Mol Biotechnol. 2024;66(3):554-66. 
  12. Karimi-Maleh H, Liu Y, Li Z, Darabi R, Orooji Y, Karaman C, et al. Calf thymus ds-DNA intercalation with pendimethalin herbicide at the surface of ZIF-8/Co/rGO/C3N4/ds-DNA/SPCE; A bio-sensing approach for pendimethalin quantification confirmed by molecular docking study. Chemosphere. 2023;332:138815.
  13. Kumer A, Chakma U, Chandro A, Howlader D, Akash S, Kobir ME, et al. Modified D-glucofuranose computationally screening for inhibitor of breast cancer and triple breast cancer: Chemical descriptor, molecular docking, molecular dynamics, and QSAR. J Chil Chem Soc. 2022;67(3):5623-35.
  14. Mekheimer RA, Abuo-Rahma GE, Abd-Elmonem M, Yahia R, Hisham M, Hayallah AM, et al. New s-triazine/tetrazole conjugates as potent antifungal and antibacterial agents: Design, molecular docking, and mechanistic study. J Mol Struct. 2022;1267:133615.
  15. Makarian M, Gonzalez M, Salvador SM, Lorzadeh S, Hudson PK, Pecic S. Synthesis, kinetic evaluation and molecular docking studies of donepezil-based acetylcholinesterase inhibitors. J Mol Struct. 2022;1247(2):131425.
  16. Yu X, Qin W, Cai H, Ren C, Huang S, Lin X, et al. Analyzing the molecular mechanism of xuefuzhuyu decoction in the treatment of pulmonary hypertension with network pharmacology and bioinformatics and verifying molecular docking. Comput Biol Med. 2024;169(14):107863.
  17. Wang H, Lin Y, Liu Q, Zhou A, Bian H, Zhang W, et al. Antioxidant, anticancer activity, and molecular docking study of lycopene with different ratios of Z-isomers. Curr Res Food Sci. 2023;6:100455.
  18. Asogwa FC, Agwamba EC, Louis H, Muozie MC, Benjamin I, Gber TE, et al. Structural benchmarking, density functional theory simulation, spectroscopic investigation, and molecular docking of N-(1H-pyrrol-2-yl) methylene)-4-methylaniline as castration-resistant prostate cancer chemotherapeutic agent. Chem Phys Impact. 2022;5(11):100091.
  19. Yang W, Feng Q, Peng Z, Wang G. An overview on the synthetic urease inhibitors with structure-activity relationship and molecular docking. Eur J Med Chem. 2022;234:114273. 
  20. Semenov VA, Krivdin LB. Combined computational NMR and molecular docking scrutiny of potential natural SARS-CoV-2 Mpro inhibitors. J Phys Chem B. 2022;126(11):2173-87.
  21. Wu Z, Xu J, Ruan J, Chen J, Li X, Yu Y, et al. Probing the mechanism of interaction between capsaicin and myofibrillar proteins through multispectral, molecular docking, and molecular dynamics simulation methods. Food Chem X. 2023;18:100734.
  22. Shi J, Zhao LX, Wang JY, Ye T, Wang M, Gao S, et al. The novel 4-hydroxyphenylpyruvate dioxygenase inhibitors in vivo and in silico approach: 3D-QSAR analysis, molecular docking, bioassay, and molecular dynamics. Arab J Chem. 2022;15(7):103919.
  23. Chigurupati S, Al-Murikhy A, Almahmoud SA, Almoshari Y, Ahmed AS, Vijayabalan S, et al. Molecular docking of phenolic compounds and screening of antioxidant and antidiabetic potential of Moringa oleifera ethanolic leaves extract from Qassim region, Saudi Arabia. Saudi J Biol Sci. 2022;29(2):854-9.
  24. Rizk HF, El-Borai MA, Ragab A, Ibrahim SA, Sadek ME. A novel of azo-thiazole moiety alternative for benzidine-based pigments: Design, synthesis, characterization, biological evaluation, and molecular docking study. Polycyclic Aromat Comp. 2023;43(1):500-22.
  25. Zhao W, Su L, Huo S, Yu Z, Li J, Liu J. Virtual screening, molecular docking and identification of umami peptides derived from Oncorhynchus mykiss. Food Sci Hum Wellness. 2023;12(1):89-93.
  26. Zhang J, Zhang J, Liang L, Sun B, Zhang Y. Identification and virtual screening of novel umami peptides from chicken soup by molecular docking. Food Chem. 2023;404(Pt A):134414.
  27. Wani TA, Zargar S, Hussain A. Spectroscopic, thermodynamic and molecular docking studies on molecular mechanisms of drug binding to proteins. Molecules. 2022;27(23):8405.
  28. Huang ZY, Li XY, Hu LY, Bai AM, Hu YJ. Comparative study of two antipsychotic drugs binding to human serum albumin: By multispectroscopic and molecular docking methods. J Mol Liq. 2022;365(5):120084.
  29. Omar R, Abd El-Salam M, Elsbaey M, Hassan M. Fourteen immunomodulatory alkaloids and two prenylated phenylpropanoids with a dual therapeutic approach for COVID-19: Molecular docking and dynamics studies. J Biomol Struct Dyn. 2024;42(5):2298-315.
  30. Makhlouf J, Louis H, Benjamin I, Ukwenya E, Valkonen A, Smirani W. Single crystal investigations, spectral analysis, DFT studies, antioxidants, and molecular docking investigations of novel hexaisothiocyanato chromate complex. J Mol Struct. 2023;1272(20):134223.
  31. Khattab ES, Ragab A, Abol-Ftouh MA, Elhenawy AA. Therapeutic strategies for COVID-19 based on molecular docking and dynamic studies to the ACE-2 receptors, Furin, and viral spike proteins. J Biomol Struct Dyn. 2022;40(23):13291-309.
  32. Lin R, Zhang J, Xu R, Yuan C, Guo L, Liu P, et al. Developments in molecular docking technologies for application of polysaccharide-based materials: A review. Crit Rev Food Sci Nutr. 2023:1-13. 
  33. Liang L, Zhou C, Zhang J, Huang Y, Zhao J, Sun B, et al. Characteristics of umami peptides identified from porcine bone soup and molecular docking to the taste receptor T1R1/T1R3. Food Chem. 2022;387:132870.
  34. Zaki AA, Ashour A, Elhady SS, Darwish KM, Al-Karmalawy AA. Calendulaglycoside a showing potential activity against SARS-CoV-2 main protease: Molecular docking, molecular dynamics, and SAR studies. J Tradit Complement Med. 2022;12(1):16-34.
  35. Raval K, Ganatra T. Basics, types and applications of molecular docking: A review. IP Int J Compr Adv Pharmacol. 2022;7(1):12-6.
  36. Azad I. Molecular docking in the study of ligand-protein recognition: An overview. Molecular Docking-Recent Advances. 2023.
  37. Luo H, Zhang K, Su Y, Zhong K, Li Z, Guo J, et al. Monocular vision pose determination-based large rigid-body docking method. Measurement. 2022;204(4):112049.
  38. Mohanty M, Mohanty PS. Molecular docking in organic, inorganic, and hybrid systems: A tutorial review. Monatsh Chem. 2023;154(7):683-707.
  39. Wang R, Sun Y, Luo Y, Li S, Yang C, Cheng X, et al. Injecting multimodal information into rigid protein docking via bi-level optimization. Adv Neural Inf Process Syst. 2024;36.
  40. Jin W, Barzilay R, Jaakkola T. Antibody-antigen docking and design via hierarchical structure refinement. International Conference on Machine Learning 2022 Jun 28 (pp. 10217-10227). PMLR.
  41. Kwon S, Seok C. CSAlign and CSAlign-Dock: Structure alignment of ligands considering full flexibility and application to protein-ligand docking. Comput Struct Biotechnol J. 2023;21(7873):1-0.
  42. Jin Z, Wei Z. Molecular simulation for food protein-ligand interactions: A comprehensive review on principles, current applications, and emerging trends. Compr Rev Food Sci Food Saf. 2024;23(1):1-29.
  43. Mijit A, Wang X, Li Y, Xu H, Chen Y, Xue W. Mapping synthetic binding proteins epitopes on diverse protein targets by protein structure prediction and protein-protein docking. Comput Biol Med. 2023;163:107183.
  44. Wu Y, Brooks III CL. Covalent docking in CDOCKER. J Comput Aided Mol Des. 2022;36(8):563-74.
  45. Tiwari S, Prakash K, Soni D. Rudimentary review on molecular docking: A beginner’s guide. IJPPR. 2023;14(4):73-83.
  46. Ketata MA, Laue C, Mammadov R, Stärk H, Wu M, Corso G, et al. Diffdock-pp: Rigid protein-protein docking with diffusion models. arXiv preprint arXiv:2304.03889. 2023.
  47. Yu Y, Xu S, He R, Liang G. Application of molecular simulation methods in food science: Status and prospects. J Agric Food Chem. 2023;71(6):2684-703.
  48. Adelusi TI, Oyedele AQ, Boyenle ID, Ogunlana AT, Adeyemi RO, Ukachi CD, et al. Molecular modeling in drug discovery. Inform Med Unlocked. 2022;29:100880.
  49. Vázquez J, López M, Gibert E, Herrero E, Luque FJ. Merging ligand-based and structure-based methods in drug discovery: An overview of combined virtual screening approaches. Molecules. 2020;25(20):4723.
  50. Yang Y, Zhu Z, Wang X, Zhang X, Mu K, Shi Y, et al. Ligand-based approach for predicting drug targets and for virtual screening against COVID-19. Brief Bioinform. 2021;22(2):1053-64.
  51. Umar AB, Uzairu A, Shallangwa GA, Uba S. Ligand-based drug design and molecular docking simulation studies of some novel anticancer compounds on MALME-3M melanoma cell line. Egypt J Med Hum Genet. 2021;22(1):1-5.
  52. Boyles F, Deane CM, Morris GM. Learning from the ligand: Using ligand-based features to improve binding affinity prediction. Bioinformatics. 2020;36(3):758-64.
  53. Cleves AE, Jain AN. Structure-and ligand-based virtual screening on DUD-E+: Performance dependence on approximations to the binding pocket. J Chem Inf Model. 2020;60(9):4296-310.
  54. Thomas M, Smith RT, O’Boyle NM, de Graaf C, Bender A. Comparison of structure-and ligand-based scoring functions for deep generative models: A GPCR case study. J Cheminf. 2021;13(1):39.
  55. Yang SQ, Ye Q, Ding JJ, Ming‐Zhu Yin, Lu AP, Chen X, et al. Current advances in ligand‐based target prediction. Wiley Interdiscip Rev Comput Mol Sci. 2021;11(3):e1504.
  56. Gautam V, Gaurav A, Masand N, Lee VS, Patil VM. Artificial intelligence and machine-learning approaches in structure and ligand-based discovery of drugs affecting central nervous system. Mol Divers. 2023;27(2):959-85.
  57. Azevedo L, Serafim MS, Maltarollo VG, Grabrucker AM, Granato D. Atherosclerosis fate in the era of tailored functional foods: Evidence-based guidelines elicited from structure-and ligand-based approaches. Trends Food Sci Technol. 2022;128(S13):75-89.
  58. Sharma V, Wakode S, Kumar H. Structure-and ligand-based drug design: Concepts, approaches, and challenges. Chemoinformatics and bioinformatics in the pharmaceutical sciences. 2021:27-53.
  59. Agnihotry S, Pathak RK, Srivastav A, Shukla PK, Gautam B. Molecular docking and structure-based drug design. Comput Aided Drug Des. 2020:115-31.
  60. Yusof ENM, Azam M, Sirat SS, Ravoof TBSA, Page AJ, Veerakumarasivam A, et al. Dithiocarbazate ligand-based Cu(II), Ni(II), and Zn(II) complexes: Synthesis, structural investigations, cytotoxicity, DNA binding, and molecular docking studies. Bioinorg Chem Appl. 2022;2022:2004052. 
  61. Vyas VK, Bhati S, Patel S, Ghate M. Structure-and ligand-based drug design methods for the modeling of antimalarial agents: A review of updates from 2012 onwards. J Biomol Struct Dyn. 2022;40(20):10481-506.
  62. Ajjarapu SM, Tiwari A, Ramteke PW, Singh DB, Kumar S. Ligand-based drug designing. InBioinformatics 2022 Jan 1 (pp. 233-252). Academic Press.
  63. Gupta N, Regar H, Verma VK, Prusty D, Mishra A, Prajapati VK. Receptor-ligand-based molecular interaction to discover adjuvant for immune cell TLRs to develop next-generation vaccine. Int J Biol Macromol. 2020;152(5):535-45. 
  64. Di Carluccio C, Forgione MC, Martini S, Berti F, Molinaro A, Marchetti R, et al. Investigation of protein-ligand complexes by ligand-based NMR methods. Carbohydr Res. 2021;503:108313.
  65. Sunny S, Jayaraj PB. Protein-protein docking: Past, present, and future. Protein J. 2022;41(1):1-26. 
  66. Alekseenko A, Ignatov M, Jones G, Sabitova M, Kozakov D. Protein-protein and protein-peptide docking with cluspro server. Methods Mol Biol. 2020;2165:157-74. 
  67. Ren Y, Long S, Cao S. Molecular docking and virtual screening of an influenza virus inhibitor that disrupts protein-protein interactions. Viruses. 2021;13(11):2229.
  68. Jandova Z, Vargiu AV, Bonvin AMJJ. Native or non-native protein-protein docking models? Molecular dynamics to the rescue. J Chem Theory Comput. 2021;17(9):5944-54.
  69. Krupa P, Karczyńska AS, Mozolewska MA, Liwo A, Czaplewski C. UNRES-Dockprotein-protein and peptide-protein docking by coarse-grained replica-exchange MD simulations. Bioinformatics. 2021;37(11):1613-5.
  70. Lensink MF, Nadzirin N, Velankar S, Wodak SJ. Modeling protein-protein, protein-peptide, and protein-oligosaccharide complexes: CAPRI 7th edition. Proteins. 2020;88(8):916-38. 
  71. Zacharias M. Computational protein-protein docking. Protein interactions: The molecular basis of interactomics. 2022:53-76.
  72. Soofi A, Taghizadeh M, Tabatabaei SM, Rezaei Tavirani M, Shakib H, Namaki S, et al. Centrality analysis of protein-protein interaction networks and molecular docking prioritize potential drug targets in type 1 diabetes. Iran J Pharm Res. 2020;19(4):121-34.
  73. Slater O, Miller B, Kontoyianni M. Decoding protein-protein interactions: An overview. Curr Top Med Chem. 2020;20(10):855-82. 
  74. Chen SY, Zacharias M. What makes a good protein-protein interaction stabilizer: Analysis and application of the dual-binding mechanism. ACS Cent Sci. 2023;9(5):969-79.
  75. Verma N, Srivastava S, Malik R, Yadav JK, Goyal P, Pandey J. Computational investigation for modeling the protein-protein interaction of TasA(28-261)-TapA(33-253): A decisive process in biofilm formation by Bacillus subtilis. J Mol Model. 2020;26(9):226. 
  76. Andreani J, Quignot C, Guerois R. Structural prediction of protein interactions and docking using conservation and coevolution. Wiley Interdiscip Rev Comput Mol Sci. 2020;10(6):e1470.
  77. Xiong G, Shen C, Yang Z, Jiang D, Liu S, Lu A, et al. Featurization strategies for protein-ligand interactions and their applications in scoring function development. Wiley Interdiscip Rev Comput Mol Sci. 2022;12(2):e1567.
  78. Shree P, Srivastava S, Chaube R, Tripathi YB. Protein-Protein docking interaction of Nephrin protein with the expression of MMP-9: An anti-inflammatory agent. Res J Biotechnol. 2022;17(6):67-72.
  79. Gheyouche E, Bagueneau M, Loirand G, Hoffmann B, Téletchéa S. Structural design and analysis of the RHOA-ARHGEF1 binding mode: Challenges and applications for protein-protein interface prediction. Front Mol Biosci. 2021;8:643728. 
  80. Zhang W, Bell EW, Yin M, Zhang Y. EDock: Blind protein-ligand docking by replica-exchange Monte Carlo simulation. J Cheminform. 2020;12(1):37.
  81. Perez-Sanchez H, Kumar DT, Doss CG, Rodríguez-Schmidt R, Cerón-Carrasco JP, Peña-García J, et al. Prediction and characterization of influenza virus polymerase inhibitors through blind docking and ligand-bassed virtual screening. J Mol Liq. 2021;321:114784.
  82. Shivanika C, Kumar D, Ragunathan V, Tiwari P, Sumitha A. Molecular docking, validation, dynamics simulations, and pharmacokinetic prediction of natural compounds against the SARS-CoV-2 main-protease. J Biomol Struct Dyn. 2020:40(2):585-611.
  83. Che X, Chai S, Zhang Z, Zhang L. Prediction of ligand binding sites using improved blind docking method with a machine learning-based scoring function. Chem Eng Sci. 2022;261:117962.
  84. Mazzini S, Musso L, Dallavalle S, Artali R. Putative SARS-CoV-2 Mpro inhibitors from an in-house library of natural and nature-inspired products: A virtual screening and molecular docking study. Molecules. 2020;25(16):3745.
  85. Sierra-Campos E, Valdez-Solana M, Avitia-Domínguez C, Campos-Almazán M, Flores-Molina I, García-Arenas G, et al. Effects of Moringa oleifera leaf extract on diabetes-induced alterations in paraoxonase 1 and catalase in rats analyzed through progress kinetic and blind docking. Antioxidants (Basel). 2020;9(9):840.
  86. Che X, Liu Q, Zhang L. An accurate and universal protein-small molecule batch docking solution using Autodock Vina. Results Eng. 2023;19(7):101335.
  87. Yang Y, Yao K, Repasky MP, Leswing K, Abel R, Shoichet BK, et al. Efficient exploration of chemical space with docking and deep learning. J Chem Theory Comput. 2021;17(11):7106-19.
  88. Kawsar SM, Hosen MA, Chowdhury TS, Rana KM, Fujii Y, Ozeki Y. Thermochemical, PASS, molecular docking, drug-likeness and in silico ADMET prediction of cytidine derivatives against HIV-1 reverse transcriptase. Rev Chim. 2021;72(3):159-78.
  89. Masters M, Mahmoud AH, Lill MA. PocketNet: Ligand-guided pocket prediction for blind docking. InICLR 2023-machine learning for drug discovery workshop 2023 May 5.
How to cite this article
Vancouver
Sahu MK, Nayak AK, Hailemeskel B, Eyupoglu OE. Exploring Recent Updates on Molecular Docking: Types, Method, Application, Limitation & Future Prospects. Int J Pharm Res Allied Sci. 2024;13(2):24-40. https://doi.org/10.51847/Une9jqjUCl
APA
Sahu, M. K., Nayak, A. K., Hailemeskel, B., & Eyupoglu, O. E. (2024). Exploring Recent Updates on Molecular Docking: Types, Method, Application, Limitation & Future Prospects. International Journal of Pharmaceutical Research and Allied Sciences, 13(2), 24-40. https://doi.org/10.51847/Une9jqjUCl
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