Issue 1 Volume 14 (2025)

The ability of crude ethanol extracts from the leaves of Tamarindus indica and Calotropis procera to repel insects. This work has investigated many household insects. The leaf extract from C. procera was shown to have higher insecticidal action. The study indicates that the leaf extracts from these plants are promising candidates for use in the development of bioinsecticides, which may be less expensive and safer than synthetic pesticides when it comes to managing the population of domestic inse

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Research in ophthalmology in India has raised concerns about the improper selection and prescription of essential and generic medicines. To address this, the current study analyzed drug usage patterns in the ophthalmology department of a tertiary care hospital. This study was prospective and observational, conducted from January to December 2021. It included patients visiting the ophthalmology OPD but excluded those with glaucoma, cataracts, and hospitalized cases. Patient information was docume

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Molecular-dynamics simulation can reveal motions, interactions, and state transitions that are difficult to observe directly, but its evidential strength is frequently inferred from computational scale rather than demonstrated through reproducibility. Longer trajectories, larger systems, elaborate enhanced-sampling calculations, and visually persuasive conformational movies may increase descriptive richness without resolving uncertainty arising from system preparation, force-field selection, ini

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Drug–target recognition is often represented by a preferred binding pose and a scalar estimate of binding favorability. These representations are valuable for structural interpretation, but they become pharmacologically incomplete when affinity, selectivity, efficacy, or residence time depends on alternative molecular states, hidden intermediates, transition barriers, allosteric coupling, or condition-dependent population shifts. This article proposes Conformational-Ensemble Drug–Target Recognit

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Molecular pharmacology spans a temporal range that extends from femtosecond-scale bond and electronic motions to clinical-scale patterns of exposure, efficacy, and failure. The central difficulty is not merely computational cost. It is the absence of a disciplined architecture for deciding when information obtained at one scale can legitimately constrain claims at another. This article proposes the Temporal–Mechanistic Translation Architecture, an original conceptual synthesis that connects mole

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Deep learning has expanded pharmacoinformatics from models built primarily on predefined descriptors and fingerprints toward architectures that learn representations from molecular graphs, strings, three-dimensional structures, assay context, and multiple aligned modalities. This expansion has produced increasingly flexible modelling systems, but it has also encouraged a misleading equation between representational complexity, benchmark performance, and pharmaceutical usefulness. This state-of-t

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External validation is frequently treated as a binary property of quantitative structure–activity relationship models, although evaluation sets can differ from development data along chemically and experimentally distinct dimensions. A holdout may be structurally distant yet generated in the same assay and laboratory, chronologically later yet dominated by familiar chemical series, or externally sourced while retaining substantial analogue overlap. This methodological standards article proposes

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Molecular dynamics simulation has become central to pharmaceutical research because it can represent time-dependent molecular behaviour at spatial and temporal resolutions that are difficult to access experimentally. Its interpretive reach, however, is often extended beyond what a simulated trajectory can directly support. Structural persistence may be described as thermodynamic stability, incomplete sampling may be presented as a conformational ensemble, binding-pose retention may be treated as

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Interpretable structure–activity modelling is increasingly expected to provide not only accurate predictions but also scientifically credible reasons for them. Yet prediction, interpretation, explanation, and molecular mechanism represent different epistemic achievements, and their conflation can produce persuasive but unsupported pharmaceutical narratives. This Original Interpretability Architecture Article defines the prediction–explanation divide and proposes a Decision-Adaptive Prediction–Ex

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Quantitative structure activity relationship models support molecular prioritization, property estimation, virtual screening, and evidence preparation, yet apparently strong performance can create false confidence when detached from data provenance, endpoint meaning, chemical space coverage, applicability domains, calibration, dataset shift, and decision consequences. This article proposes the QSAR Model-Evidence Dossier, or Q-MED, as an original nonempirical architecture for organizing those di

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Pharmacoinformatics has become increasingly effective at identifying statistical relationships among molecular structure, biological measurements, and pharmaceutical outcomes, yet prediction alone does not establish what would happen if a compound, target, dose, pathway, or treatment policy were deliberately changed. This article develops an original, non-empirical causal-modelling framework for moving from molecular correlation toward mechanistic intervention in drug discovery, safety assessmen

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Pharmacovigilance can detect unusual drug–event reporting patterns long before their clinical meaning is settled. The interpretive error is to treat signal strength, exposed-population risk, excess above expected occurrence, temporal plausibility, and causal credibility as though they were interchangeable quantities. They are not. Exposure denominators may be incomplete or misclassified; background event rates vary across populations, databases, phenotypes, and calendar periods; and the meaning

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Pharmacovigilance often treats a statistically prominent drug–event association as if it were inherently closer to clinical or regulatory action. That inference is unsafe. Disproportionality, machine-learning prioritisation, longitudinal healthcare data, case-level assessment, and other signal methods answer questions about whether an association is unusual, reproducible, temporally coherent, or evidentially credible. They do not by themselves determine whether an intervention is warranted. Acti

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Compelling target biology is often treated as the beginning of a translational trajectory, yet mechanistic plausibility does not establish that a therapeutic perturbation reaches the relevant tissue, engages its target under biologically realistic conditions, produces an interpretable pharmacodynamic response, or acts in patients whose disease state remains dependent on that mechanism. This Translational Perspective argues that these evidence layers should be treated as distinct but connected te

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Translational biomarkers are often expected to reduce uncertainty between preclinical experiments and early human development, yet many fail precisely at the point where they are asked to carry an inference across biological, analytical, pharmacological, or clinical boundaries. This review argues that biomarker failure is rarely a single event. A marker can be analytically robust but biologically nonrepresentative, transferable across assays but not across species or disease states, exposure-res

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Drug–target residence time is often discussed as though it were a scalar molecular attribute attached to a ligand–protein pair. Molecular simulation increasingly makes that interpretation difficult to sustain. Long-timescale and enhanced-sampling studies show that dissociation can proceed through multiple bound microstates, kinetically distinct exit routes, transition-state configurations, and protein–solvent reorganizations, while the inferred kinetic observable itself depends on sampling strat

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