%0 Journal Article
%T Pharmacoinformatics after Deep Learning: A State-of-the-Art Review of Molecular Representations, Benchmark Design, and Generalization across Chemical Space
%A Youssef Hariri
%A Hassan Nasser
%A Fatima Zahra
%J International Journal of Pharmaceutical Research and Allied Sciences
%@ 2277-3657
%D 2025
%V 14
%N 1
%R 10.51847/DOiTQHEsxY
%P 46-57
%X 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-the-art review examines the current technical frontier in molecular representation learning, benchmark design, and generalization across chemical space. It organizes descriptor-based, fingerprint-based, graph, sequence, geometric, and multimodal approaches according to the information they encode, the assumptions they impose, and the pharmaceutical questions they can reasonably address. The review also compares supervised, self-supervised, contrastive, transfer-learning, and foundation-model strategies while distinguishing retrospective prediction from externally tested, prospective, experimental, or operational evidence. Particular attention is given to benchmark construction, scaffold and temporal splitting, activity cliffs, out-of-distribution evaluation, predictive calibration, interpretability, assay shift, and reproducibility. The reviewed evidence indicates that learned representations can improve selected molecular-property and bioactivity tasks, yet their relative performance remains dependent on endpoint definition, data curation, chemical-space coverage, split strategy, model tuning, and comparator strength. Richer representations do not automatically yield better generalization, mechanistic understanding, or decision value. Research priorities therefore include assay-aware representation learning, prospective and cross-organizational validation, calibrated uncertainty under distribution shift, chemically meaningful stress testing, transparent reporting, and evaluation frameworks connected to intended pharmaceutical use. The central conclusion is that post-deep-learning pharmacoinformatics should be judged not by representational novelty alone, but by evidence that a model remains informative when chemistry, assays, time, and decision context change.
%U https://ijpras.com/article/pharmacoinformatics-after-deep-learning-a-state-of-the-art-review-of-molecular-representations-ben-zns0e0ohxg6rqtn