Pharmacoinformatics after Deep Learning: A State-of-the-Art Review of Molecular Representations, Benchmark Design, and Generalization across Chemical Space

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.