Bioinformatics in Drug Design: Integrating Pharmacology, Toxicology, and Pharmaceutics

Abstract

By combining computational biology, cheminformatics, and systems modelling with the conventional experimental fields of pharmacology, toxicology, and pharmaceutics, bioinformatics has completely changed contemporary drug discovery. Together with developments in artificial intelligence (AI), molecular modelling, and network-based analyses, the availability of extensive biological and chemical datasets has sped up the process of identifying new drug targets, predicting safety risks, and refining formulation techniques. Early-stage decision-making is changing due to advances in structural biology, like AlphaFold, and predictive toxicology tools, like ToxCast and ADMET modelling. Likewise, biopharmaceutics modelling (PBBM) and physiologically based pharmacokinetics (PBPK) are being used more and more to support regulatory submissions and model drug performance. Even with these developments, there are still significant obstacles to overcome, especially in the areas of model interpretability, data quality and standardisation, and international regulatory acceptance of in silico evidence. This review highlights gaps that impede translation into clinical and regulatory practice, offers a critical analysis of current bioinformatics applications in pharmacology, toxicology, and pharmaceutics, and suggests future directions. In order to make bioinformatics-driven drug design a pillar of precision medicine, we stress the significance of multimodal data integration, hybrid AI-mechanistic modelling approaches, and unified regulatory frameworks.