Transforming Computational Pharmacy in Drug Discovery and Development: Integrating CADD, Bioinformatics, Artificial Intelligence, and Pharmacometric Modeling

Abstract

Computational pharmacy has evolved from using molecular modeling and quantitative structure–activity relationship (QSAR) to a multidisciplinary ecosystem that integrates computer-aided drug design (CADD), bioinformatics, artificial intelligence (AI), molecular simulation, as well as pharmacokinetic and systems pharmacology modeling. This article aims to analyze how the convergence of these technologies shapes the workflow of modern drug discovery and development, and to identify opportunities, limitations, and research priorities that determine the translation of computational results into credible pharmaceutical decisions. The writing uses a qualitative literature review with an interpretative synthesis of reputable journal articles mainly published between 2020 and 2026. The review shows that the main value of computational pharmacy doesn't come from a single algorithm, but from the integration of the data–model–experiment cycle: databases and structure predictions expand target space; virtual screening, molecular dynamics, and free-energy calculations prioritize candidates; AI speeds up prediction and generative design; while PBPK and quantitative systems pharmacology bridge molecular findings to predictions of exposure, response, and populations. However, data quality, domain applicability, bias, interpretability, reproducibility, experimental validation, and regulatory acceptance still remain major challenges. The article concludes that the future of computational pharmacy will be shaped by hybrid physics- and data-based models, closed-loop experimentation, validated digital twins, and transparent, auditable model governance