Introduction:: Computer modeling is an indispensable feature of contemporary drug discovery and development, providing practical approaches to enhance effectiveness, reduce expenses, and improve outcomes. This systematic review presents the role of computational and mathematical models in various stages of drug discovery, such as target identification, lead optimization, and clinical trials. Methods:: Molecular docking enables predictions of how well a drug will bind to its target. Quantitative Structure-Activity Relationship (QSAR) modeling establishes relationships between the structural characteristics of a drug and its activity, and molecular dynamics simulations allow analysis of the interaction between a drug molecule and its target at the quantum level. Pharmacokinetic/Pharmacodynamic (PK/PD) models explain the behavior of a drug in the body, aid in selecting the optimal dose, and anticipate potential side effects. Computer sciences, specifically machine learning and Artificial Intelligence (AI), serve as transformative approaches for analyzing extensive datasets and predicting drug efficacy and safety profiles. Results:: These techniques also facilitate drug repurposing to treat new diseases more rapidly. This review emphasizes the potential of in silico, in vitro, and in vivo approaches in developing a robust drug discovery pipeline. Discussion:: It also highlights the challenges posed by regulatory guidelines and the effort required to understand and validate models. However, some barriers are found: the usability of biological systems and the need for high data quality. Conclusion:: This review shows that modelling approaches can significantly change drug discovery research, reduce costs, and be more patient-centric. Characterized as essential for advancing precision medicine, modelling bridges the discrepancies between laboratory research and clinical application.
Panda et al. (Tue,) studied this question.