Zero Hunger (SDG 2), Good Health & Well-being (SDG 3)
Computer programs in the discovery of active pharmaceutical ingredients
Sphelele C. Sosibo 1,2 1 Department of Chemistry, School of Physical and Chemical Sciences, North-West University, Private Bag X2046, Mmabatho, 2735, 2 Materials Science Innovation and Modelling (MaSIM), North-West University, Private Bag X2046, Mmabatho E-mail: Sphelele.Sosibo@nwu.ac.za Computational chemistry techniques such as docking, molecular dynamics (MD), ADMET modelling, and QSAR analysis have significantly advanced drug discovery by streamlining drug candidate design, optimization, and evaluation. Docking simulations predict binding affinities between drugs and target proteins, while MD provides insights into the stability of these interactions. ADMET modelling evaluates candidates’ pharmacokinetics and safety profiles, reducing late-stage failures. QSAR correlates molecular properties with biological activity, and the integration of machine learning into QSAR has enhanced predictive accuracy by uncovering complex relationships within large datasets. These methods have accelerated drug development, improved the efficiency of R&D, and reduced reliance on traditional
experimental approaches, ultimately driving innovation in drug discovery. Key words: Molecular docking, molecular dynamics, density functional theory References 1. Sosibo SC et al, 2024, Chemical Physics Impact 9, 100757
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