The Future of Computer-aided Drug Design
Elifsu Persilioglu · 2025
identification of druggable targets by analyzing gene expression profiles, protein interaction networks, and disease-associated mutations.Predictive modeling of pharmacokinetics and pharmacodynamics (PK/PD) properties also contributes to higher success rates.In silico methods like quantitative structure-activity relationship (QSAR) modeling, physiologically-based pharmacokinetic (PBPK) modeling, and molecular docking help predict a compound's absorption, distribution, metabolism, excretion, and toxicity (ADMET) profiles early in the discovery process(E.P. Chen et al., 2024; D. Kaushik & Kaushik, 2024a).By identifying potential liabilities such as poor solubility, off-target interactions, or metabolic instability, these tools guide the selection of candidates with favorable drug-like properties, thereby reducing the likelihood of clinical-stage failures.Molecular dynamics simulations provide further insights into the structural basis of drug resistance, an issue commonly encountered in the development of antibiotics, antivirals, and anticancer agents.For example, MD studies of HIV protease have elucidated the molecular mechanisms underlying resistance mutations, guiding the development of nextgeneration inhibitors with improved efficacy against resistant viral strains (Ali et al., 2010;A. K. Ghosh et al., 2008).Additionally, CADD facilitates the design of drugs with reduced toxicity profiles by predicting off-target effects and potential adverse reactions.Computational approaches such as inverse docking and polypharmacology modeling assess the interaction of candidate compounds with multiple biological targets, helping to identify and mitigate potential side effects (Kabir & Muth, 2022;Peters, 2024;Rigby, 2024).This proactive identification of toxicity risks aligns with regulatory guidelines emphasizing early safety assessments, ultimately contributing to higher clinical trial success rates.The integration of artificial intelligence (AI) and machine learning (ML)