Abstract

EXPLORING THE ROLE OF ARTIFICIAL INTELLIGENCE IN DEVELOPING THE FUTURE OF MEDICINAL CHEMISTRY

G. Tamilarasi*, G. Deventhiran, R. Aravind, G. S. Baby Bavasree, R. Deepika and
P. Dinesh Kumar

ABSTRACT

This review examines the essential role of artificial intelligence (AI)and machine learning (ML) in advancing medicinal chemistry,particularly in drug discovery and development. These technologies arerevolutionizing target identification, structure-based drug design, andmolecular interaction predictions by analyzing biological data toidentify disease-related proteins and generate novel compounds usingmethods like Self-Organizing Maps (SOM). Recent advancements,such as deep learning models like AlphaFold, have improved proteinstructure prediction, offering critical insights for therapeutic design.The integration of quantum mechanics with AI also enhances drugtargetinteraction predictions, while neural networks effectively modelcomplex molecular data. Additionally, the review highlights AI'scontributions to predictive chemistry and synthetic planning, especiallythrough initiatives like the Machine Learning for PharmaceuticalDiscovery and Synthesis (MLPDS). Computer-aided synthesis planning (CASP) streamlinesworkflows by refining retrosynthesis and recommending reaction conditions. AI-driventoxicity prediction models are crucial in early drug development, assessing potential adverseeffects, including hepatotoxicity and cardiotoxicity. Techniques like support vectormachines and QSAR models improve toxicity risk management through personalizedpredictions based on genomic and phenotypic data. Overall, AI integration in medicinalchemistry holds great promise for enhancing drug safety, efficacy, and discovery efficiency.

Keywords: Artificial Intelligence (AI), Machine Learning(ML), Quantum Mechanics, Molecular Interaction Prediction, Toxicity Prediction.