NEW CURRENT TOPICS IN BIOLOGICAL SCIENCES

This academic book offers a comprehensive overview of Computer-Aided Drug Discovery (CADD) advancements and its integration within biotechnology. It covers the historical evolution of CADD, detailing fundamental techniques such as molecular docking, molecular dynamics simulations, and Quantitative Structure-Activity Relationship (QSAR) modeling. These methods significantly accelerate drug development processes through enhanced target identification, lead compound discovery, and optimization while ensuring cost and time efficiency. The text delves into the incorporation of artificial intelligence (AI) and machine learning (ML) into CADD, showcasing how AI addresses challenges like receptor flexibility in docking and improves QSAR prediction accuracy with models such as deep learning and random forests. It further explores emerging quantum computing and quantum machine learning approaches that hold promise for superior molecular simulations and target evaluation. The book underscores the pivotal role of integrating functional genomics, including CRISPR screens, and multi-omics data fusion powered by AI for robust target identification. The importance of federated learning to enable secure collaborative data analyses across distributed datasets is discussed, alongside advances in AI-driven protein structure prediction exemplified by AlphaFold, which revolutionize structure-based drug design and target druggability assessments. Ethical considerations, explainable AI, and regulatory challenges are comprehensively discussed, pointing to AI's critical role not only in predicting drug-target interactions and generating novel compounds via generative models but also in facilitating an end-to-end drug discovery pipeline from target identification to clinical development. Ultimately, the book emphasizes the harmonious synergy of AI with human expertise and multidisciplinary scientific advancements poised to transform computer-aided drug design and accelerate the journey from big biomedical data to effective therapeutic interventions.