Description
Book SynopsisThe History of Artificial Intelligence and Drug Discovery.- Data Mining and Integration Approaches in AI-driven Drug Discovery.- Artificial Intelligence for Drug Target and Pathway Identification, Assessment, Validation, and Indication Expansion.- Artificial Intelligence in Structure-Based Drug Design.- Artificial intelligence in Ligand-Based drug design.- Artificial Intelligence in De Novo Drug Design.- Artificial Intelligence in Peptide Drug Discovery.- Deep Learning for In Silico ADMET Prediction.- Harnessing Artificial Intelligence to Revolutionize Molecular Modelling and Simulations.- Drug discovery with quantum machine learning.- AI-Driven Discovery of MicroRNA Targets for Disease Therapy and Drug Development.- AI in Retrosynthesis: Introduction, Methods, Evaluation, and Future Directions.- Active Learning in Drug Discovery: Revolutionizing Chemical Space Exploration.- Large Language Models in Drug Discovery.- Contrastive Learning Approaches for Drug Discovery.- Few-shot Learning in Drug Discovery.- Explainable Artificial Intelligence in Drug Discovery.- Federated Learning in Drug Discovery: Challenges, Innovations and Future Directions.- Revolutionizing drug delivery: the role of artificial intelligence in nanomedicine and precision pharma.- Artificial Intelligence-Driven and In Silico Approaches in Health Emergencies: A Case Study on Antiviral Drug Discovery.- Practical and Reproducible AI-driven Modeling Protocols in Drug Discovery.- AI-based Platforms for Drug Discovery: Current Tools and Human-Centered Design Strategies.- AI and ML-Driven Strategies for Drug Repurposing: Tech-niques, Applications, and Challenges.- Artificial intelligence in clinical trials: from protocol design to pharmacovigilance.- Leveraging Generative AI in Clinical Studies to Improve Efficiency and Quality of Drug Development.- AI-Driven Advances in Personalized Therapeutic Strategies for Precision Medicine.- Challenges and Future Directions in Al for Drug Discovery.
