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Artificial intelligence in drug design
Looks at applications of artificial intelligence (AI), machine learning (ML), and deep learning (DL) in drug design. The chapters in this book describe how AI/ML/DL approaches can be applied to accelerate and revolutionize traditional drug design approaches such as: structure- and ligand-based, augmented and multi-objective de novo drug design, SAR and big data analysis, prediction of binding/activity, ADMET, pharmacokinetics and drug-target residence time, precision medicine and selection of favorable chemical synthetic routes. How broadly are these approaches applied and where do they maximally impact productivity today and potentially in the near future.
Antisense RNA Design, Delivery, and Analysis
This volume gathers a variety of models, delivery systems, and approaches that can be used to assess RNA technology for exploiting antisense as a therapeutic intervention. Beginning with a section on the design of antisense technology and their delivery, the book continues by covering model systems developed to evaluate efficacy, both in vivo and in vitro, as well as methods to evaluate preclinically the toxicity associated with these new potential drugs, and intellectual property considerations. Written for the highly successful Methods in Molecular Biology series, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls.
