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Princeton University Library Catalog
Artificial intelligence in drug design / edited by Alexander Heifetz.
New York, NY : Humana Press, 
xi, 529 pages : illustrations (some color) ; 26 cm.
Methods in molecular biology (Clifton, N.J.) ; v. 2390.
[More in this series]
Springer protocols (Series)
[More in this series]
Methods in molecular biology, 1064-3745 ; 2390
Springer protocols, 1949-2448
This volume 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. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary software and tools, step-by-step, readily reproducible modeling protocols, and tips on troubleshooting and avoiding known pitfalls. Cutting-edge and unique, Artificial Intelligence in Drug Design is a valuable resource for structural and molecular biologists, computational and medicinal chemists, pharmacologists and drug designers.
Includes bibliographical references and index.
Rights and reproductions note
Current copyright fee: GBP19.00 42\0.
Applications of Artificial Intelligence in Drug Design: Opportunities and Challenges
Machine Learning Applied to the Modeling of Pharmacological and ADMET Endpoints
Fighting COVID-19 with Artificial Intelligence
Application of Artificial Intelligence and Machine Learning in Drug Discovery
Deep Learning and Computational Chemistry
Has Drug Design Augmented by Artificial Intelligence Become a Reality?
Network Driven Drug Discovery
Predicting Residence Time of GPCR Ligands with Machine Learning
De Novo Molecular Design with Chemical Language Models
Deep Neural Networks for QSAR
Deep Learning in Structure-Based Drug Design
Deep Learning Applied to Ligand-Based De Novo Drug Design
Ultra-High Throughput Protein-Ligand Docking with Deep Learning
Artificial Intelligence and Quantum Computing as the Next Pharma Disruptors
Artificial Intelligence in Compound Design
Artificial Intelligence, Machine Learning, and Deep Learning in Real Life Drug Design Cases
Artificial Intelligence-Enabled De Novo Design of Novel Compounds that are Synthesizable
Machine Learning from Omics Data
Deep Learning in Therapeutic Antibody Development
Machine Learning for In Silico ADMET Prediction
Opportunities and Considerations in the Application of Artificial Intelligence to Pharmacokinetic Prediction
Artificial Intelligence in Drug Safety and Metabolism
Molecule Ideation Using Matched Molecular Pairs.
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Artificial Intelligence in Drug Design [electronic resource] / edited by Alexander Heifetz.