This book focuses on the latest advances in computational de novo drug discovery methods, also known as generative drug discovery. This book describes the state-of-the-art methods and applications for de novo design of drug candidates using generative chemistry models as well as ethical aspects of this technology.
This book focuses on the latest advances in computational de novo drug discovery methods, also known as generative drug discovery. This book describes the state-of-the-art methods and applications for de novo design of drug candidates using generative chemistry models as well as ethical aspects of this technology.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Sean Ekins is founder and CEO of Collaborations Pharmaceuticals, Inc. (CPI) which is focused on using machine learning approaches for rare and neglected disease drug discovery. Sean graduated from the University of Aberdeen, receiving his M.Sc., Ph.D. in Clinical Pharmacology and D.Sc. in Science. He was a postdoctoral fellow at Eli Lilly, before working as a senior scientist at Pfizer and then returning to Eli Lilly. He went on to join several startup companies at increasingly senior levels. Since 2005 he has been awarded numerous grants as PI for a wide array of start-up companies totaling over $12.2M as well as performing as a consultant on others. Since 2016 he has additionally won over 20 additional grants from NIH and DOD (STTR/SBIR grants, R21, UH2 and R01) totaling over $21.2M for CPI. He has a passion for advancing new technologies for drug discovery and is a prolific collaborator. He has authored or co-authored over 370 peer reviewed papers, book chapters, edited 5 books on different aspects of drug discovery research and topics and written one book on winning grants. Coverage of his recent research has also appeared in the Economist, Financial Times, Washington Post, Wired, Scientific American, CNN and Netflix as well as several podcasts. When he is not writing he enjoys cycling and record collecting.
Inhaltsangabe
Front Pages. List of Contributors. Preface. Acknowledgments Part I - Introduction to Generative Chemical DesignGoing Beyond Serendipity: Generative Artificial Intelligence for Drug DiscoveryGenerative Drug Discovery Part II - Generative Chemical Models Based on Language ProcessingDe novo Drug Design by Chemical Language Modeling Part III - Generative Models and Synthetic AccessibilitySynthesis-based design - A Practical and Generalizable Approach to de novo Molecular DiscoveryA Medicinal Chemistry Perspective on Generative AIMegaSyn for Generative Molecule Design Part IV - From Models to PracticeGenerative Topographic Mapping of Chemical Space in de novo DesignIn Silico ADME/Tox in the Generative AI ParadigmThe Dark Side - Dual Use Implications of Generative Drug Discovery Part V - The FutureFuture Labs - Generative Approaches in Self Driving LabsThe Future of Generative Drug Discovery. Index.
Front Pages. List of Contributors. Preface. Acknowledgments Part I - Introduction to Generative Chemical DesignGoing Beyond Serendipity: Generative Artificial Intelligence for Drug DiscoveryGenerative Drug Discovery Part II - Generative Chemical Models Based on Language ProcessingDe novo Drug Design by Chemical Language Modeling Part III - Generative Models and Synthetic AccessibilitySynthesis-based design - A Practical and Generalizable Approach to de novo Molecular DiscoveryA Medicinal Chemistry Perspective on Generative AIMegaSyn for Generative Molecule Design Part IV - From Models to PracticeGenerative Topographic Mapping of Chemical Space in de novo DesignIn Silico ADME/Tox in the Generative AI ParadigmThe Dark Side - Dual Use Implications of Generative Drug Discovery Part V - The FutureFuture Labs - Generative Approaches in Self Driving LabsThe Future of Generative Drug Discovery. Index.
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