Machine Learning in Drug Development: Part 2
- 1 Edición, Volumen 65 - 6 de noviembre de 2025
- Última edición
- Editores: Joy Feng, Katherine Seley-Radtke
- Idioma: Inglés
Machine Learning in Drug Development: Part Two, Volume 65 in the Annual Reports on Medicinal Chemistry series, highlights new advances in the field, with this new volume presentin… Leer más
Descripción
Descripción
Machine Learning in Drug Development: Part Two, Volume 65 in the Annual Reports on Medicinal Chemistry series, highlights new advances in the field, with this new volume presenting interesting chapters written by an international board of authors. Chapters in this volume explore Transforming Modern Drug Discovery with Machine Learning – Applications in Ligand-based Drug Design, Optimizing Multi-Modal Drug Design Through Computational Pocket Mapping and Data Integration, Harnessing AI for Nucleic Acid Drug Discovery: Small Molecule Targeting DNA and RNA, AI-aided Drug Development for Protein Degraders: Design, Lead Identification, and Optimization, and more.
Additional section delve into Artificial Intelligence in the Development of Antiviral Drugs – Progress and Applications, Artificial Intelligence for Drug Target Identification, and Machine Learning in Proteomic Biomarker Discovery
Puntos claves
Puntos claves
- Provides the authority and expertise of leading contributors from an international board of authors
- Presents the latest release in the Annual Reports on Medicinal Chemistry series
- Includes the latest information in the field
De interès para
De interès para
Ideally suited for chemists engaged in multidisciplinary teams for drug discovery including medicinal chemists and others involved in chemical biology and bio-organic disciplines and computational chemistry
Índice
Índice
1. Transforming Modern Drug Discovery with Machine Learning – Applications in Ligand-based Drug Design
2. Optimizing Multi-Modal Drug Design Through Computational Pocket Mapping and Data Integration
3. Harnessing AI for Nucleic Acid Drug Discovery: Small Molecule Targeting DNA and RNA
4. AI-aided Drug Development for Protein Degraders: Design, Lead Identification, and Optimization
5. Artificial Intelligence in the Development of Antiviral Drugs – Progress and Applications
6. Artificial Intelligence for Drug Target Identification
7. Machine Learning in Proteomic Biomarker Discovery
Detalles del producto
Detalles del producto
- Edición: 1
- Última edición
- Volumen: 65
- Publicado: 6 de noviembre de 2025
- Idioma: Inglés
Sobre los editores
Sobre los editores
JF
Joy Feng
Joy is an Associate Professor of Pediatrics at Emory University with a 25-year experience in the pharmaceutical industry. She received her B.S. from Peking University School of Pharmaceutical Sciences, her Ph.D. in Medicinal Chemistry from Dr. Raymond Bergeron’s lab at the University of Florida School of Pharmacy, and postdoctoral training in enzymology in Dr. Karen Anderson’s lab at Yale University School of Medicine. Joy’s research focuses on drug mechanisms of action, drug combinations, drug resistance, drug metabolism, off-target effects, and toxicity. Joy contributed to the approval of three marketed drugs: Emtricitabine (FTC) for HIV, Sofosbuvir for HCV, and is one of the inventors of Remdesivir, the first FDA-approved direct antiviral for treating COVID-19, and Obeldesivir (GS-5245), currently in clinical trials for the treatment of RSV infection.
KS