New Perspectives on Machine Learning in Drug Discovery
| Autor: | Simona Musella, Giulio Verna, Simone Di Micco, Alessio Fasano |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Drug
drug design Computer science media_common.quotation_subject Machine learning computer.software_genre 01 natural sciences Biochemistry Machine Learning 03 medical and health sciences Drug Development Artificial Intelligence Drug Discovery Humans 0101 mathematics 030304 developmental biology media_common ADMET artificial intelligence drug repurposing personalized medicine synthesis planning virtual screening Pharmacology 0303 health sciences Virtual screening Scope (project management) business.industry Drug discovery Organic Chemistry 010101 applied mathematics Clinical trial Drug repositioning Drug development Molecular Medicine Personalized medicine Artificial intelligence business computer |
| Zdroj: | Current Medicinal Chemistry. 28:6704-6728 |
| ISSN: | 0929-8673 |
| Popis: | Artificial intelligence methods, in particular, machine learning, has been playing a pivotal role in drug development, from structural design to the clinical trial. This approach is harnessing the impact of computer-aided drug discovery due to large available data sets for drug candidates and its new and complex manner of information interpretation to identify patterns for the study scope. In the present review, recent applications related to drug discovery and therapies are assessed, and limitations and future perspectives are analyzed. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|