On the virtues of automated quantitative structure-activity relationship: the new kid on the block
| Autor: | Edson Katekawa, Marcelo T. de Oliveira |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
0301 basic medicine
Quantitative structure–activity relationship media_common.quotation_subject Chemistry Pharmaceutical Quantitative Structure-Activity Relationship Context (language use) Machine learning computer.software_genre Field (computer science) Machine Learning 03 medical and health sciences Automation Drug Discovery Quality (business) media_common Block (data storage) Pharmacology business.industry Art Data availability Identification (information) 030104 developmental biology Molecular Medicine Artificial intelligence business computer Cartography Algorithms |
| Zdroj: | Future medicinal chemistry. 10(3) |
| ISSN: | 1756-8927 |
| Popis: | Quantitative structure–activity relationship (QSAR) has proved to be an invaluable tool in medicinal chemistry. Data availability at unprecedented levels through various databases have collaborated to a resurgence in the interest for QSAR. In this context, rapid generation of quality predictive models is highly desirable for hit identification and lead optimization. We showcase the application of an automated QSAR approach, which randomly selects multiple training/test sets and utilizes machine-learning algorithms to generate predictive models. Results demonstrate that AutoQSAR produces models of improved or similar quality to those generated by practitioners in the field but in just a fraction of the time. Despite the potential of the concept to the benefit of the community, the AutoQSAR opportunity has been largely undervalued. |
| Databáze: | OpenAIRE |
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