AI-based language models powering drug discovery and development
| Autor: | Ruth A. Roberts, Ruili Huang, Madhu Lal-Nag, Weida Tong, Xi Chen, Zhichao Liu |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Pharmacology
Artificial intelligence Coronavirus disease 2019 (COVID-19) Drug discovery Computer science SARS-CoV-2 Natural language processing Language models COVID-19 Drug development Data science COVID-19 Drug Treatment Identification (information) Drug repositioning Pharmacovigilance ComputingMethodologies_PATTERNRECOGNITION Humans Language model Set (psychology) Keynote (Green) |
| Zdroj: | Drug Discovery Today |
| ISSN: | 1878-5832 |
| Popis: | The discovery and development of new medicines is expensive, time-consuming, and often inefficient, with many failures along the way. Powered by artificial intelligence (AI), language models (LMs) have changed the landscape of natural language processing (NLP), offering possibilities to transform treatment development more effectively. Here, we summarize advances in AI-powered LMs and their potential to aid drug discovery and development. We highlight opportunities for AI-powered LMs in target identification, clinical design, regulatory decision-making, and pharmacovigilance. We specifically emphasize the potential role of AI-powered LMs for developing new treatments for Coronavirus 2019 (COVID-19) strategies, including drug repurposing, which can be extrapolated to other infectious diseases that have the potential to cause pandemics. Finally, we set out the remaining challenges and propose possible solutions for improvement. |
| Databáze: | OpenAIRE |
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