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Machine Learning in Drug Discovery and Development Part 1: A Primer.
Alan Talevi1, Juan Francisco Morales1, Gregory Hather2
1Laboratorio de Investigación y Desarrollo de Bioactivos (LIDeB), Faculty of Exact Sciences, National University of La Plata (UNLP), Buenos Aires, Argentina.
Artificial intelligence, specifically machine learning (ML), offers a promising approach to reduce drug development failures. This primer covers essential ML algorithms, data sources, and best practices for model development in drug discovery.
Area of Science:
- Computational chemistry and pharmacology
- Biotechnology and pharmaceutical sciences
- Data science in medicine
Background:
- Drug development faces high attrition rates, increasing costs and timelines.
- Artificial intelligence (AI) and machine learning (ML) show potential to improve efficiency and success.
- A foundational understanding of ML is crucial for its effective application in pharmaceuticals.
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