- Overview: AI in drug development accelerates the process of discovering and developing new medications by analyzing large datasets and predicting outcomes.
- Key Technologies: Machine learning, deep learning, bioinformatics, computational chemistry.
- Applications: Drug discovery, preclinical testing, clinical trial design, personalized medicine.
- Benefits: Reduced time and cost of drug development, increased success rates, identification of new drug targets.
- Challenges: Data quality and availability, regulatory hurdles, integration with existing research methods.
- Case Studies: BenevolentAI's drug discovery platform, Atomwise's AI for molecular screening, Insilico Medicine's AI for aging research.
Last update on July 8, 2:27 am by Admin.
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