Drug candidates often fail in the clinic not because they fail to engage their target, but because of ADMET: absorption, distribution, metabolism, excretion, and toxicity. Pat Walters has spent 30 years watching this happen. Now, as Chief Scientist at OpenADMET, he’s building the open data infrastructure to fix it.
Pat argues that data, not better algorithms, is the real bottleneck in applying AI to drug discovery, and explains why you can’t just pull reliable ADMET data from the literature. He walks through OpenADMET’s approach: generating large, consistent, publicly available datasets; running blind prediction challenges (370 groups participated in their first); and integrating structural biology to move from black-box models to mechanistic understanding.
OpenADMET is supported by Radial. Radial is the life sciences arm of Astera. Radial designs, funds, and operates high-impact programs that fit the structure to the problem. Radial is reimagining how science is done, shared, and scaled. Read more: https://radial.org/
Links
- OpenADMET: https://openadmet.org/
- Practical Cheminformatics: https://patwalters.github.io