Materials design enables technologies critical to humanity, including combating climate change with solar cells and batteries.
Interpretability of machine learning models is critical to scientific understanding, AI safety, as well as debugging.
no code implementations • 31 Jan 2020 • Kevin McCloskey, Eric A. Sigel, Steven Kearnes, Ling Xue, Xia Tian, Dennis Moccia, Diana Gikunju, Sana Bazzaz, Betty Chan, Matthew A. Clark, John W. Cuozzo, Marie-Aude Guié, John P. Guilinger, Christelle Huguet, Christopher D. Hupp, Anthony D. Keefe, Christopher J. Mulhern, Ying Zhang, Patrick Riley
We demonstrate a new approach applying machine learning to DEL selection data by identifying active molecules from a large commercial collection and a virtual library of easily synthesizable compounds.
The dataset bias makes these models unreliable for accurately revealing information about the mechanisms of protein-ligand binding.
Molecular "fingerprints" encoding structural information are the workhorse of cheminformatics and machine learning in drug discovery applications.
Ranked #4 on Graph Regression on Lipophilicity