Autonomous, multi-property-driven molecular discovery: from predictions to measurements and back
Published in ChemRxiv, 2023
Recommended citation: Koscher, Brent; Canty, Richard B; McDonald, Matthew A; Greenman, Kevin P; McGill, Charles J; Bilodeau, Camille L; Jin, Wengong; Wu, Haoyang; Vermeire, Florence H; Jin, Brooke; Hart, Travis; Kulesza, Timothy; Li, Shih-Cheng; Jaakkola, Tommi S; Barzilay, Regina; Gómez-Bombarelli, Rafael; Green, William H; Jensen, Klavs F. (2023). "Autonomous, multi-property-driven molecular discovery: from predictions to measurements and back." ChemRxiv. https://chemrxiv.org/engage/chemrxiv/article-details/6435f8c5a41dec1a56e64577
Abstract
A closed-loop, autonomous molecular discovery platform driven by integrated machine learning tools was developed to accelerate the design of molecules with desired properties. Two case studies are demonstrated on dye-like molecules, targeting absorption wavelength, lipophilicity, and photo-oxidative stability. In the first, the platform experimentally realized 312 unreported molecules across three automatic iterations of molecular design-make-test-analyze cycles while exploring the structure–function space of four rarely reported scaffolds. In each iteration, the property-prediction models which guided the exploration learned the structure–property space of diverse inexpensive scaffold derivatives realized through using multi-step syntheses. Conversely, the second study exploited property models trained on a chemical space with pre-existing examples to discover 6 top-performing molecules within the structure-property space. By closing the molecular discovery cycle of prediction, synthesis, measurement, and model retraining, the platform demonstrates the potential for integrated platforms to automatically understand a local chemical space and discover functional molecules.
