Taking Some Guesswork Out of Drug Discovery

Researchers at the Massachusetts Institute of Technology have developed a deep learning model that can rapidly predict the likely three-dimensional shape of a molecule, given a two-dimensional graph of its structure. The deep learning GeoMol model developed by Massachusetts Institute of Technology (MIT) researchers can rapidly predict the three-dimensional shapes of drug-like molecules, which could expedite drug discovery. GeoMol's predictions are based solely on two-dimensional molecular graphs, and it can process molecules in seconds while outperforming other machine learning models, according to the researchers. The system utilizes a message passing neural network to forecast the lengths of chemical bonds between atoms and those bonds' angles; GeoMol then predicts the structure of each atom's local neighborhood and constructs neighboring pairs of rotatable bonds by computing and aligning torsion angles. MIT's Octavian-Eugen Ganea said GeoMol could help drugmakers indentify new drugs faster by reducing the number of molecules on which they must experiment.