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Taking Some Guesswork Out of Drug Discovery

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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.

Credit: MIT News

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.

From MIT News
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Abstracts Copyright © 2021 SmithBucklin, Washington, DC, USA


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