New Deep Learning Model Could Accelerate the Process of Discovering New Medicines
Taking Some of the Guesswork Out of Drug Discovery
A deep learning model rapidly predicts the 3D shapes of drug-like molecules, which could accelerate the process of discovering new medicines.
In their quest to discover effective new medicines, scientists search for drug-like molecules that can attach to disease-causing proteins and change their functionality. It is crucial that they know the 3D shape of a molecule to understand how it will attach to specific surfaces of the protein.
But a single molecule can fold in thousands of different ways, so solving that puzzle experimentally is a time-consuming and expensive process akin to searching for a needle in a molecular haystack.
MIT researchers are using machine learning to streamline this complex task. They have created a deep learning model that predicts the 3D shapes of a molecule solely based on a graph in 2D of its molecular structure. Molecules are typically represented as small graphs.
Their system, GeoMol, processes molecules in only seconds and performs better than other machine learning models, including some commercial methods. GeoMol could help pharmaceutical companies accelerate the drug discovery process by narrowing down the number of molecules they need to test in lab experiments, says Octavian-Eugen Ganea, a postdoc in the Computer Science and Artificial Intelligence Laboratory (CSAIL) and co-lead author of the paper.
“When you are thinking about how these structures move in 3D space, there are really only certain parts of the molecule that are actually flexible, these rotatable bonds. One of the key innovations of our work is that we think about modeling the conformational flexibility like a chemical engineer would. It is really about trying to predict the potential distribution of rotatable bonds in the structure,” says Lagnajit Pattanaik, a graduate student in the Department of Chemical Engineering and co-lead author of the paper.
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