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Graph-Convolutional Neural Net Model of the Statistical Torsion Profiles for Small Organic Molecules
Eugene Raush1, Ruben Abagyan2, Maxim Totrov1
1Molsoft L.L.C., 11199 Sorrento Valley Road, S209, San Diego, California92121, United States.
Abstract:
We present a graph-convolutional neural network (GCNN)-based method for learning and prediction of statistical torsional profiles (STP) in small organic molecules based on the experimental X-ray structure data. A specialized GCNN torsion profile model is trained using the structures in the Crystallography Open Database (COD). The GCNN-STP model captures torsional preferences over a wide range of torsion rotor chemotypes and correctly predicts a variety of effects from the vicinal atoms and moieties. GCNN-STP statistical profiles also show good agreement with quantum chemically (DFT) calculated torsion energy profiles. Furthermore, we demonstrate the application of the GCNN-STP statistical profiles for conformer generation. A web server that allows interactive profile prediction and viewing is made freely available at https://www.molsoft.com/tortool.html.
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