Graph-Convolutional Neural Net Model of the Statistical Torsion Profiles for Small Organic Molecules.
J Chem Inf Model
; 62(23): 5896-5906, 2022 Dec 12.
Article
in En
| MEDLINE
| ID: mdl-36456533
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.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Neural Networks, Computer
Type of study:
Prognostic_studies
Language:
En
Journal:
J Chem Inf Model
Journal subject:
INFORMATICA MEDICA
/
QUIMICA
Year:
2022
Type:
Article
Affiliation country:
United States