Graph Nets for Partial Charge Prediction
This addresses a bottleneck in computational chemistry for researchers and practitioners by providing a fast and accurate alternative to existing methods.
The paper tackles the problem of slow and unreliable partial charge prediction for molecular dynamics by introducing a Graph Nets-based method that approximates DFT-derived charges with high accuracy and over 500-fold speedup.
Atomic partial charges are crucial parameters for Molecular Dynamics (MD) simulations, molecular mechanics calculations, and virtual screening, as they determine the electrostatic contributions to interaction energies. Current methods for calculating partial charges, however, are either slow and scale poorly with molecular size (quantum chemical methods) or unreliable (empirical methods). Here, we present a new charge derivation method based on Graph Nets---a set of update and aggregate functions that operate on molecular topologies and propagate information thereon---that could approximate charges derived from Density Functional Theory (DFT) calculations with high accuracy and an over 500-fold speed up.