Machine-learned molecular mechanics force field for the simulation of protein-ligand systems and beyond

5 code implementations • 13 Jul 2023 • Kenichiro Takaba, Iván Pulido, Pavan Kumar Behara, Chapin E. Cavender, Anika J. Friedman, Michael M. Henry, Hugo MacDermott Opeskin, Christopher R. Iacovella, Arnav M. Nagle, Alexander Matthew Payne, Michael R. Shirts, David L. Mobley, John D. Chodera, Yuanqing Wang

The development of reliable and extensible molecular mechanics (MM) force fields -- fast, empirical models characterizing the potential energy surface of molecular systems -- is indispensable for biomolecular simulation and computer-aided drug design.

Drug Discovery

EspalomaCharge: Machine learning-enabled ultra-fast partial charge assignment

1 code implementation • 14 Feb 2023 • Yuanqing Wang, Iván Pulido, Kenichiro Takaba, Benjamin Kaminow, Jenke Scheen, Lily Wang, John D. Chodera

Our hybrid approach couples a graph neural network to a streamlined charge equilibration approach in order to predict molecule-specific atomic electronegativity and hardness parameters, followed by analytical determination of optimal charge-equilibrated parameters that preserves total molecular charge.

End-to-End Differentiable Molecular Mechanics Force Field Construction

3 code implementations • 2 Oct 2020 • Yuanqing Wang, Josh Fass, Benjamin Kaminow, John E. Herr, Dominic Rufa, Ivy Zhang, Iván Pulido, Mike Henry, John D. Chodera

Trained with arbitrary loss functions, it can construct entirely new force fields self-consistently applicable to both biopolymers and small molecules directly from quantum chemical calculations, with superior fidelity than traditional atom or parameter typing schemes.

Drug Discovery