Machine-learned molecular mechanics force fields from large-scale quantum chemical data

Kenichiro Takaba, Anika J Friedman, Chapin E Cavender, Pavan Kumar Behara, Iván Pulido, 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
Chemical Science 15:12861, 2024 [DOI] [arXiv preprint]

We present a new self-consistent MM force field trained on $>$1.1M quantum chemical calculations that uses graph nets to achieve high accuracy and produce accurate protein-ligand binding free energies.

DrugGym: A testbed for the economics of autonomous drug discovery

Michael Retchin, Yuanqing Wang, Kenichiro Takaba, and John D. Chodera
[bioRxiv preprint]

We present DrugGym, a sandbox for exploring reinforcement learning strategies and evaluating the economics of decisionmaking strategies and predictive models on small molecule discovery. We use this tool to quantify the value of predictive model accuracy on hit-to-lead programs.

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

Wang Y, Pulido I, Takaba K, Kaminow B, Scheen J, Wang L, Chodera JD
preprint: [arXiv]

We present a drop-in replacement for generating AM1-BCC ELF10 charges based on graph convolutional nets that is orders of magnitude faster than standard methods for both small molecules and biomolecules.