Espaloma-0.3.0: Machine-learned molecular mechanics force field for the simulation of protein-ligand systems and beyond

Molecular mechanics (MM) force fields -- the models that characterize the energy landscape of molecular systems via simple pairwise and polynomial terms -- have traditionally relied on human expert-curated, inflexible, and poorly extensible discrete chemical parameter assignment rules, namely atom or valence types. Recently, there has been significant interest in using graph neural networks to replace this process, while enabling the parametrization scheme to be learned in an end-to-end differentiable manner directly from quantum chemical calculations or condensed-phase data. In this paper, we extend the Espaloma end-to-end differentiable force field construction approach by incorporating both energy and force fitting directly to quantum chemical data into the training process. Building on the OpenMM SPICE dataset, we curate a dataset containing chemical spaces highly relevant to the broad interest of biomolecular modeling, covering small molecules, proteins, and RNA. The resulting force field, espaloma 0.3.0, self-consistently parametrizes these diverse biomolecular species, accurately predicts quantum chemical energies and forces, and maintains stable quantum chemical energy-minimized geometries. Surprisingly, this simple approach produces highly accurate protein-ligand binding free energies when self-consistently parametrizing protein and ligand. This approach -- capable of fitting new force fields to large quantum chemical datasets in one GPU-day -- shows significant promise as a path forward for building systematically more accurate force fields that can be easily extended to new chemical domains of interest

choderalab/geometry-benchmark-espaloma: Small molecule geometry benchmark dataset to validate espaloma-0.3

This is a collection of preprocessed QM and MM optimized structures needed to perform the small molecule geometry benchmark study, described in the espaloma-0.3 paper: Kenichiro Takaba, Iván Pulido, Pavan Kumar Behara, Mike Henry, Hugo MacDermott Opeskin, John D. Chodera, Yuanqing Wang. "Machine-learned molecular mechanics force field for the simulation of protein-ligand systems and beyond." (TBA) This benchmark study calculates and compares the RMSD, TFD, and ddE metrics for a specified set of MM force fields. The initial optimized structures were sourced from the OpenFF Industry Benchmark Season 1 v1.1 dataset, which is available through QCArchive. More details about the preprocessing steps is available at https://github.com/choderalab/geometry-benchmark-espaloma/tree/main/qc-opt-geo. 02-chunks.tar.gz: QM optimized structures chunked into small file sizes. 02-outputs-openff-2.0.0-espaloma-0.3.0rc1.tar.gz: MM optimized structures using openff-2.0.0 and espaloma-0.3.0rc1 force field (former release candidate of espaloma-0.3) 02-outputs-espaloma-0.3.0rc6.tar.gz: MM optimized structures using espaloma-0.3.0rc6 (espaloma-0.3) force field 02-outputs-openff-2.1.0.tar.gz: MM optimized structures using openff-2.1.0 force fiel

Double-Helix Supramolecular Nanofibers Assembled from Negatively Curved Nanographenes

The layered structures of graphite and related nanographene molecules play key roles in their physical and electronic functions. However, the stacking modes of negatively curved nanographenes remains unclear, owing to the lack of suitable nanographene molecules. Herein we report the synthesis and one-dimensional supramolecular self-assembly of negatively curved nanographenes without any assembly-assisting substituents. This curved nanographene self-assembles in various organic solvents and acts as an efficient gelator. The formation of nanofibers was confirmed by microscopic measurements, and an unprecedented double-helix assembly by continuous π-π stacking was uncovered by three-dimensional electron crystallography. This work not only reports the discovery of an all-sp2-carbon supramolecular π-organogelator with negative curvature, but also demonstrates the power of three-dimensional electron crystallography for the structural determination of submicrometer-sized molecular alignment