Search

Alchemical free energy methods using molecular mechanics (MM) force fields are essential tools for predicting thermodynamic properties of small molecules, especially via free energy calculations that can estimate quantities relevant for drug discovery such as affinities, selectivities, the impact of target mutations, and ADMET properties. While traditional MM forcefields rely on hand-crafted, discrete atom types and parameters, modern approaches based on graph neural networks (GNNs) learn continuous embedding vectors that represent chemical environments from which MM parameters can be generated. Excitingly, GNN parameterization approaches provide a fully end-to-end differentiable model that offers the possibility of systematically improving these models using experimental data. In this study, we treat a pretrained GNN force field-here, espaloma-0.3.2-as a foundation simulation model and fine-tune its charge model using limited quantities of experimental hydration free energy data, with the goal of assessing the degree to which this can systematically improve the prediction of other related free energies. We demonstrate that a highly efficient "one-shot fine-tuning" method using an exponential (Zwanzig) reweighting free energy estimator can improve prediction accuracy without the need to resimulate molecular configurations. To achieve this "one-shot" improvement, we demonstrate the importance of using effective sample size (ESS) regularization strategies to retain good overlap between initial and fine-tuned force fields. Moreover, we show that leveraging low-rank projections of embedding vectors can achieve comparable accuracy improvements as higher-dimensional approaches in a variety of data-size regimes. Our results demonstrate that linearly-perturbative fine-tuning of foundation model electrostatic parameters to limited experimental data offers a cost-effective strategy that achieves state-of-the-art performance in predicting hydration free energies on the FreeSolv dataset.

Molecular mechanics (MM) potentials have long been a workhorse of computational chemistry. Leveraging accuracy and speed, these functional forms find use in a wide variety of applications in biomolecular modeling and drug discovery, from rapid virtual screening to detailed free energy calculations. Traditionally, MM potentials have relied on human-curated, inflexible, and poorly extensible discrete chemical perception rules (atom types) for applying parameters to small molecules or biopolymers, making it difficult to optimize both types and parameters to fit quantum chemical or physical property data. Here, we propose an alternative approach that uses graph neural networks to perceive chemical environments, producing continuous atom embeddings from which valence and nonbonded parameters can be predicted using invariance-preserving layers. Since all stages are built from smooth neural functions, the entire process-spanning chemical perception to parameter assignment-is modular and end-to-end differentiable with respect to model parameters, allowing new force fields to be easily constructed, extended, and applied to arbitrary molecules. We show that this approach is not only sufficiently expressive to reproduce legacy atom types, but that it can learn to accurately reproduce and extend existing molecular mechanics force fields. 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. When adapted to simultaneously fit partial charge models, espaloma delivers high-quality partial atomic charges orders of magnitude faster than current best-practices with low inaccuracy. When trained on the same quantum chemical small molecule dataset used to parameterize the Open Force Field ("Parsley") openff-1.2.0 small molecule force field augmented with a peptide dataset, the resulting espaloma model shows superior accuracy vis-á-vis experiments in computing relative alchemical free energy calculations for a popular benchmark. This approach is implemented in the free and open source package espaloma, available at https://github.com/choderalab/espaloma., Competing Interests: The authors declare no conflicts of interest, but highlight all sources of funding in Disclosures in the interest of full transparency., (This journal is © The Royal Society of Chemistry.)

Bright, photostable, and nontoxic fluorescent contrast agents are critical for biological imaging. "Self-healing" dyes, in which triplet states are intramolecularly quenched, enable fluorescence imaging by increasing fluorophore brightness and longevity, while simultaneously reducing the generation of reactive oxygen species that promote phototoxicity. Here, we systematically examine the self-healing mechanism in cyanine-class organic fluorophores spanning the visible spectrum. We show that the Baird aromatic triplet-state energy of cyclooctatetraene can be physically altered to achieve order of magnitude enhancements in fluorophore brightness and signal-to-noise ratio in both the presence and absence of oxygen. We leverage these advances to achieve direct measurements of large-scale conformational dynamics within single molecules at submillisecond resolution using wide-field illumination and camera-based detection methods. These findings demonstrate the capacity to image functionally relevant conformational processes in biological systems in the kilohertz regime at physiological oxygen concentrations and shed important light on the multivariate parameters critical to self-healing organic fluorophore design., Competing Interests: Competing interest statement: S.C.B. and R.B.A. have an equity interest in Lumidyne Technologies., (Copyright © 2020 the Author(s). Published by PNAS.)