End-to-End Differentiable Molecular Mechanics Force Field Construction

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 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 molecules or biopolymers, making them difficult to optimize to fit quantum chemical or physical property data. Here, we propose an alternative approach that uses graph nets to perceive chemical environments, producing continuous atom embeddings from which valence and nonbonded parameters can be predicted using a feed-forward neural network. Since all stages are built using smooth functions, the entire process of chemical perception and parameter assignment is differentiable end-to-end with respect to model parameters, allowing new force fields to be easily constructed, extended, and applied to arbitrary molecules. We show that this approach has the capacity to reproduce legacy atom types and can be fit to MM and QM energies and forces, among other targets

Quantifying configuration-sampling error in Langevin simulations of complex molecular systems

While Langevin integrators are popular in the study of equilibrium properties of complex systems, it is challenging to estimate the timestep-induced discretization error: the degree to which the sampled phase-space or configuration-space probability density departs from the desired target density due to the use of a finite integration timestep. Sivak et al., introduced a convenient approach to approximating a natural measure of error between the sampled density and the target equilibrium density, the Kullback-Leibler (KL) divergence, in phase space, but did not specifically address the issue of configuration-space properties, which are much more commonly of interest in molecular simulations. Here, we introduce a variant of this near-equilibrium estimator capable of measuring the error in the configuration-space marginal density, validating it against a complex but exact nested Monte Carlo estimator to show that it reproduces the KL divergence with high fidelity. To illustrate its utility, we employ this new near-equilibrium estimator to assess a claim that a recently proposed Langevin integrator introduces extremely small configuration-space density errors up to the stability limit at no extra computational expense. Finally, we show how this approach to quantifying sampling bias can be applied to a wide variety of stochastic integrators by following a straightforward procedure to compute the appropriate shadow work, and describe how it can be extended to quantify the error in arbitrary marginal or conditional distributions of interest

choderalab/espaloma: Version 0.3.2 - Support Latest DGL Version

Note This release updates espaloma to support the newest version (1.1.2) of DGL. There are no API breaking changes or updates to the espaloma model. We have attached espaloma-0.3.2.pt to this release for convince, but this model is identical to espaloma-0.3.1.pt. espaloma-latest.pt is also the same as espaloma-0.3.1.pt. Please see https://github.com/choderalab/espaloma/releases/tag/0.3.1 for more information about the espaloma 0.3.x family of models. What's Changed Support latest (1.1.2) DGL version Bug fixes, packaging, and documentation updates Installation Guide Update by @mikemhenry in https://github.com/choderalab/espaloma/pull/175 Add docker and apptainer image building on demand by @mikemhenry in https://github.com/choderalab/espaloma/pull/176 Update to new dgl version (1.1.1) by @mikemhenry in https://github.com/choderalab/espaloma/pull/179 Add Instructions on loading local model by @ijpulidos in https://github.com/choderalab/espaloma/pull/183 Test & Update to newest DGL Version (1.1.2) by @mikemhenry in https://github.com/choderalab/espaloma/pull/186 Update readme and docs for version 0.3.2 @mikemhenry in https://github.com/choderalab/espaloma/pull/188 Full Changelog: https://github.com/choderalab/espaloma/compare/0.3.1...0.3.

choderalab/openmmtools: Release 0.8.3 - minor feature update

This release extends some of the systems in openmmtools.testsystems to allow multiple CustomGBForce-based GB models supported by OpenMM's customgbforces.py functionality to be used. TolueneImplicit, HostGuestImplicit, and LysozymeImplicit now accept the implicitSolvent option, allowing GB models from simtk.openmm.app (such as HCT, OBC1, OBC2, GBn, and GBn2) to be optionally specified. OBC1 remains the default. These three classes also support any kwargs supported by AmberPrmtopFile.createSystem(). Any parameters that createSystem supports will be passed along from the constructor. TolueneImplicit and HostGuestImplicit also have variants like TolueneImplicitHCT, TolueneImplicitOBC1, etc. for all OpenMM-supported implicit solvent models

choderalab/openmmtools: Bugfix release 0.9.1

This release fixes a Python 2 bug caused by how Python 2 and Python 3 handle differently user-defined equality operators. This was affecting the sanity checks in alchemy.AlchemicalState to always fail when checking the compatibility of a System

Toward Learned Chemical Perception of Force Field Typing Rules

Molecular mechanics force fields define how the energy and forces of a molecular system are computed from its atomic positions, and enable the study of such systems through computational methods like molecular dynamics and Monte Carlo simulations. Despite progress toward automated force field parameterization, considerable human expertise is required to develop or extend force fields. In particular, human input has long been required to define atom types, which encode chemically unique environments that determine which parameters must be assigned. However, relying on humans to establish atom types is suboptimal: the resulting atom types are often unjustified from a statistical perspective, leading to over- or under-fitting; they are difficult to extend in a systematic and consistent manner when new chemistries must be modeled or new data becomes available; and human effort is not scalable when force fields must be generated for new (bio)polymers or materials. We aim to replace human specification of atom types with an automated approach, based on solid statistics and driven by experimental and/or quantum chemical reference data. Here, we describe a novel technology for this purpose, termed SMARTY, which generalizes atom typing by using direct chemical perception with SMARTS strings, and adopting a hierarchical approach to type assignment. The SMARTY technology enables creation of a move set in atom-typing space that can be used in a Monte Carlo optimization approach to atom typing. We demonstrate the power of this approach with a fully automated procedure that is able to re-discover human-defined atom types in the traditional small molecule force field parm99/parm@Frosst. Furthermore, we show how an extension of this approach that makes use of SMIRKS strings to match multiple atoms, which we term SMIRKY, allows us to take full advantage of the advances in direct chemical perception for valence types (bonds, angles, and torsions) afforded by the recently-proposed SMIRNOFF direct chemical perception force field typing language. We assess these approaches using several molecular datasets, including one which covers a diverse molecular subset from DrugBank. </div

Binding Modes of Ligands Using Enhanced Sampling (BLUES): Rapid Decorrelation of Ligand Binding Modes Using Nonequilibrium Candidate Monte Carlo

Accurately predicting protein-ligand binding is a major goal in computational chemistry, but even the prediction of ligand binding modes in proteins poses major challenges. Here, we focus on solving the binding mode prediction problem for rigid fragments. That is, we focus on computing the dominant placement, conformation, and orientations of a relatively rigid, fragment-like ligand in a receptor, and the populations of the multiple binding modes which may be relevant. This problem is important in its own right, but is even more timely given the recent success of alchemical free energy calculations. Alchemical calculations are increasingly used to predict binding free energies of ligands to receptors. However, the accuracy of these calculations is dependent on proper sampling of the relevant ligand binding modes. Unfortunately, ligand binding modes may often be uncertain, hard to predict, and/or slow to interconvert on simulation timescales, so proper sampling with current techniques can require prohibitively long simulations. We need new methods which dramatically improve sampling of ligand binding modes. Here, we develop and apply a nonequilibrium candidate Monte Carlo (NCMC) method to improve sampling of ligand binding modes.In this technique the ligand is rotated and subsequently allowed to relax in its new position through alchemical perturbation before accepting or rejecting the rotation and relaxation as a nonequilibrium Monte Carlo move. When applied to a T4 lysozyme model binding system, this NCMC method shows over two orders of magnitude improvement in binding mode sampling efficiency compared to a brute force molecular dynamics simulation. This is a first step towards applying this methodology to pharmaceutically relevant binding of fragments and, eventually, drug-like molecules. We are making this approach available via our new Binding Modes of Ligands using Enhanced Sampling (BLUES) package which is freely available on GitHub.</div

Biomolecular Simulations under Realistic Macroscopic Salt Conditions

Biomolecular simulations are typically performed in an aqueous environment where the number of ions remains fixed for the duration of the simulation, generally with either a minimally neutralizing ion environment or a number of salt pairs intended to match the macroscopic salt concentration. In contrast, real biomolecules experience local ion environments where the salt concentration is dynamic and may differ from bulk. The degree of salt concentration variability and average deviation from the macroscopic concentration remains, as yet, unknown. Here, we describe the theory and implementation of a Monte Carlo <i>osmostat</i> that can be added to explicit solvent molecular dynamics or Monte Carlo simulations to sample from a semigrand canonical ensemble in which the number of salt pairs fluctuates dynamically during the simulation. The osmostat reproduces the correct equilibrium statistics for a simulation volume that can exchange ions with a large reservoir at a defined macroscopic salt concentration. To achieve useful Monte Carlo acceptance rates, the method makes use of nonequilibrium candidate Monte Carlo (NCMC) moves in which monovalent ions and water molecules are alchemically transmuted using short nonequilibrium trajectories, with a modified Metropolis-Hastings criterion ensuring correct equilibrium statistics for an (<i>Δμ</i>, <i>N</i>, <i>p</i>, <i>T</i>) ensemble to achieve a ∼10⁴⁶× boost in acceptance rates. We demonstrate how typical protein (DHFR and the tyrosine kinase Src) and nucleic acid (Drew–Dickerson B-DNA dodecamer) systems exhibit salt concentration distributions that significantly differ from fixed-salt bulk simulations and display fluctuations that are on the same order of magnitude as the average

choderalab/openmmtools: 0.13.1 - Bugfix release

In this release: Fix pickling of CompoundThermodynamicState (#284). Add missing term to OBC2 GB alchemical Force (#288). Generalize forcefactories.restrain_atoms() to non-protein receptors (#290). Standardize integrator global variables in ContextCache (#291)