Machine-learning interatomic potentials (MLIPs) trained by directly learning the total interatomic interaction energies can suffer from limited transferability,

Accelerating scientific discovery is among the most consequential applications of AI, and computational biomolecular simulation stands out as a particularly promising target within this broader effort...

Accelerating scientific discovery is among the most consequential applications of AI, and computational biomolecular simulation stands out as a particularly promising target within this broader effort...

Structural bioinformatics aims to answer biological questions by considering biomolecular structures at scale. This Perspective argues that now that we have accurate predictions, we need to ask what w...

Accurately measuring compound binding affinities is key to driving the pharmaceutical development process. Rigorous physics-based in silico approaches, particularly alchemical free energy methods, hav...

Force fields for molecular dynamics are usually developed manually, limiting their transferability and making systematic exploration of functional forms challenging. We developed a graph neural networ...

The increasing importance and predictive power of modern molecular modeling, driven by physics- and machine-learning-based methods, necessitates a new collaborative architecture to replace the isolate...

Molecular mechanics force fields enable atomistic simulations of complex systems that are too large for a quantum mechanical treatment. Simulation accuracy depends on the parameters employed in the fo...

This release adds openff-2.3.0.offxml and openff_unconstrained-2.3.0.offxml. Sage 2.3.0 is the first OpenFF force field to use the AshGC neural network charge model to assign charges. Both vdW para...

Accurately measuring compound binding affinities is key to driving the pharmaceutical development process. Rigorous physics-based in silico approaches, particularly alchemical free energy methods, hav...

We evaluate the feasibility of using co-folding models for synthetic data augmentation in training machine learning-based scoring functions (MLSFs) for binding affinity prediction. Our results show th...

Hybrid ML/MM approaches that combine machine learning (ML) potentials with molecular mechanics (MM) potentials offer a promising balance between computational cost and accuracy. Most ML/MM simulations reported to date employ mechanical embedding schemes, and rely on Lennard-Jones and Coulomb potentials to model intermolecular interactions between the ML and MM regions. A promising approach to improving ML/MM schemes is to use electrostatic embedding, where polarization effects on the ML region by the MM region are explicitly incorporated. The electrostatic machine learning embedding (EMLE) method has been developed for this purpose. Here, we compute absolute hydration free energies for a set of small organic molecules to derive robust methodologies for training EMLE models using quantum mechanical data. We establish protocols for fine-tuning the static and induced components of electrostatic interactions and evaluate the accuracy limits of fitting these components to first-principles calculations. We also introduce an empirical adjustment to enhance agreement with experimental results, strengthening the competitiveness of ML/MM simulations relative to state-of-the-art methods. Overall, our findings provide valuable insights into the challenges and opportunities of electrostatic embedding ML/MM simulations, and offer strategies for achieving robust modeling of classes of drug-like molecules where the accuracy of conventional MM force fields fall short.

New minor release of alchemlyb with fixes and enhancements. Supports Python 3.11 - 3.14. See CHANGES for details. What's Changed update action/checkout by @orbeckst in #417 Parallel read and prepro...

Many machine learning potentials (MLPs) rely on representations of the total energy in terms of the positions of the atoms in their local environment, using either a cutoff radius or a limited number ...

We evaluate the feasibility of using co-folding models for synthetic data augmentation in training machine learning-based scoring functions (MLSFs) for binding affinity prediction. Our results show th...

Following the sequence and structure revolutions, predicting functionally relevant protein structure changes at scale remains an outstanding challenge. We introduce BioEmu, a deep learning system that...

Free energies play a central role in characterising the behaviour of chemical systems and are among the most important quantities that can be calculated by molecular dynamics simulations. The free ene...

Alchemical free energy calculations are essential to modern structure-based drug design. Such calculations are usually performed at a series of discrete intermediates along a nonphysical thermodynamic...

EPSRC Postdoctoral Pathway Fellowship

The anaerobic glycyl radical enzyme choline trimethylamine-lyase (CutC) is produced by multiple bacterial species in the human gut microbiome and catalyzes the conversion of choline to trimethylamine ...

The bacterial mechanosensitive channel, MscL, opens in response to elevated membrane tension during osmotic shock. Some mutations, like L17A and V21A, can reduce the activation tension threshold, thus...

Classical empirical force fields have dominated biomolecular simulations for over 50 years. Although widely used in drug discovery, crystal structure prediction, and biomolecular dynamics, they generally lack the accuracy and transferability required for first-principles predictive modeling. In this paper, we introduce MACE-OFF, a series of short-range transferable force fields for organic molecules created using state-of-the-art machine learning technology and first-principles reference data computed with a high level of quantum mechanical theory. MACE-OFF demonstrates the remarkable capabilities of short-range models by accurately predicting a wide variety of gas- and condensed-phase properties of molecular systems. It produces accurate, easy-to-converge dihedral torsion scans of unseen molecules as well as reliable descriptions of molecular crystals and liquids, including quantum nuclear effects. We further demonstrate the capabilities of MACE-OFF by determining free energy surfaces in explicit solvent as well as the folding dynamics of peptides and nanosecond simulations of a fully solvated protein. These developments enable first-principles simulations of molecular systems for the broader chemistry community at high accuracy and relatively low computational cost.

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