mdpi.com

Development of Coarse-Grained Lipid Force Fields Based on a Graph Neural Network

Coarse-grained (CG) lipid models enable efficient simulations of large-scale membrane events. However, achieving both speed and atomic-level accuracy remains challenging. Graph neural networks (GNNs) ...

pubs.acs.org

Evaluation of the QCxMS2 Method for the Calculation of Collision-Induced Dissociation Spectra via Automated Reaction Network Exploration

Collision-induced dissociation mass spectrometry (CID-MS) is an important tool in analytical chemistry for the structural elucidation of unknown compounds. The theoretical prediction of the CID spectra plays a critical role in supporting and accelerating this process. To this end, we adapt the recently developed QCxMS2 program originally designed for the calculation of electron ionization (EI) spectra to enable the computation of CID-MS. To account for the fragmentation conditions characteristic of CID within the automated reaction network discovery approach of QCxMS2 we adapted the internal energy distribution to match the experimental conditions. This distribution can be adjusted via a single parameter to approximate various activation settings, thereby eliminating the need for explicit simulations of the collisional process. We evaluate our approach on a test set of 13 organic molecules with diverse functional groups, compiled specifically for this study. All reference spectra were recorded consistently under the same measurement conditions, including both CID and higher-energy collisional dissociation (HCD) modes. Overall, QCxMS2 achieves a good average entropy similarity score (ESS) of 0.687 for the HCD spectra and 0.773 for the CID spectra. The direct comparison to experimental data demonstrates that the QCxMS2 approach, even without explicit modeling of collisions, is generally capable of computing both CID and HCD spectra with reasonable accuracy and robustness. This highlights its potential as a valuable tool for integration into structure elucidation workflows in analytical mass spectrometry.

doi.org

The Arch from the Stones: Understanding Protein Folding Energy Landscapes via Bioinspired Collective Variables

Protein folding remains a formidable challenge despite significant advances, particularly in sequence-to-structure prediction. Accurately capturing thermodynamics and intermediates via simulations dem...

pubs.acs.org

Martini 3 Coarse-Grained Models for Carbon Nanomaterials

The Martini model is a coarse-grained force field allowing simulations of biomolecular systems as well as a range of materials including different types of nanomaterials of technological interest. Recently, a new version of the force field (version 3) has been released that includes new parameters for lipids, proteins, carbohydrates, and a number of small molecules, but not yet carbon nanomaterials. Here, we present new Martini models for three major types of carbon nanomaterials: fullerene, carbon nanotubes, and graphene. The new models were parametrized within the Martini 3 framework, and reproduce semiquantitatively a range of properties for each material. In particular, the model of fullerene yields excellent solid-state properties and good properties in solution, including correct trends in partitioning between different solvents and realistic translocation across lipid membranes. The models of carbon nanotubes reproduce the atomistic behavior of nanotube porins spanning lipid bilayers. The model of graphene reproduces structural and elastic properties, as well as trends in experimental adsorption enthalpies of organic molecules. All new models can be used in large-scale simulations to study the interaction with the wide variety of molecules already available in the Martini 3 force field, including biomolecular and synthetic systems.

pubs.acs.org

acnash - Overview

Senior Computational Biophysicist and Chemist, Software Engineer, and Medical Statistician. I build novel chemical software to solve protein-disease models. - acnash

github.com

Methodological developments in electronic structure theory and chemical dynamics

This Collection aims to highlight research that advances our understanding of electronic structure and chemical dynamics, as well as the application of ...

nature.com

On the failure of rescoring in virtual screening

Why automated virtual screening and docking remains hard and why expertise remains essential

medchemash.substack.com

MACE-OFF: Short-Range Transferable Machine Learning Force Fields for Organic Molecules

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.

pubs.acs.org

facebook/OMol25 · Hugging Face

We’re on a journey to advance and democratize artificial intelligence through open source and open science.

huggingface.co

Ten quick tips to perform meaningful and reproducible molecular docking calculations

Author summary The ten quick tips presented here are aimed at understanding the drug target thoroughly and performing molecular docking to ensure maximum precision and biological relevance. The emphas...

journals.plos.org

A guide to machine learning for biologists - Nature Reviews Molecular Cell Biology

Machine learning is becoming a widely used tool for the analysis of biological data. However, for experimentalists, proper use of machine learning methods can be challenging. This Review provides an o...

nature.com

Variety name origins

dahliaworld.co.uk

Qubit Pharmaceuticals. How AI-Powered Quantum Chemistry Is Transforming Molecular Simulations

FeNNix-Bio1, an AI model integrating quantum principles, revolutionizes molecular simulations for drug design by offering rapid, precise predictions at scale, reducing experimentation costs and expanding applications in materials science.

quantumzeitgeist.com

Machine Learning for Toxicity Prediction Using Chemical Structures: Pillars for Success in the Real World

Machine learning (ML) is increasingly valuable for predicting molecular properties and toxicity in drug discovery. However, toxicity-related end points have always been challenging to evaluate experim...

pubs.acs.org

OpenMM 8.3 beta · openmm openmm · Discussion #4926

A beta of OpenMM 8.3 is now available. Please try it out and let us know how it works for you! You can install it with mamba/conda with the command mamba install -c conda-forge/label/openmm_rc -c c...

github.com