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) ...

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.

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

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.

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

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

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

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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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...

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...

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.

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...

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...

Reactive potentials serve as essential tools for investigating chemical reactions with moderate computational costs. However, traditional reactive potentials often depend on fixed, semiempirical parameters, which limits their accuracy and transferability. Overcoming these limitations can significantly expand the applicability of reactive potentials, enabling the simulation of a broader range of reactions under diverse conditions and the prediction of reaction properties, such as barrier heights. This work introduces ANI-1xBB, a novel ANI-based reactive ML potential trained on off-equilibrium molecular conformers generated through an automated bond-breaking workflow. ANI-1xBB significantly enhances the prediction of reaction energetics, barrier heights, and bond dissociation energies, surpassing those of conventional ANI models. Our results show that ANI-1xBB improves transition state modeling and reaction pathway prediction while generalizing effectively to pericyclic reactions and radical-driven processes. Furthermore, the automated data generation strategy supports the efficient construction of large-scale, high-quality reactive data sets, reducing reliance on expensive QM calculations. This work highlights ANI-1xBB as a practical model for accelerating the development of reactive machine learning potentials, offering new opportunities for modeling reaction phenomena.

Successful grant writing takes careful thought as well as considerable skill. Experienced investigators appreciate just how much work and background development are required. However, those new to the...

The information provided on this page is a summary of the main rules and requirements for Postdoctoral Fellowships (PFs) and who can apply for them.

This lab adopted agile design thinking for their latest research project. Here's their advice for how you can transform your own lab work using scrum.

Scientific Reports - Comparing ANI-2x, ANI-1ccx neural networks, force field, and DFT methods for predicting conformational potential energy of organic molecules

Matrix metalloproteases (MMPs) comprise a family of enzymes that cleave protein substrates based on a conserved mechanism involving activation of an active site-bound water molecule by a Zn2+ ion. Alt...

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