A new strategy using Lie-Algebra generator pools enables efficient Variational Quantum Eigensolvers for complex quantum chemistry simulations.

Scaling Down to Speed Up: Lie Algebra Pools for Variational Eigensolvers

Szegedy’s quantum walk, offering a potentially quadratic acceleration to Metropolis-Hastings simulations, now benefits from a novel implementation.

Quantum circuits for the Metropolis-Hastings algorithm, Claudon, Baptiste, Rodenas Ruiz, Pablo, Piquemal, Jean-Philip, Monmarché, Pierre

The International Society of Quantum Biology and Pharmacology The ISQBP is a society founded in 1970 to provide a forum for chemists, pharmacologists, and biologists to discuss and extend the impact...

breaking barriers with the computational microscope

High-performance molecular visualization software. Contribute to VTX-Molecular-Visualization/VTX development by creating an account on GitHub.

An analysis of 97 million citations has found that rates of fabricated citations have climbed steeply since 2023.

Communications Chemistry, Published online: 08 May 2026; doi:10.1038/s42004-026-02022-6Relative Binding Free Energy (RBFE) calculations are essential for drug discovery but are often hindered by high computational costs and limited reliability for complex transformations. Here, the authors introduce the Dual-LAO framework integrating Lambda-ABF-OPES, dual topology, and dual-DBC restraints, that accelerates RBFE calculations by 15 to 30 times, maintains high accuracy and tackles previously prohibitive molecular changes.

Relative Binding Free Energy (RBFE) calculations are essential for drug discovery but are often hindered by high computational costs and limited reliability for complex transformations. Here, the auth...

The ERC Scientific Council has listened to the concerns from members of the research community about the recent announcement on changes to the re-submission rules. The changes, intended to manage the ...

The ERC plans to launch the grant competitions under its 2027 Work Programme between July 2026 and June 2027, with the calls for proposals introducing several changes to the eligibility rules for appl...

Register for Telluride Science!

Quantum state preparation is a central primitive in many quantum algorithms, yet it is generally resource intensive, with efficient constructions known only for structured families of states. This wor...

For years, complex quantum algorithms have struggled to deliver dependable results on existing hardware due to inherent noise. Now, a quantum Markov Chain Monte Carlo algorithm has been successfully i...

Researchers simulated nearly every molecule in a bacterial cell — and then watched the cell grow and reproduce.

arXiv abstract link

The recently released Boltz-2 cofolding model is generating high expectations by enabling both protein–ligand structure and binding affinity predictions. When applied to a recently described and chall...

A string of surprising advances suggests usable quantum computers could be here in a decade.

We present a distilled multi-time-step (DMTS) strategy to accelerate molecular dynamics simulations using foundation neural network models. DMTS uses a dual-level neural network, where the target accurate potential is coupled to a simpler but faster model obtained via a distillation process. The 3.5 Å cutoff distilled model is sufficient to capture the fast-varying forces, i.e., mainly bonded interactions, from the accurate potential, allowing its use in a reversible reference system propagator algorithm (RESPA)-like formalism. The approach conserves accuracy, preserving both static and dynamic properties, while enabling us to evaluate the costly model only every 3 to 6 fs depending on the system. Consequently, large simulation speedups over standard 1 fs integration are observed: nearly 4-fold in homogeneous systems and 3-fold in large solvated proteins through leveraging active learning for enhanced stability. Such a strategy is applicable to any neural network potential and reduces the performance gap with classical force fields.

With so little money to go round, the costs of competing for grants can exceed what the grants are worth. When that happens, nobody wins.

Coarse-grained molecular dynamics simulations, such as those performed with the recently parametrized Martini 3 force field, simplify molecular models and enable the study of larger systems over longer time scales. With this new implementation, Martini 3 allows more bead types and sizes, becoming more amenable to studying dynamical phenomena involving small molecules such as protein–ligand interactions and membrane permeation. However, while solutions existed to automatically model small molecules using the previous iteration of the Martini force field, there is no simple way to generate such molecules for Martini 3 yet. Here, we introduce Auto-MartiniM3, an advanced and updated version of the Auto-Martini program designed to automate the coarse-graining of small molecules to be used with the Martini 3 force field. We validated our approach by modeling 81 simple molecules from the Martini Database and comparing their structural and thermodynamic properties with those obtained from models designed by Martini experts. Additionally, we assessed the behavior of Auto-MartiniM3-generated models by calculating solute translocation and free energy across lipid bilayers. We also evaluated more complex molecules such as caffeine by testing its binding to the adenosine A2A receptor. Finally, our results from deploying Auto-MartiniM3 on a large data set of molecular fragments demonstrate that this program can become a tool of choice for fast, high-throughput creation of coarse-grained models of small molecules, offering a good balance between automation and accuracy. Auto-MartiniM3 source code is freely available at https://github.com/Martini-Force-Field-Initiative/Automartini_M3.

Machine learning interatomic potentials (MLIPs) have become powerful tools to extend molecular simulations beyond the limits of quantum methods, offering near-quantum accuracy at much lower computatio...