92 citations · 521 across the 27 of their papers we have counts for
28 papers
Fast Monte Carlo calculation of multiple scattering in resonant neutron capture
R. M. Djilkibaev
A fast Monte Carlo code has been developed to calculate multiple neutron scattering in a sample. Good agreement with an independent Monte Carlo calculation using SAMMY is obtained…
The Convergence Frontier: Integrating Machine Learning and High Performance Quantum Computing for Next-Generation Drug Discovery
Narjes Ansari, César Feniou, Nicolaï Gouraud +13
Integrating quantum mechanics into drug discovery marks a decisive shift from empirical trial-and-error toward quantitative precision. However, the prohibitive cost of ab initio mo…
Faster Molecular Dynamics with Neural Network Potentials via Distilled Multiple Time-Stepping and Non-Conservative Forces
Nicolaï Gouraud, Côme Cattin, Thomas Plé +3
Following our previous work (J. Phys. Chem. Lett., 2026, 17, 5, 1288-1295), we propose the DMTS-NC approach, a distilled multi-time-step (DMTS) strategy using non-conservative (NC)…
Dual-LAO for calculating fast and robust relative binding free energies of simple and complex transformations
Narjes Ansari, Félix Aviat, Jérôme Hénin +2
Relative Binding Free Energy (RBFE) calculations are a cornerstone of rational hit-to-lead and lead optimization in modern drug discovery. However, the high computational cost and…
Accelerating Molecular Dynamics Simulations with Foundation Neural Network Models using Multiple Time-Step and Distillation
Côme Cattin, Thomas Plé, Olivier Adjoua +3
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 wh…
Pushing the Accuracy Limit of Foundation Neural Network Models with Quantum Monte Carlo Forces and Path Integrals
Anouar Benali, Thomas Plé, Olivier Adjoua +18
We propose an end-to-end integrated strategy to produce highly accurate quantum chemistry (QC) synthetic datasets (energies and forces) aimed at deriving Foundation Machine Learnin…
Lambda-ABF-OPES: Faster Convergence with High Accuracy in Alchemical Free Energy Calculations
Narjes Ansari, Zhifeng Francis Jing, Antoine Gagelin +5
Predicting the binding affinity between small molecules and target macromolecules while combining both speed and accuracy, is a cornerstone of modern computational drug discovery w…
The Q-AMOEBA (CF) Polarizable Potential
Nastasia Mauger, Thomas Plé, Louis Lagardère +2
We present Q-AMOEBA (CF), an enhanced version of the Q-AMOEBA polarizable model that integrates a geometry-dependent charge flux (CF) term while designed for an explicit treatment…
Velocity Jumps for Molecular Dynamics
Nicolaï Gouraud, Louis Lagardère, Olivier Adjoua +3
We introduce the Velocity Jumps approach, denoted as JUMP, a new class of Molecular dynamics integrators, replacing the Langevin dynamics by a hybrid model combining a classical La…
FeNNol: an Efficient and Flexible Library for Building Force-field-enhanced Neural Network Potentials
Thomas Plé, Olivier Adjoua, Louis Lagardère +1
Neural network interatomic potentials (NNPs) have recently proven to be powerful tools to accurately model complex molecular systems while bypassing the high numerical cost of ab-i…
Lambda-ABF: Simplified, Portable, Accurate and Cost-effective Alchemical Free Energy Computations
Louis Lagardère, Lise Maurin, Olivier Adjoua +4
We introduce an efficient and robust method to compute alchemical free energy differences, resulting from the application of multiple walker Adaptive Biasing Force (ABF) in conjunc…
Smooth Particle Mesh Ewald-integrated stochastic Lanczos Many-body Dispersion algorithm
Pier P. Poier, Louis Lagardère, Jean-Philip Piquemal
We derive and implement an alternative formulation of the Stochastic Lanczos algorithm to be employed in connection with the Many-Body Dispersion model (MBD). Indeed, this formulat…
Force-Field-Enhanced Neural Network Interactions: from Local Equivariant Embedding to Atom-in-Molecule properties and long-range effects
Thomas Plé, Louis Lagardère, Jean-Philip Piquemal
We introduce FENNIX (Force-Field-Enhanced Neural Network InteraXions), a hybrid approach between machine-learning and force-fields. We leverage state-of-the-art equivariant neural…
ANKH: A Generalized O(N) Interpolated Ewald Strategy for Molecular Dynamics Simulations
Igor Chollet, Louis Lagardère, Jean-Philip Piquemal
To evaluate electrostatics interactions, Molecular dynamics (MD) simulations rely on Particle Mesh Ewald (PME), an O(Nlog(N)) algorithm that uses Fast Fourier Transforms (FFTs) or,…
Routine Molecular Dynamics Simulations Including Nuclear Quantum Effects: from Force Fields to Machine Learning Potentials
Thomas Plé, Nastasia Mauger, Olivier Adjoua +4
We report the implementation of a multi-CPU and multi-GPU massively parallel platform dedicated to the explicit inclusion of nuclear quantum effects (NQEs) in the Tinker-HP molecul…
Generalized Many-Body Dispersion Correction through Random-phase Approximation for Chemically Accurate Density Functional Theory
Pier Paolo Poier, Louis Lagardère, Jean-Philip Piquemal
We extend our recently proposed Deep Learning-aided many-body dispersion (DNN-MBD) model to quadrupole polarizability (Q) terms using a generalized Random Phase Approximation (RPA)…
Scalable Hybrid Deep Neural Networks/Polarizable Potentials Biomolecular Simulations including long-range effects
Théo Jaffrelot Inizan, Thomas Plé, Olivier Adjoua +5
Deep-HP is a scalable extension of the \TinkerHP\ multi-GPUs molecular dynamics (MD) package enabling the use of Pytorch/TensorFlow Deep Neural Networks (DNNs) models. Deep-HP incr…
Improving Condensed Phase Water Dynamics with Explicit Nuclear Quantum Effects: the Polarizable Q-AMOEBA Force Field
Nastasia Mauger, Thomas Plé, Louis Lagardère +2
We introduce a new parametrization of the AMOEBA polarizable force field for water denoted Q-AMOEBA, for use in simulations that explicitly account for nuclear quantum effects (NQE…
Targeting the Major Groove of the Palindromic d(GGCGCC)2 Sequence by Oligopeptide Derivatives of Anthraquinone Intercalators
Krystel El Hage, Giovanni Ribaudo, Louis Lagardère +6
GC-rich sequences are recurring motifs in oncogenes and retroviruses, and could be targeted by non-covalent major-groove therapeutic ligands. We considered the palindromic sequence…
Accurate Deep Learning-aided Density-free Strategy for Many-Body Dispersion-corrected Density Functional Theory
Pier Paolo Poier, Théo Jaffrelot Inizan, Olivier Adjoua +2
Using a Deep Neuronal Network model (DNN) trained on the large ANI-1 data set of small organic molecules, we propose a transferable density-free many-body dispersion model (DNN-MBD…
Development of the Quantum Inspired SIBFA Many-Body Polarizable Force Field: Enabling Condensed Phase Molecular Dynamics Simulations
Sehr Naseem-Khan, Louis Lagardère, Christophe Narth +4
We present the extension of the SIBFA (Sum of Interactions Between Fragments Ab initio Computed many-body polarizable force field to condensed phase Molecular Dynamics (MD) simulat…
Computationally driven discovery of SARS-CoV-2 Mpro inhibitors: from design to experimental validation
L. El Khoury, Z. Jing, A. Cuzzolin +32
We report a fast-track computationally-driven discovery of new SARS-CoV2 Main Protease (M) inhibitors whose potency range from mM for initial non-covalent ligands to sub-$μ…
Nuclear Quantum Effects in liquid water at near classical computational cost using the adaptive Quantum Thermal Bath
Nastasia Mauger, Thomas Plé, Louis Lagardère +4
We demonstrate the accuracy and efficiency of a recently introduced approach to account for nuclear quantum effects (NQE) in molecular simulations: the adaptive Quantum Thermal Bat…
Tinker-HP : Accelerating Molecular Dynamics Simulations of Large Complex Systems with Advanced Point Dipole Polarizable Force Fields using GPUs and Multi-GPUs systems
Olivier Adjoua, Louis Lagardère, Luc-Henri Jolly +9
We present the extension of the Tinker-HP package (Lagardère et al., Chem. Sci., 2018,9, 956-972) to the use of Graphics Processing Unit (GPU) cards to accelerate molecular dynamic…