arXiv subjects

Félix Aviat

Publications and source records attributed to Félix Aviat.

4 recordsLinked to original sources

arXiv2026

Dual-LAO for calculating fast and robust relative binding free energies of simple and complex transformations

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 limited reliability in tackling large or complex molecular transformations often prevent their routine, high-throughput use. Here we introduce dual-LAO, a novel, highly efficient method for calculating RBFE. Building on the Lambda-ABF-OPES framework, this method combines a dual-topology setup and suitable restraints to dramatically accelerate free energy convergence. We demonstrate that dual-LAO, in combination with the AMOEBA polarizable force field, achieves an unprecedented acceleration factor of 15 to 30 times compared to current state-of-the-art methods on standard drug targets. Crucially, the approach maintains high accuracy and successfully tackles previously prohibitive molecular changes, including scaffold-hopping, buried water displacement, charge changes, ring-opening, and binding pose perturbations. This significant leap in efficiency allows for the widespread, routine integration of predictive molecular simulations into the rapid optimization cycles of drug discovery, enabling chemists to confidently model historically challenging systems in timescales compatible with real-world project deadlines.

physics.chem-ph↗

arXiv2026

The Convergence Frontier: Integrating Machine Learning and High Performance Quantum Computing for Next-Generation Drug Discovery

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 molecular dynamics has historically forced a compromise between chemical accuracy and computational scalability. This paper identifies the convergence of High-Performance Computing (HPC), Machine Learning (ML), and Quantum Computing (QC) as the definitive solution to this bottleneck. While ML foundation models, such as FeNNix-Bio1, enable quantum-accurate simulations, they remain tethered to the inherent limits of classical data generation. We detail how High-Performance Quantum Computing (HPQC), utilizing hybrid QPU-GPU architectures, will serve as the ultimate accelerator for quantum chemistry data. By leveraging Hilbert space mapping, these systems can achieve true chemical accuracy while bypassing the heuristics of classical approximations. We show how this tripartite convergence optimizes the drug discovery pipeline, spanning from initial system preparation to ML-driven, high-fidelity simulations. Finally, we position quantum-enhanced sampling as the beyond GPU frontier for modeling reactive cellular systems and pioneering next-generation materials.

quant-ph↗

arXiv2025

Lambda-ABF-OPES: Faster Convergence with High Accuracy in Alchemical Free Energy Calculations

Predicting the binding affinity between small molecules and target macromolecules while combining both speed and accuracy, is a cornerstone of modern computational drug discovery which is critical for accelerating therapeutic development. Despite recent progresses in molecular dynamics (MD) simulations, such as advanced polarizable force fields and enhanced-sampling techniques, estimating absolute binding free energies (ABFE) remains computationally challenging. To overcome these difficulties, we introduce a highly efficient, hybrid methodology that couples the Lambda-Adaptive Biasing Force (Lambda-ABF) scheme with On-the-fly Probability Enhanced Sampling (OPES). This approach achieves up to a nine-fold improvement in sampling efficiency and computational speed compared to the original Lambda-ABF when used in conjunction with the AMOEBA polarizable force field, yielding converged results at a fraction of the cost of standard techniques.

physics.chem-ph↗

arXiv2017

The Truncated Conjugate Gradient (TCG), a Non-iterative/Fixed-cost Strategy for Computing Polarization in Molecular Dynamics: Fast Evaluation of Analytical Forces

In a recent paper (J. Chem. Theory. Comput., 2017, 13, 180-190) we proposed the Truncated Conjugate Gradient (TCG) approach to compute the polarization energy and forces in polarizable molecular simulations. The method consists in truncating the Conjugate Gradient algorithm at a fixed predetermined order leading to a fixed computational cost and can thus be considered "non-iterative". This gives the possibility to derive analytical forces avoiding the usual energy conservation (i.e. drifts) issues occurring with iterative approaches. A key point concerns the evaluation of the 1

physics.chem-ph↗