arXiv subjects

Narjes Ansari

Publications and source records attributed to Narjes Ansari.

8 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↗

arXiv2022

Water regulates the residence time of Benzamidine in Trypsin

The process of ligand-protein unbinding is crucial in biophysics. Water is an essential part of any biological system and yet, many aspects of its role remain elusive. Here, we simulate with state-of-the-art enhanced sampling techniques the binding of Benzamidine to Trypsin which is a much studied and paradigmatic ligand-protein system. We use machine learning methods to determine efficient collective coordinates for the complex non-local network of water. These coordinates are used to perform On-the-fly Probability Enhanced Sampling simulations, which we adapt to calculate also the ligand residence time. Our results, both static and dynamic, are in good agreement with experiments. We find that the presence of a water molecule located at the bottom of the binding pocket allows via a network of hydrogen bonds the ligand to be released into the solution. On a finer scale, even when unbinding is allowed, another water molecule further modulates the exit time.

physics.bio-ph↗

arXiv2022

Rare Event Kinetics from Adaptive Bias Enhanced Sampling

We introduce a novel enhanced sampling approach named OPES flooding for calculating the kinetics of rare events from atomistic molecular dynamics simulation. This method is derived from the On-the-fly-Probability-Enhanced-Sampling (OPES) approach [Invernizzi and Parrinello, JPC Lett. 2020], which has been recently developed for calculating converged free energy surfaces for complex systems. In this paper, we describe the theoretical details of the OPES flooding technique and demonstrate the application on three systems of increasing complexity: barrier crossing in a two-dimensional double well potential, conformational transition in the alanine dipeptide in gas phase, and the folding and unfolding of the chignolin polypeptide in aqueous environment. From extensive tests, we show that the calculation of accurate kinetics not only requires the transition state to be bias-free, but the amount of bias deposited should also not exceed the effective barrier height measured along the chosen collective variables. In this vein, the possibility of computing rates from biasing suboptimal order parameters has also been explored. Furthermore, we describe the choice of optimum parameter combinations for obtaining accurate results from limited computational effort.

physics.chem-ph↗

arXiv2022

Charge gradients around dendritic voids cause nanoscale inhomogeneities in liquid water

Water is the matrix of life and is generally considered a homogeneous, uniform, liquid. Recent experiments report that water is instead a two-state liquid. However, subsequently, these findings were contested. The structure of water and whether we should think of it as uniform therefore remains an open question. Here, we report femtosecond elastic second harmonic scattering (fs-ESHS) of liquid water in comparison to an isotropic liquid (CCl4) and show that water is indeed a non-uniform liquid, The coherent fs-ESHS intensity was interpreted, using molecular dynamics simulations, as arising from charge density fluctuations and consequentially enhanced nanoscale polarizabilities around transient voids having an average lifetime of 300 fs. Although voids were also present in CCl4, they were not characterized by hydrogen bond defects and did not show strong polarizability fluctuations, leading to fs-ESHS of an isotropic liquid. The voids increased in number at higher temperatures above room temperature, in agreement with the fs-ESHS results. The measured nanoscale-femtosecond inhomogeneities therefore do not necessarily relate to the proposed two state model of water, but instead underscore the elusive nature of liquid water, and undoubtedly have an impact on any type of transition that occurs in liquid water.

physics.chem-ph↗

arXiv2021

The role of water in host-guest interaction

One of the main applications of atomistic computer simulations is the calculation of ligand binding energies. The accuracy of these calculations depends on the force field quality and on the thoroughness of configuration sampling. Sampling is an obstacle in modern simulations due to the frequent appearance of kinetic bottlenecks in the free energy landscape. Very often this difficulty is circumvented by enhanced sampling techniques. Typically, these techniques depend on the introduction of appropriate collective variables that are meant to capture the system's degrees of freedom. In ligand binding, water has long been known to play a key role, but its complex behaviour has proven difficult to fully capture. In this paper we combine machine learning with physical intuition to build a non-local and highly efficient water-describing collective variable. We use it to study a set of of host-guest systems from the SAMPL5 challenge. We obtain highly accurate binding energies and good agreement with experiments. The role of water during the binding process is then analysed in some detail.

physics.comp-ph↗

arXiv2020

Molecular Mechanism of Gas Solubility in Liquid: Constant Chemical Potential Molecular Dynamics Simulations

Accurate prediction of a gas solubility in a liquid is crucial in many areas of chemistry, and a detailed understanding of the molecular mechanism of the gas solvation continues to be an active area of research. Here, we extend the idea of constant chemical potential molecular dynamics (CμMD) approach to the calculation of the gas solubility in the liquid under constant gas chemical potential conditions. As a representative example, we utilize this method to calculate the isothermal solubility of carbon dioxide in water. Additionally, we provide microscopic insight into the mechanism of solvation that preferentially occurs in areas of the surface where the hydrogen network is broken.

cond-mat.soft↗