arXiv subjects

Dechin Chen

Publications and source records attributed to Dechin Chen.

7 recordsLinked to original sources

arXiv2026

A Massively Scalable Ligand-Protein Dissociation Dynamic Database Derived from Atomistic Molecular Modelling

Understanding the kinetics of drug-protein interactions is paramount for drug design, yet the field lacks large-scale, dynamic data to move beyond static structural analysis. Here, we present DD-03B, a massively scalable database providing dynamic, all-atom dissociation trajectories for a broad set of ligand-protein complexes. Utilising and extending a validated computational pipeline, we generated dissociation trajectories for 19,037 ligand-protein complexes sourced from PDBbind+v2020R1, resulting in a repository of approximately 0.3 billion simulation frames totalling 40 TB in size. For these systems-which possess experimental binding affinities (kd) but typically lack measured koff rates-we computed and assigned dissociation rate constants through trajectory reweighting. Our analysis reveals that protein-ligand complexes can be categorised into three mechanistic types (pathway-dominant, open-pocket, and entropy-pocket systems), each requiring distinct strategies for accurate kinetic characterisation. Together with our previously released DD-13M, DD-03B forms the core of the expandable Dissociation Dynamic Database (DDD) project, which will be continuously augmented with new trajectories. This large-scale, publicly available resource establishes a critical foundation for training and benchmarking next-generation generative AI models to predict and optimise drug-protein dissociation kinetics.

physics.comp-ph↗

arXiv2025

Great Restraining Wall in Multidimensional Collective Variable Space

Enhanced sampling methods are pivotal for exploring rare events in molecular dynamics (MD), yet face challenges in high-dimensional collective variable (CV) spaces where exhaustive sampling becomes computationally prohibitive. While techniques like metadynamics (MetaD) and path-CV enable targeted free energy surface (FES) reconstruction, they often struggle with confinement stability, hyperparameter sensitivity, and geometric flexibility. This work introduces the Great Restraining Wall (GW) method, a robust framework for efficient FES sampling within predefined CV subspaces, addressing these limitations through a novel kernel density estimation (KDE)-derived restraining potential. GW operates by constructing a bias potential that confines sampling to user defined regions ranging from multidimensional masks to 1D pathways via asymptotically half-harmonic barriers. Unlike MetaD variants requiring iterative bias deposition, GW potential is derived from a cumulative distribution function, ensuring confinement without manual hyperparameter tuning. GW provides a versatile, stable, and efficient framework for targeted FES sampling, particularly beneficial for complex biomolecular systems with intricate CV landscapes. Its integration with existing enhanced sampling protocols opens avenues for studying ligand binding, conformational transitions, and other rare events with unprecedented precision. Future work will explore GW extension to adaptive regions and machine learning-guided CV discovery.

physics.comp-ph↗

arXiv2025

A Novel 4-D Dataset Paradigm for Studying Complete Ligand-Protein Dissociation Dynamics

The kinetics and dynamics of drug-protein binding and dissociation are crucial to understanding drug absorption and metabolism. Despite advances in artificial intelligence (AI) tools for drug-protein interaction studies, existing training datasets remain limited to static structures or quasi-static conformations. This paper proposes a novel computational approach for rapidly generating drug-protein dissociation trajectories and presents the inaugural dynamically time-resolved 4-D (t, x, y, z) trajectory database DD-13M. This dataset captures over 26,000 complete dissociation processes for 565 ligand-protein complexes, providing nearly 13 million frames of all-atom simulation trajectories. A deep equivariant generative model, UnbindingFlow, was trained using the DD-13M dataset. This model has the capacity to produce dissociation trajectories for novel targets whilst accurately predicting their rate constants (koff). DD-13M introduces a new type of training dataset for AI models, establishing a de novo paradigm for studying the dynamics of drug-protein interactions.

physics.comp-ph↗

arXiv2025

Performing Path Integral Molecular Dynamics Using Artificial Intelligence Enhanced Molecular Simulation Framework

This study employed an artificial intelligence-enhanced molecular simulation framework to enable efficient Path Integral Molecular Dynamics (PIMD) simulations. Owing to its modular architecture and high-throughput capabilities, the framework effectively mitigates the computational complexity and resource-intensive limitations associated with conventional PIMD approaches. By integrating machine learning force fields (MLFFs) into the framework, we rigorously tested its performance through two representative cases: a small-molecule reaction system (double proton transfer in formic acid dimer) and a bulk-phase transition system (water-ice phase transformation). Computational results demonstrate that the proposed framework achieves accelerated PIMD simulations while preserving quantum mechanical accuracy. These findings show that nuclear quantum effects can be captured for complex molecular systems, using relatively low computational cost.

physics.chem-ph↗

arXiv2024

A Sinking Approach to Explore Arbitrary Areas in Free Energy Landscapes

To address the time-scale limitations in molecular dynamics (MD) simulations, numerous enhanced sampling methods have been developed to expedite the exploration of complex free energy landscapes. A commonly employed approach accelerates the sampling of degrees of freedom associated with pre-defined collective variables (CVs), which typically tends to traverse the entire CV range. However, in many scenarios, the focus of interest is on specific regions within the CV space. This paper introduces a novel "sinking" approach that enables enhanced sampling of arbitrary areas within the CV space. We begin by proposing a gridded convolutional approximation that productively replicates the effects of metadynamics, a powerful CV-based enhanced sampling technique. Building on this, we present the SinkMeta method, which "sinks" the interior bias potential to create restraining potential "cliffs" at the grid edges. This technique can confine the exploration of CVs in MD simulations to a preset area. Our experimental results demonstrate that SinkMeta requires minimal sampling steps to estimate the free energy landscape for CV subspaces of various shapes and dimensions, including irregular two-dimensional regions and one-dimensional pathways between metastable states. We believe that SinkMeta will pioneer a new paradigm for sampling partial phase spaces, especially offering an efficient and flexible solution for sampling minimum free energy paths in high-dimensional spaces.

physics.comp-ph↗

arXiv2023

A Generalized Nucleation Theory for Ice Crystallization

Despite the simplicity of the water molecule, the kinetics of ice nucleation under natural conditions can be complex. We investigated spontaneously grown ice nuclei using all-atom molecular dynamics simulations and found significant differences between the kinetics of ice formation through spontaneously formed and ideal nuclei. Since classical nucleation theory can only provide a good description of ice nucleation in ideal conditions, we propose a generalized nucleation theory that can better characterize the kinetics of ice crystal nucleation in general conditions. This study provides an explanation on why previous experimental and computational studies have yielded widely varying critical nucleation sizes.

cond-mat.soft↗

arXiv2021

Towards a Larger Molecular Simulation on the Quantum Computer: Up to 28 Qubits Systems Accelerated by Point Group Symmetry

The exact evaluation of the molecular ground state in quantum chemistry requires an exponentially increasing computational cost. Quantum computation is a promising way to overcome the exponential problem using polynomial-time quantum algorithms. A quantum-classical hybrid optimization scheme known as the variational quantum eigensolver(VQE) is preferred for noisy intermediate-scale quantum devices. However, the circuit depth becomes one of the bottlenecks of its application to large molecules of more than 20 qubits. In this work, we employ the point group symmetry to reduce the number of operators in constructing ansatz so as to achieve a more compact quantum circuit. We illustrate this methodology with a series of molecules ranging from LiH(12 qubits) to C2H4(28 qubits). A significant reduction of up to 82% of the operator numbers is reached on C2H4, which enables the largest molecule ever numerically simulated by VQE-UCC to the best of our knowledge. This also shed light into the further work of this direction to construct even shallower ansatz with enough expressive power and simulate even larger scale system.

quant-ph↗