Integrating Protein Dynamics into Structure-Based Drug Design via Full-Atom Stochastic Flows

• URL: http://arxiv.org/abs/2503.03989v1
• Date: Thu, 06 Mar 2025 00:34:44 GMT
• Title: Integrating Protein Dynamics into Structure-Based Drug Design via Full-Atom Stochastic Flows
• Authors: Xiangxin Zhou, Yi Xiao, Haowei Lin, Xinheng He, Jiaqi Guan, Yang Wang, Qiang Liu, Feng Zhou, Liang Wang, Jianzhu Ma,
• Abstract summary: Traditional structure-based drug design (SBDD) approaches typically target binding sites with rigid structures.<n>We propose to use generative modeling for SBDD considering conformational changes of protein pockets.<n>We show that DynamicFlow learns to transform apo pockets and noisy pockets into holo pockets and corresponding 3D molecules.
• Score: 29.49146207945794
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The dynamic nature of proteins, influenced by ligand interactions, is essential for comprehending protein function and progressing drug discovery. Traditional structure-based drug design (SBDD) approaches typically target binding sites with rigid structures, limiting their practical application in drug development. While molecular dynamics simulation can theoretically capture all the biologically relevant conformations, the transition rate is dictated by the intrinsic energy barrier between them, making the sampling process computationally expensive. To overcome the aforementioned challenges, we propose to use generative modeling for SBDD considering conformational changes of protein pockets. We curate a dataset of apo and multiple holo states of protein-ligand complexes, simulated by molecular dynamics, and propose a full-atom flow model (and a stochastic version), named DynamicFlow, that learns to transform apo pockets and noisy ligands into holo pockets and corresponding 3D ligand molecules. Our method uncovers promising ligand molecules and corresponding holo conformations of pockets. Additionally, the resultant holo-like states provide superior inputs for traditional SBDD approaches, playing a significant role in practical drug discovery.

Related papers

• Enhanced Sampling, Public Dataset and Generative Model for Drug-Protein Dissociation Dynamics [10.80659641278556]
  Drug-protein binding and dissociation dynamics are fundamental to understanding molecular interactions in biological systems. We propose a novel research paradigm that combines molecular dynamics simulations, enhanced sampling, and AI generative models to address this issue. Our ongoing efforts focus on expanding this methodology to encompass a broader spectrum of drug-protein complexes and exploring novel applications in pathway prediction.
  arXiv  Detail & Related papers  (2025-04-25T14:10:06Z)
• Molecule Generation for Target Protein Binding with Hierarchical Consistency Diffusion Model [17.885767456439215]
  Atom-Motif Consistency Diffusion Model (AMDiff) is a hierarchical diffusion architecture that integrates both atom- and motif-level views of molecules.<n>Compared to existing approaches, AMDiff exhibits superior validity and novelty in generating molecules tailored to fit various protein pockets.
  arXiv  Detail & Related papers  (2025-03-02T17:54:30Z)
• Aligning Target-Aware Molecule Diffusion Models with Exact Energy Optimization [147.7899503829411]
  AliDiff is a novel framework to align pretrained target diffusion models with preferred functional properties. It can generate molecules with state-of-the-art binding energies with up to -7.07 Avg. Vina Score.
  arXiv  Detail & Related papers  (2024-07-01T06:10:29Z)
• UniIF: Unified Molecule Inverse Folding [67.60267592514381]
  We propose a unified model UniIF for inverse folding of all molecules. Our proposed method surpasses state-of-the-art methods on all tasks.
  arXiv  Detail & Related papers  (2024-05-29T10:26:16Z)
• AUTODIFF: Autoregressive Diffusion Modeling for Structure-based Drug Design [16.946648071157618]
  We propose a diffusion-based fragment-wise autoregressive generation model for structure-based drug design (SBDD) We design a novel molecule assembly strategy named conformal motif that preserves the conformation of local structures of molecules first. We then encode the interaction of the protein-ligand complex with an SE(3)-equivariant convolutional network and generate molecules motif-by-motif with diffusion modeling.
  arXiv  Detail & Related papers  (2024-04-02T14:44:02Z)
• DecompDiff: Diffusion Models with Decomposed Priors for Structure-Based Drug Design [62.68420322996345]
  Existing structured-based drug design methods treat all ligand atoms equally. We propose a new diffusion model, DecompDiff, with decomposed priors over arms and scaffold. Our approach achieves state-of-the-art performance in generating high-affinity molecules.
  arXiv  Detail & Related papers  (2024-02-26T05:21:21Z)
• Functional-Group-Based Diffusion for Pocket-Specific Molecule Generation and Elaboration [63.23362798102195]
  We propose D3FG, a functional-group-based diffusion model for pocket-specific molecule generation and elaboration. D3FG decomposes molecules into two categories of components: functional groups defined as rigid bodies and linkers as mass points. In the experiments, our method can generate molecules with more realistic 3D structures, competitive affinities toward the protein targets, and better drug properties.
  arXiv  Detail & Related papers  (2023-05-30T06:41:20Z)
• Learning Subpocket Prototypes for Generalizable Structure-based Drug Design [17.61987808099346]
  Deep generative models have achieved remarkable success in generating 3D molecules conditioned on the protein pocket. Most existing methods consider molecular generation for protein pockets independently. We propose a novel method DrugGPS for generalizable structure-based drug design.
  arXiv  Detail & Related papers  (2023-05-22T13:49:49Z)
• DiffBP: Generative Diffusion of 3D Molecules for Target Protein Binding [51.970607704953096]
  Previous works usually generate atoms in an auto-regressive way, where element types and 3D coordinates of atoms are generated one by one. In real-world molecular systems, the interactions among atoms in an entire molecule are global, leading to the energy function pair-coupled among atoms. In this work, a generative diffusion model for molecular 3D structures based on target proteins is established, at a full-atom level in a non-autoregressive way.
  arXiv  Detail & Related papers  (2022-11-21T07:02:15Z)
• State-specific protein-ligand complex structure prediction with a multi-scale deep generative model [68.28309982199902]
  We present NeuralPLexer, a computational approach that can directly predict protein-ligand complex structures. Our study suggests that a data-driven approach can capture the structural cooperativity between proteins and small molecules, showing promise in accelerating the design of enzymes, drug molecules, and beyond.
  arXiv  Detail & Related papers  (2022-09-30T01:46:38Z)
• Widely Used and Fast De Novo Drug Design by a Protein Sequence-Based Reinforcement Learning Model [4.815696666006742]
  Structure-based de novo method can overcome the data scarcity of active by incorporating drug-target interaction into deep generative architectures. Here, we demonstrate a widely used and fast protein sequence-based reinforcement learning model for drug discovery. As a proof of concept, the RL model was utilized to design molecules for four targets.
  arXiv  Detail & Related papers  (2022-08-14T10:41:52Z)
• Accurate Machine Learned Quantum-Mechanical Force Fields for Biomolecular Simulations [51.68332623405432]
  Molecular dynamics (MD) simulations allow atomistic insights into chemical and biological processes. Recently, machine learned force fields (MLFFs) emerged as an alternative means to execute MD simulations. This work proposes a general approach to constructing accurate MLFFs for large-scale molecular simulations.
  arXiv  Detail & Related papers  (2022-05-17T13:08:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.