Re-Dock: Towards Flexible and Realistic Molecular Docking with Diffusion Bridge

• URL: http://arxiv.org/abs/2402.11459v2
• Date: Wed, 21 Feb 2024 07:46:07 GMT
• Title: Re-Dock: Towards Flexible and Realistic Molecular Docking with Diffusion Bridge
• Authors: Yufei Huang, Odin Zhang, Lirong Wu, Cheng Tan, Haitao Lin, Zhangyang Gao, Siyuan Li and Stan.Z. Li
• Abstract summary: Re-Dock is a novel diffusion bridge generative model extended to geometric manifold. We propose energy-to-geometry mapping inspired by the Newton-Euler equation to co-model the binding energy and conformations. Experiments on designed benchmark datasets including apo-dock and cross-dock demonstrate our model's superior effectiveness and efficiency over current methods.
• Score: 69.80471117520719
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Accurate prediction of protein-ligand binding structures, a task known as molecular docking is crucial for drug design but remains challenging. While deep learning has shown promise, existing methods often depend on holo-protein structures (docked, and not accessible in realistic tasks) or neglect pocket sidechain conformations, leading to limited practical utility and unrealistic conformation predictions. To fill these gaps, we introduce an under-explored task, named flexible docking to predict poses of ligand and pocket sidechains simultaneously and introduce Re-Dock, a novel diffusion bridge generative model extended to geometric manifolds. Specifically, we propose energy-to-geometry mapping inspired by the Newton-Euler equation to co-model the binding energy and conformations for reflecting the energy-constrained docking generative process. Comprehensive experiments on designed benchmark datasets including apo-dock and cross-dock demonstrate our model's superior effectiveness and efficiency over current methods.

Related papers

• Bridging Geometric States via Geometric Diffusion Bridge [79.60212414973002]
  We introduce the Geometric Diffusion Bridge (GDB), a novel generative modeling framework that accurately bridges initial and target geometric states. GDB employs an equivariant diffusion bridge derived by a modified version of Doob's $h$-transform for connecting geometric states. We show that GDB surpasses existing state-of-the-art approaches, opening up a new pathway for accurately bridging geometric states.
  arXiv  Detail & Related papers  (2024-10-31T17:59:53Z)
• SPIN: SE(3)-Invariant Physics Informed Network for Binding Affinity Prediction [3.406882192023597]
  Accurate prediction of protein-ligand binding affinity is crucial for drug development. Traditional methods often fail to accurately model the complex's spatial information. We propose SPIN, a model that incorporates various inductive biases applicable to this task.
  arXiv  Detail & Related papers  (2024-07-10T08:40:07Z)
• Diffusion Model with Cross Attention as an Inductive Bias for Disentanglement [58.9768112704998]
  Disentangled representation learning strives to extract the intrinsic factors within observed data. We introduce a new perspective and framework, demonstrating that diffusion models with cross-attention can serve as a powerful inductive bias. This is the first work to reveal the potent disentanglement capability of diffusion models with cross-attention, requiring no complex designs.
  arXiv  Detail & Related papers  (2024-02-15T05:07:54Z)
• Multi-scale Iterative Refinement towards Robust and Versatile Molecular Docking [17.28573902701018]
  Molecular docking is a key computational tool utilized to predict the binding conformations of small molecules to protein targets. We introduce DeltaDock, a robust and versatile framework designed for efficient molecular docking.
  arXiv  Detail & Related papers  (2023-11-30T14:09:20Z)
• Pre-Training on Large-Scale Generated Docking Conformations with HelixDock to Unlock the Potential of Protein-ligand Structure Prediction Models [42.16524616409125]
  In this work, we show that by pre-training on a large-scale docking conformation, we can obtain a protein-ligand structure prediction model with outstanding performance. The proposed model, HelixDock, aims to acquire the physical knowledge encapsulated by the physics-based docking tools during the pre-training phase.
  arXiv  Detail & Related papers  (2023-10-21T05:54:26Z)
• ETDock: A Novel Equivariant Transformer for Protein-Ligand Docking [36.14826783009814]
  Traditional docking methods rely on scoring functions and deep learning to predict the docking between proteins and drugs. In this paper, we propose a transformer neural network for protein-ligand docking pose prediction. The experimental results on real datasets show that our model can achieve state-of-the-art performance.
  arXiv  Detail & Related papers  (2023-10-12T06:23:12Z)
• Latent Traversals in Generative Models as Potential Flows [113.4232528843775]
  We propose to model latent structures with a learned dynamic potential landscape. Inspired by physics, optimal transport, and neuroscience, these potential landscapes are learned as physically realistic partial differential equations. Our method achieves both more qualitatively and quantitatively disentangled trajectories than state-of-the-art baselines.
  arXiv  Detail & Related papers  (2023-04-25T15:53:45Z)
• DIFFormer: Scalable (Graph) Transformers Induced by Energy Constrained Diffusion [66.21290235237808]
  We introduce an energy constrained diffusion model which encodes a batch of instances from a dataset into evolutionary states. We provide rigorous theory that implies closed-form optimal estimates for the pairwise diffusion strength among arbitrary instance pairs. Experiments highlight the wide applicability of our model as a general-purpose encoder backbone with superior performance in various tasks.
  arXiv  Detail & Related papers  (2023-01-23T15:18:54Z)
• EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction [31.191844909335963]
  Predicting how a drug-like molecule binds to a specific protein target is a core problem in drug discovery. An extremely fast computational binding method would enable key applications such as fast virtual screening or drug engineering. We present EquiBind, an SE(3)-equivariant geometric deep learning model performing direct-shot prediction of both i) the receptor binding location (blind docking) and ii) the ligand's bound pose and orientation.
  arXiv  Detail & Related papers  (2022-02-07T16:26:05Z)
• A Unifying and Canonical Description of Measure-Preserving Diffusions [60.59592461429012]
  A complete recipe of measure-preserving diffusions in Euclidean space was recently derived unifying several MCMC algorithms into a single framework. We develop a geometric theory that improves and generalises this construction to any manifold.
  arXiv  Detail & Related papers  (2021-05-06T17:36:55Z)

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.