Binding-Adaptive Diffusion Models for Structure-Based Drug Design

• URL: http://arxiv.org/abs/2402.18583v1
• Date: Mon, 15 Jan 2024 00:34:00 GMT
• Title: Binding-Adaptive Diffusion Models for Structure-Based Drug Design
• Authors: Zhilin Huang, Ling Yang, Zaixi Zhang, Xiangxin Zhou, Yu Bao, Xiawu Zheng, Yuwei Yang, Yu Wang, Wenming Yang,
• Abstract summary: We propose a novel framework, namely Binding-Adaptive Diffusion Models (BindDM) In BindDM, we adaptively extract subcomplex, the essential part of binding sites responsible for protein-ligand interactions. BindDM can generate molecules with more realistic 3D structures and higher binding affinities towards the protein targets, with up to -5.92 Avg. Vina Score.
• Score: 33.9764269117599
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Structure-based drug design (SBDD) aims to generate 3D ligand molecules that bind to specific protein targets. Existing 3D deep generative models including diffusion models have shown great promise for SBDD. However, it is complex to capture the essential protein-ligand interactions exactly in 3D space for molecular generation. To address this problem, we propose a novel framework, namely Binding-Adaptive Diffusion Models (BindDM). In BindDM, we adaptively extract subcomplex, the essential part of binding sites responsible for protein-ligand interactions. Then the selected protein-ligand subcomplex is processed with SE(3)-equivariant neural networks, and transmitted back to each atom of the complex for augmenting the target-aware 3D molecule diffusion generation with binding interaction information. We iterate this hierarchical complex-subcomplex process with cross-hierarchy interaction node for adequately fusing global binding context between the complex and its corresponding subcomplex. Empirical studies on the CrossDocked2020 dataset show BindDM can generate molecules with more realistic 3D structures and higher binding affinities towards the protein targets, with up to -5.92 Avg. Vina Score, while maintaining proper molecular properties. Our code is available at https://github.com/YangLing0818/BindDM

Related papers

• 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)
• 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)
• Autoregressive fragment-based diffusion for pocket-aware ligand design [0.0]
  AutoFragDiff is a fragment-based autoregressive diffusion model for generating 3D structures conditioned on target protein structures. We employ geometric vector perceptrons to predict atom types and spatial coordinates of new molecular fragments conditioned on molecular scaffolds and protein pockets.
  arXiv  Detail & Related papers  (2023-12-15T04:03:03Z)
• 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)
• 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)
• Equivariant 3D-Conditional Diffusion Models for Molecular Linker Design [82.23006955069229]
  We propose DiffLinker, an E(3)-equivariant 3D-conditional diffusion model for molecular linker design. Our model places missing atoms in between and designs a molecule incorporating all the initial fragments. We demonstrate that DiffLinker outperforms other methods on the standard datasets generating more diverse and synthetically-accessible molecules.
  arXiv  Detail & Related papers  (2022-10-11T09:13:37Z)
• In-Pocket 3D Graphs Enhance Ligand-Target Compatibility in Generative Small-Molecule Creation [0.0]
  We present a graph-based generative modeling technology that encodes explicit 3D protein-ligand contacts within a relational graph architecture. The models combine a conditional variational autoencoder that allows for activity-specific molecule generation with putative contact generation that provides predictions of molecular interactions within the target binding pocket.
  arXiv  Detail & Related papers  (2022-04-05T22:53:51Z)
• Generating 3D Molecules Conditional on Receptor Binding Sites with Deep Generative Models [0.0]
  We describe for the first time a deep learning system for generating 3D molecular structures conditioned on a receptor binding site. We apply atom fitting and bond inference procedures to construct valid molecular conformations from generated atomic densities. This work opens the door for end-to-end prediction of stable bioactive molecules from protein structures with deep learning.
  arXiv  Detail & Related papers  (2021-10-28T15:17:24Z)

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.