Autoregressive fragment-based diffusion for pocket-aware ligand design

• URL: http://arxiv.org/abs/2401.05370v1
• Date: Fri, 15 Dec 2023 04:03:03 GMT
• Title: Autoregressive fragment-based diffusion for pocket-aware ligand design
• Authors: Mahdi Ghorbani, Leo Gendelev, Paul Beroza, Michael J. Keiser
• Abstract summary: 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this work, we introduce AutoFragDiff, a fragment-based autoregressive diffusion model for generating 3D molecular 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. Our approach improves the local geometry of the resulting 3D molecules while maintaining high predicted binding affinity to protein targets. The model can also perform scaffold extension from user-provided starting molecular scaffold.

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)
• 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)
• 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)
• Scalable Fragment-Based 3D Molecular Design with Reinforcement Learning [68.8204255655161]
  We introduce a novel framework for scalable 3D design that uses a hierarchical agent to build molecules. In a variety of experiments, we show that our agent, guided only by energy considerations, can efficiently learn to produce molecules with over 100 atoms.
  arXiv  Detail & Related papers  (2022-02-01T18:54:24Z)
• Structure-aware generation of drug-like molecules [2.449909275410288]
  Deep generative methods have shown promise in proposing novel molecules from scratch (de-novo design) We propose a novel supervised model that generates molecular graphs jointly with 3D pose in a discretised molecular space. We evaluate our model using a docking benchmark and find that guided generation improves predicted binding affinities by 8% and drug-likeness scores by 10% over the baseline.
  arXiv  Detail & Related papers  (2021-11-07T15:19:54Z)
• 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)
• Generating 3D Molecular Structures Conditional on a Receptor Binding Site with Deep Generative Models [0.0]
  We describe for the first time a deep generative model that can generate 3D structures conditioned on a three-dimensional molecular binding pocket. We show that valid and unique molecules can be readily sampled from the variational latent space defined by a reference seed' structure.
  arXiv  Detail & Related papers  (2020-10-16T16:27:47Z)

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.