Conformation Generation using Transformer Flows

• URL: http://arxiv.org/abs/2411.10817v1
• Date: Sat, 16 Nov 2024 14:42:05 GMT
• Title: Conformation Generation using Transformer Flows
• Authors: Sohil Atul Shah, Vladlen Koltun,
• Abstract summary: We present ConfFlow, a flow-based model for conformation generation based on transformer networks. ConfFlow directly samples in the coordinate space without enforcing any explicit physical constraints. ConfFlow improve accuracy by up to $40%$ relative to state-of-the-art learning-based methods.
• Score: 55.2480439325792
• License:
• Abstract: Estimating three-dimensional conformations of a molecular graph allows insight into the molecule's biological and chemical functions. Fast generation of valid conformations is thus central to molecular modeling. Recent advances in graph-based deep networks have accelerated conformation generation from hours to seconds. However, current network architectures do not scale well to large molecules. Here we present ConfFlow, a flow-based model for conformation generation based on transformer networks. In contrast with existing approaches, ConfFlow directly samples in the coordinate space without enforcing any explicit physical constraints. The generative procedure is highly interpretable and is akin to force field updates in molecular dynamics simulation. When applied to the generation of large molecule conformations, ConfFlow improve accuracy by up to $40\%$ relative to state-of-the-art learning-based methods. The source code is made available at https://github.com/IntelLabs/ConfFlow.

Related papers

• Improving Molecular Graph Generation with Flow Matching and Optimal Transport [8.2504828891983]
  GGFlow is a discrete flow matching generative model incorporating optimal transport for molecular graphs. It incorporates an edge-augmented graph transformer to enable the direct communications among chemical bounds. GGFlow demonstrates superior performance on both unconditional and conditional molecule generation tasks.
  arXiv  Detail & Related papers  (2024-11-08T16:27:27Z)
• GraphXForm: Graph transformer for computer-aided molecular design with application to extraction [73.1842164721868]
  We present GraphXForm, a decoder-only graph transformer architecture, which is pretrained on existing compounds and then fine-tuned. We evaluate it on two solvent design tasks for liquid-liquid extraction, showing that it outperforms four state-of-the-art molecular design techniques.
  arXiv  Detail & Related papers  (2024-11-03T19:45:15Z)
• ET-Flow: Equivariant Flow-Matching for Molecular Conformer Generation [3.4146914514730633]
  We introduce Equivariant Transformer Flow (ET-Flow) to predict low-energy molecular conformations. Our approach results in a straightforward and scalable method that operates on all-atom coordinates with minimal assumptions. ET-Flow significantly increases the precision and physical validity of the generated conformers, while being a lighter model and faster at inference.
  arXiv  Detail & Related papers  (2024-10-29T16:44:10Z)
• GeoMFormer: A General Architecture for Geometric Molecular Representation Learning [84.02083170392764]
  We introduce a novel Transformer-based molecular model called GeoMFormer to achieve this goal. We show that GeoMFormer achieves strong performance on both invariant and equivariant tasks of different types and scales.
  arXiv  Detail & Related papers  (2024-06-24T17:58:13Z)
• Mixed Continuous and Categorical Flow Matching for 3D De Novo Molecule Generation [0.0]
  Flow matching is a recently proposed generative modeling framework that generalizes diffusion models. We extend the flow matching framework to categorical data by constructing flows that are constrained to exist on a continuous representation of categorical data known as the probability simplex. We find that, in practice, a simpler approach that makes no accommodations for the categorical nature of the data yields equivalent or superior performance.
  arXiv  Detail & Related papers  (2024-04-30T17:37:21Z)
• Zero Shot Molecular Generation via Similarity Kernels [0.6597195879147557]
  We present Similarity-based Molecular Generation (SiMGen), a new method for zero shot molecular generation. SiMGen combines a time-dependent similarity kernel with descriptors from a pretrained machine learning force field to generate molecules. We also release an interactive web tool that allows users to generate structures with SiMGen online.
  arXiv  Detail & Related papers  (2024-02-13T17:53:44Z)
• Learning Joint 2D & 3D Diffusion Models for Complete Molecule Generation [32.66694406638287]
  We propose a new joint 2D and 3D diffusion model (JODO) that generates molecules with atom types, formal charges, bond information, and 3D coordinates. Our model can also be extended for inverse molecular design targeting single or multiple quantum properties.
  arXiv  Detail & Related papers  (2023-05-21T04:49:53Z)
• DiffFacto: Controllable Part-Based 3D Point Cloud Generation with Cross Diffusion [68.39543754708124]
  We introduce DiffFacto, a novel probabilistic generative model that learns the distribution of shapes with part-level control. Experiments show that our method is able to generate novel shapes with multiple axes of control. It achieves state-of-the-art part-level generation quality and generates plausible and coherent shapes.
  arXiv  Detail & Related papers  (2023-05-03T06:38:35Z)
• MUDiff: Unified Diffusion for Complete Molecule Generation [104.7021929437504]
  We present a new model for generating a comprehensive representation of molecules, including atom features, 2D discrete molecule structures, and 3D continuous molecule coordinates. We propose a novel graph transformer architecture to denoise the diffusion process. Our model is a promising approach for designing stable and diverse molecules and can be applied to a wide range of tasks in molecular modeling.
  arXiv  Detail & Related papers  (2023-04-28T04:25:57Z)
• Geometric Transformer for End-to-End Molecule Properties Prediction [92.28929858529679]
  We introduce a Transformer-based architecture for molecule property prediction, which is able to capture the geometry of the molecule. We modify the classical positional encoder by an initial encoding of the molecule geometry, as well as a learned gated self-attention mechanism.
  arXiv  Detail & Related papers  (2021-10-26T14:14:40Z)

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.