ATOM3D: Tasks On Molecules in Three Dimensions

• URL: http://arxiv.org/abs/2012.04035v1
• Date: Mon, 7 Dec 2020 20:18:23 GMT
• Title: ATOM3D: Tasks On Molecules in Three Dimensions
• Authors: Raphael J.L. Townshend, Martin V\"ogele, Patricia Suriana, Alexander Derry, Alexander Powers, Yianni Laloudakis, Sidhika Balachandar, Brandon Anderson, Stephan Eismann, Risi Kondor, Russ B. Altman, Ron O. Dror
• Abstract summary: Deep neural networks have recently gained significant attention. In this work we present ATOM3D, a collection of both novel and existing datasets spanning several key classes of biomolecules. We develop three-dimensional molecular learning networks for each of these tasks, finding that they consistently improve performance.
• Score: 91.72138447636769
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Computational methods that operate directly on three-dimensional molecular structure hold large potential to solve important questions in biology and chemistry. In particular deep neural networks have recently gained significant attention. In this work we present ATOM3D, a collection of both novel and existing datasets spanning several key classes of biomolecules, to systematically assess such learning methods. We develop three-dimensional molecular learning networks for each of these tasks, finding that they consistently improve performance relative to one- and two-dimensional methods. The specific choice of architecture proves to be critical for performance, with three-dimensional convolutional networks excelling at tasks involving complex geometries, while graph networks perform well on systems requiring detailed positional information. Furthermore, equivariant networks show significant promise. Our results indicate many molecular problems stand to gain from three-dimensional molecular learning. All code and datasets can be accessed via https://www.atom3d.ai .

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)
• HeMeNet: Heterogeneous Multichannel Equivariant Network for Protein Multitask Learning [33.972536394058004]
  We propose a neural network model to address multiple tasks jointly upon the input of 3D protein structures. In particular, we first construct a standard structure-based multi-task benchmark called Protein-MT. Then, we develop a novel graph neural network for multi-task learning, dubbed Heterogeneous Multichannel Equivariant Network (HeMeNet)
  arXiv  Detail & Related papers  (2024-04-02T06:53:45Z)
• A Hitchhiker's Guide to Geometric GNNs for 3D Atomic Systems [87.30652640973317]
  Recent advances in computational modelling of atomic systems represent them as geometric graphs with atoms embedded as nodes in 3D Euclidean space. Geometric Graph Neural Networks have emerged as the preferred machine learning architecture powering applications ranging from protein structure prediction to molecular simulations and material generation. This paper provides a comprehensive and self-contained overview of the field of Geometric GNNs for 3D atomic systems.
  arXiv  Detail & Related papers  (2023-12-12T18:44:19Z)
• A Universal Framework for Accurate and Efficient Geometric Deep Learning of Molecular Systems [19.268713909099507]
  PAMNet is a universal framework for learning the representations of 3D molecules of varying sizes and types in any molecular system. Inspired by molecular mechanics, PAMNet induces a physics-informed bias to explicitly model local and non-local interactions and their combined effects. In benchmark studies, PAMNet outperforms state-of-the-art baselines regarding both accuracy and efficiency in three diverse learning tasks.
  arXiv  Detail & Related papers  (2023-11-19T04:52:05Z)
• Implicit Geometry and Interaction Embeddings Improve Few-Shot Molecular Property Prediction [53.06671763877109]
  We develop molecular embeddings that encode complex molecular characteristics to improve the performance of few-shot molecular property prediction. Our approach leverages large amounts of synthetic data, namely the results of molecular docking calculations. On multiple molecular property prediction benchmarks, training from the embedding space substantially improves Multi-Task, MAML, and Prototypical Network few-shot learning performance.
  arXiv  Detail & Related papers  (2023-02-04T01:32:40Z)
• ViSNet: an equivariant geometry-enhanced graph neural network with vector-scalar interactive message passing for molecules [69.05950120497221]
  We propose an equivariant geometry-enhanced graph neural network called ViSNet, which elegantly extracts geometric features and efficiently models molecular structures. Our proposed ViSNet outperforms state-of-the-art approaches on multiple MD benchmarks, including MD17, revised MD17 and MD22, and achieves excellent chemical property prediction on QM9 and Molecule3D datasets.
  arXiv  Detail & Related papers  (2022-10-29T07:12:46Z)
• Equivariant Graph Attention Networks for Molecular Property Prediction [0.34376560669160383]
  Learning about 3D molecular structures with varying size is an emerging challenge in machine learning and especially in drug discovery. We propose an equivariant Graph Neural Networks (GNN) that operates with Cartesian coordinates to incorporate directionality. We demonstrate the efficacy of our architecture on predicting quantum mechanical properties of small molecules and its benefit on problems that concern macromolecular structures such as protein complexes.
  arXiv  Detail & Related papers  (2022-02-20T19:07:29Z)
• 3D Infomax improves GNNs for Molecular Property Prediction [1.9703625025720701]
  We propose pre-training a model to reason about the geometry of molecules given only their 2D molecular graphs. We show that 3D pre-training provides significant improvements for a wide range of properties.
  arXiv  Detail & Related papers  (2021-10-08T13:30:49Z)
• 3D-Transformer: Molecular Representation with Transformer in 3D Space [11.947499562836953]
  3D-Transformer is a variant of the Transformer for molecular representations that incorporates 3D spatial information. Our experiments show significant improvements over state-of-the-art models on the crystal property prediction task and the protein-ligand binding affinity prediction task.
  arXiv  Detail & Related papers  (2021-10-04T05:11:23Z)
• TSGCNet: Discriminative Geometric Feature Learning with Two-Stream GraphConvolutional Network for 3D Dental Model Segmentation [141.2690520327948]
  We propose a two-stream graph convolutional network (TSGCNet) to learn multi-view information from different geometric attributes. We evaluate our proposed TSGCNet on a real-patient dataset of dental models acquired by 3D intraoral scanners.
  arXiv  Detail & Related papers  (2020-12-26T08:02:56Z)

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.