GeoMol: Torsional Geometric Generation of Molecular 3D Conformer Ensembles

• URL: http://arxiv.org/abs/2106.07802v1
• Date: Tue, 8 Jun 2021 14:17:59 GMT
• Title: GeoMol: Torsional Geometric Generation of Molecular 3D Conformer Ensembles
• Authors: Octavian-Eugen Ganea, Lagnajit Pattanaik, Connor W. Coley, Regina Barzilay, Klavs F. Jensen, William H. Green, Tommi S. Jaakkola
• Abstract summary: Prediction of a molecule's 3D conformer ensemble from the molecular graph holds a key role in areas of cheminformatics and drug discovery. Existing generative models have several drawbacks including lack of modeling important molecular geometry elements. We propose GeoMol, an end-to-end, non-autoregressive and SE(3)-invariant machine learning approach to generate 3D conformers.
• Score: 60.12186997181117
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Prediction of a molecule's 3D conformer ensemble from the molecular graph holds a key role in areas of cheminformatics and drug discovery. Existing generative models have several drawbacks including lack of modeling important molecular geometry elements (e.g. torsion angles), separate optimization stages prone to error accumulation, and the need for structure fine-tuning based on approximate classical force-fields or computationally expensive methods such as metadynamics with approximate quantum mechanics calculations at each geometry. We propose GeoMol--an end-to-end, non-autoregressive and SE(3)-invariant machine learning approach to generate distributions of low-energy molecular 3D conformers. Leveraging the power of message passing neural networks (MPNNs) to capture local and global graph information, we predict local atomic 3D structures and torsion angles, avoiding unnecessary over-parameterization of the geometric degrees of freedom (e.g. one angle per non-terminal bond). Such local predictions suffice both for the training loss computation, as well as for the full deterministic conformer assembly (at test time). We devise a non-adversarial optimal transport based loss function to promote diverse conformer generation. GeoMol predominantly outperforms popular open-source, commercial, or state-of-the-art machine learning (ML) models, while achieving significant speed-ups. We expect such differentiable 3D structure generators to significantly impact molecular modeling and related applications.

Related papers

• Automated 3D Pre-Training for Molecular Property Prediction [54.15788181794094]
  We propose a novel 3D pre-training framework (dubbed 3D PGT) It pre-trains a model on 3D molecular graphs, and then fine-tunes it on molecular graphs without 3D structures. Extensive experiments on 2D molecular graphs are conducted to demonstrate the accuracy, efficiency and generalization ability of the proposed 3D PGT.
  arXiv  Detail & Related papers  (2023-06-13T14:43:13Z)
• Geometric Latent Diffusion Models for 3D Molecule Generation [172.15028281732737]
  Generative models, especially diffusion models (DMs), have achieved promising results for generating feature-rich geometries. We propose a novel and principled method for 3D molecule generation named Geometric Latent Diffusion Models (GeoLDM)
  arXiv  Detail & Related papers  (2023-05-02T01:07:22Z)
• 3D Molecular Geometry Analysis with 2D Graphs [79.47097907673877]
  Ground-state 3D geometries of molecules are essential for many molecular analysis tasks. Modern quantum mechanical methods can compute accurate 3D geometries but are computationally prohibitive. We propose a novel deep learning framework to predict 3D geometries from molecular graphs.
  arXiv  Detail & Related papers  (2023-05-01T19:00:46Z)
• Geometry-Complete Diffusion for 3D Molecule Generation and Optimization [3.8366697175402225]
  We introduce the Geometry-Complete Diffusion Model (GCDM) for 3D molecule generation. GCDM outperforms existing 3D molecular diffusion models by significant margins across conditional and unconditional settings. We also show that GCDM's geometric features can be repurposed to consistently optimize the geometry and chemical composition of existing 3D molecules.
  arXiv  Detail & Related papers  (2023-02-08T20:01:51Z)
• Equivalent Distance Geometry Error for Molecular Conformation Comparison [26.331322944298208]
  We propose Equivalent Distance Geometry Error (EDGE) to calculate the differential discrepancy between conformations. In the improved version of our method, the optimization features minimizing linear transformations of atom-pair distances within 3-hop.
  arXiv  Detail & Related papers  (2021-11-13T09:04:55Z)
• 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)
• Molecule3D: A Benchmark for Predicting 3D Geometries from Molecular Graphs [79.06686274377009]
  We develop a benchmark, known as Molecule3D, that includes a dataset with precise ground-state geometries of approximately 4 million molecules. We implement two baseline methods that either predict the pairwise distance between atoms or atom coordinates in 3D space. Our method can achieve comparable prediction accuracy but with much smaller computational costs.
  arXiv  Detail & Related papers  (2021-09-30T22:09:28Z)
• An End-to-End Framework for Molecular Conformation Generation via Bilevel Programming [71.82571553927619]
  We propose an end-to-end solution for molecular conformation prediction called ConfVAE. Specifically, the molecular graph is first encoded in a latent space, and then the 3D structures are generated by solving a principled bilevel optimization program.
  arXiv  Detail & Related papers  (2021-05-15T15:22:29Z)

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.