CoarsenConf: Equivariant Coarsening with Aggregated Attention for Molecular Conformer Generation

• URL: http://arxiv.org/abs/2306.14852v2
• Date: Thu, 19 Oct 2023 23:18:42 GMT
• Title: CoarsenConf: Equivariant Coarsening with Aggregated Attention for Molecular Conformer Generation
• Authors: Danny Reidenbach, Aditi S. Krishnapriyan
• Abstract summary: We introduce CoarsenConf, which integrates molecular graphs based on torsional angles into an SE(3)-equivariant hierarchical variational autoencoder. Through equivariant coarse-graining, we aggregate the fine-grained atomic coordinates of subgraphs connected via rotatable bonds, creating a variable-length coarse-grained latent representation. Our model uses a novel aggregated attention mechanism to restore fine-grained coordinates from the coarse-grained latent representation, enabling efficient generation of accurate conformers.
• Score: 3.31521245002301
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Molecular conformer generation (MCG) is an important task in cheminformatics and drug discovery. The ability to efficiently generate low-energy 3D structures can avoid expensive quantum mechanical simulations, leading to accelerated virtual screenings and enhanced structural exploration. Several generative models have been developed for MCG, but many struggle to consistently produce high-quality conformers. To address these issues, we introduce CoarsenConf, which coarse-grains molecular graphs based on torsional angles and integrates them into an SE(3)-equivariant hierarchical variational autoencoder. Through equivariant coarse-graining, we aggregate the fine-grained atomic coordinates of subgraphs connected via rotatable bonds, creating a variable-length coarse-grained latent representation. Our model uses a novel aggregated attention mechanism to restore fine-grained coordinates from the coarse-grained latent representation, enabling efficient generation of accurate conformers. Furthermore, we evaluate the chemical and biochemical quality of our generated conformers on multiple downstream applications, including property prediction and oracle-based protein docking. Overall, CoarsenConf generates more accurate conformer ensembles compared to prior generative models.

Related papers

• Lift Your Molecules: Molecular Graph Generation in Latent Euclidean Space [46.11163798008912]
  We introduce a new framework for molecular graph generation with 3D molecular generative models. Our framework maps molecular graphs to Euclidean point clouds via synthetic conformer coordinates and learns the inverse map using an E(n)-Equivariant Graph Neural Network (EGNN) The induced point cloud-structured latent space is well-suited to apply existing 3D molecular generative models.
  arXiv  Detail & Related papers  (2024-06-15T05:29:07Z)
• HEroBM: a deep equivariant graph neural network for universal backmapping from coarse-grained to all-atom representations [0.0]
  coarse-grained (CG) techniques have emerged as invaluable tools to sample large-scale systems. They sacrifice atomistic details that might hold significant relevance in deciphering the investigated process. A recommended approach is to identify key CG conformations and process them using backmapping methods, which retrieve atomistic coordinates. We introduce HEroBM, a dynamic and scalable method that employs deep equivariant graph neural networks and a hierarchical approach to achieve high-resolution backmapping.
  arXiv  Detail & Related papers  (2024-04-25T13:54:31Z)
• CWF: Consolidating Weak Features in High-quality Mesh Simplification [50.634070540791555]
  We propose a smooth functional that simultaneously considers all of these requirements. The functional comprises a normal anisotropy term and a Centroidal Voronoi Tessellation (CVT) energy term.
  arXiv  Detail & Related papers  (2024-04-24T05:37:17Z)
• DecompOpt: Controllable and Decomposed Diffusion Models for Structure-based Molecular Optimization [49.85944390503957]
  DecompOpt is a structure-based molecular optimization method based on a controllable and diffusion model. We show that DecompOpt can efficiently generate molecules with improved properties than strong de novo baselines.
  arXiv  Detail & Related papers  (2024-03-07T02:53:40Z)
• Mesh-based Gaussian Splatting for Real-time Large-scale Deformation [58.18290393082119]
  It is challenging for users to directly deform or manipulate implicit representations with large deformations in the real-time fashion. We develop a novel GS-based method that enables interactive deformation. Our approach achieves high-quality reconstruction and effective deformation, while maintaining the promising rendering results at a high frame rate.
  arXiv  Detail & Related papers  (2024-02-07T12:36:54Z)
• SIGMA: Scale-Invariant Global Sparse Shape Matching [50.385414715675076]
  We propose a novel mixed-integer programming (MIP) formulation for generating precise sparse correspondences for non-rigid shapes. We show state-of-the-art results for sparse non-rigid matching on several challenging 3D datasets.
  arXiv  Detail & Related papers  (2023-08-16T14:25:30Z)
• Modular Flows: Differential Molecular Generation [18.41106104201439]
  Flows can generate molecules effectively by inverting the encoding process. Existing flow models require artifactual dequantization or specific node/edge orderings. We develop continuous normalizing E(3)-equivariant flows, based on a system of node ODEs and a graph PDE. Our models can be cast as message-passing temporal networks, and result in superlative performance on the tasks of density estimation and molecular generation.
  arXiv  Detail & Related papers  (2022-10-12T09:08:35Z)
• GeoDiff: a Geometric Diffusion Model for Molecular Conformation Generation [102.85440102147267]
  We propose a novel generative model named GeoDiff for molecular conformation prediction. We show that GeoDiff is superior or comparable to existing state-of-the-art approaches.
  arXiv  Detail & Related papers  (2022-03-06T09:47:01Z)
• Generative Coarse-Graining of Molecular Conformations [28.127928605838388]
  We propose a novel model that embeds the vital probabilistic nature and geometric consistency requirements of the backmapping transformation. Our approach always recovers more realistic structures and outperforms existing data-driven methods with a significant margin.
  arXiv  Detail & Related papers  (2022-01-28T15:18:34Z)
• GeoMol: Torsional Geometric Generation of Molecular 3D Conformer Ensembles [60.12186997181117]
  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.
  arXiv  Detail & Related papers  (2021-06-08T14:17:59Z)

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.