E(3)-equivariant models cannot learn chirality: Field-based molecular generation

• URL: http://arxiv.org/abs/2402.15864v2
• Date: Fri, 18 Apr 2025 07:16:40 GMT
• Title: E(3)-equivariant models cannot learn chirality: Field-based molecular generation
• Authors: Alexandru Dumitrescu, Dani Korpela, Markus Heinonen, Yogesh Verma, Valerii Iakovlev, Vikas Garg, Harri Lähdesmäki,
• Abstract summary: Chirality plays a key role in determining drug safety and potency.<n>We introduce a novel field-based representation, proposing reference rotations that replace rotational symmetry constraints.<n>The proposed model captures all molecular geometries including chirality, while still achieving highly competitive performance with E(3)-based methods across standard benchmarking metrics.
• Score: 51.327048911864885
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Obtaining the desired effect of drugs is highly dependent on their molecular geometries. Thus, the current prevailing paradigm focuses on 3D point-cloud atom representations, utilizing graph neural network (GNN) parametrizations, with rotational symmetries baked in via E(3) invariant layers. We prove that such models must necessarily disregard chirality, a geometric property of the molecules that cannot be superimposed on their mirror image by rotation and translation. Chirality plays a key role in determining drug safety and potency. To address this glaring issue, we introduce a novel field-based representation, proposing reference rotations that replace rotational symmetry constraints. The proposed model captures all molecular geometries including chirality, while still achieving highly competitive performance with E(3)-based methods across standard benchmarking metrics.

Related papers

• Learning Disentangled Equivariant Representation for Explicitly Controllable 3D Molecule Generation [29.905705361297578]
  conditional generation of 3D drug-like molecules with textitexplicit control We propose an E(3)-equivariant Wasserstein autoencoder and factorize the latent space of our generative model into two disentangled aspects: molecular properties and the remaining structural context of 3D molecules.
  arXiv  Detail & Related papers  (2024-12-19T17:33:56Z)
• MolMiner: Transformer architecture for fragment-based autoregressive generation of molecular stories [7.366789601705544]
  Chemical validity, interpretability of the generation process and flexibility to variable molecular sizes are among some of the remaining challenges for generative models in computational materials design. We propose an autoregressive approach that decomposes molecular generation into a sequence of discrete and interpretable steps. Our results show that the model can effectively bias the generation distribution according to the prompted multi-target objective.
  arXiv  Detail & Related papers  (2024-11-10T22:00:55Z)
• Conditional Synthesis of 3D Molecules with Time Correction Sampler [58.0834973489875]
  Time-Aware Conditional Synthesis (TACS) is a novel approach to conditional generation on diffusion models. It integrates adaptively controlled plug-and-play "online" guidance into a diffusion model, driving samples toward the desired properties.
  arXiv  Detail & Related papers  (2024-11-01T12:59:25Z)
• FARM: Functional Group-Aware Representations for Small Molecules [55.281754551202326]
  We introduce Functional Group-Aware Representations for Small Molecules (FARM) FARM is a foundation model designed to bridge the gap between SMILES, natural language, and molecular graphs. We rigorously evaluate FARM on the MoleculeNet dataset, where it achieves state-of-the-art performance on 10 out of 12 tasks.
  arXiv  Detail & Related papers  (2024-10-02T23:04:58Z)
• Geometry Informed Tokenization of Molecules for Language Model Generation [85.80491667588923]
  We consider molecule generation in 3D space using language models (LMs) Although tokenization of molecular graphs exists, that for 3D geometries is largely unexplored. We propose the Geo2Seq, which converts molecular geometries into $SE(3)$-invariant 1D discrete sequences.
  arXiv  Detail & Related papers  (2024-08-19T16:09:59Z)
• SE(3)-Invariant Multiparameter Persistent Homology for Chiral-Sensitive Molecular Property Prediction [1.534667887016089]
  We present a novel method for generating molecular fingerprints using multi parameter persistent homology (MPPH) This technique holds considerable significance for drug discovery and materials science, where precise molecular property prediction is vital. We demonstrate its superior performance over existing state-of-the-art methods in predicting molecular properties through extensive evaluations on the MoleculeNet benchmark.
  arXiv  Detail & Related papers  (2023-12-12T09:33:54Z)
• Diffusing on Two Levels and Optimizing for Multiple Properties: A Novel Approach to Generating Molecules with Desirable Properties [33.2976176283611]
  We present a novel approach to generating molecules with desirable properties, which expands the diffusion model framework with multiple innovative designs. To get desirable molecular fragments, we develop a novel electronic effect based fragmentation method. We show that the molecules generated by our proposed method have better validity, uniqueness, novelty, Fr'echet ChemNet Distance (FCD), QED, and PlogP than those generated by current SOTA models.
  arXiv  Detail & Related papers  (2023-10-05T11:43:21Z)
• Molecule Design by Latent Space Energy-Based Modeling and Gradual Distribution Shifting [53.44684898432997]
  Generation of molecules with desired chemical and biological properties is critical for drug discovery. We propose a probabilistic generative model to capture the joint distribution of molecules and their properties. Our method achieves very strong performances on various molecule design tasks.
  arXiv  Detail & Related papers  (2023-06-09T03:04:21Z)
• A Group Symmetric Stochastic Differential Equation Model for Molecule Multi-modal Pretraining [40.02834704975147]
  molecule pretraining has quickly become the go-to schema to boost the performance of AI-based drug discovery. Here, we propose MoleculeSDE to generate the 3D reflection from 2D topologies, and vice versa, directly in the input space. By comparing with 17 pretraining baselines, we empirically verify that MoleculeSDE can learn an expressive representation with state-of-the-art performance on 26 out of 32 downstream tasks.
  arXiv  Detail & Related papers  (2023-05-28T15:56:02Z)
• 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)
• 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)
• Molecular Geometry-aware Transformer for accurate 3D Atomic System modeling [51.83761266429285]
  We propose a novel Transformer architecture that takes nodes (atoms) and edges (bonds and nonbonding atom pairs) as inputs and models the interactions among them. Moleformer achieves state-of-the-art on the initial state to relaxed energy prediction of OC20 and is very competitive in QM9 on predicting quantum chemical properties.
  arXiv  Detail & Related papers  (2023-02-02T03:49:57Z)
• Molecular Attributes Transfer from Non-Parallel Data [57.010952598634944]
  We formulate molecular optimization as a style transfer problem and present a novel generative model that could automatically learn internal differences between two groups of non-parallel data. Experiments on two molecular optimization tasks, toxicity modification and synthesizability improvement, demonstrate that our model significantly outperforms several state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-11-30T06:10:22Z)
• Learning 3D Representations of Molecular Chirality with Invariance to Bond Rotations [2.17167311150369]
  We design an SE(3)-invariant model that processes torsion angles of a 3D molecular conformer. We test our model on four benchmarks: contrastive learning to distinguish conformers of different stereoisomers in a learned latent space, classification of chiral centers as R/S, prediction of how enantiomers rotate circularly polarized light, and ranking enantiomers by their docking scores in an enantiosensitive protein pocket.
  arXiv  Detail & Related papers  (2021-10-08T21:25:47Z)
• 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)
• Investigating 3D Atomic Environments for Enhanced QSAR [0.0]
  Predicting bioactivity and physical properties of molecules is a longstanding challenge in drug design. Most approaches use molecular descriptors based on a 2D representation of molecules as a graph of atoms and bonds, abstracting away the molecular shape. We describe a novel alignment-free 3D QSAR method using Smooth Overlap of Atomic Positions (SOAP), a well-established formalism developed for interpolating potential energy surfaces.
  arXiv  Detail & Related papers  (2020-10-24T10:04:48Z)

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.