mlsanityData to 14 Oct 2026

Learning a Continuous Representation of 3D Molecular Structures with Deep Generative Models

arXiv 2010.08687 ↗First posted 17 Oct 2020q-bio.QM cs.LG q-bio.BMcited by 24

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Abstract (arXiv)

Machine learning in drug discovery has been focused on virtual screening of molecular libraries using discriminative models. Generative models are an entirely different approach that learn to represent and optimize molecules in a continuous latent space. These methods have been increasingly successful at generating two dimensional molecules as SMILES strings and molecular graphs. In this work, we describe deep generative models of three dimensional molecular structures using atomic density grids and a novel fitting algorithm for converting continuous grids to discrete molecular structures. Our models jointly represent drug-like molecules and their conformations in a latent space that can be explored through interpolation. We are also able to sample diverse sets of molecules based on a given input compound and increase the probability of creating valid, drug-like molecules.

Key facts. Cited by 24 papers in the map (data through 14 Oct 2026). First posted on arXiv 17 Oct 2020.

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Citing papers per year: 2021: 2; 2022: 4; 2023: 4; 2024: 6; 2025: 6; 2026: 2.

24 papers.

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  1. 2022Equivariant Diffusion for Molecule Generation in 3Dcited by 677
  2. 20233D Equivariant Diffusion for Target-Aware Molecule Generation and Affinity Predictioncited by 177
  3. 2022A 3D Generative Model for Structure-Based Drug Designcited by 149
  4. 2023A Hitchhiker's Guide to Geometric GNNs for 3D Atomic Systemscited by 68
  5. 2023MolDiff: Addressing the Atom-Bond Inconsistency Problem in 3D Molecule Diffusion Generationcited by 65
  6. 2024Mixed Continuous and Categorical Flow Matching for 3D De Novo Molecule Generationcited by 54
  7. 20233D molecule generation by denoising voxel gridscited by 40
  8. 2021Learning to design drug-like molecules in three-dimensional space using deep generative modelscited by 37
  9. 2025FlowMol3: Flow Matching for 3D De Novo Small-Molecule Generationcited by 21
  10. 2022Equivariant Shape-Conditioned Generation of 3D Molecules for Ligand-Based Drug Designcited by 19
  11. 2024Structure-based drug design by denoising voxel gridscited by 19
  12. 2024TAGMol: Target-Aware Gradient-guided Molecule Generationcited by 12
12 more
  1. 2025Unified Guidance for Geometry-Conditioned Molecular Generationcited by 9
  2. 2024E(3)-equivariant models cannot learn chirality: Field-based molecular generationcited by 6
  3. 2025Sampling 3D Molecular Conformers with Diffusion Transformerscited by 6
  4. 2024NEBULA: Neural Empirical Bayes Under Latent Representations for Efficient and Controllable Design of Molecular Librariescited by 3
  5. 2022ParticleGrid: Enabling Deep Learning using 3D Representation of Materialscited by 2
  6. 2024Rapid Review of Generative AI in Smart Medical Applicationscited by 2
  7. 2025Do we need equivariant models for molecule generation?cited by 2
  8. 2025Predictive Feature Caching for Training-free Acceleration of Molecular Geometry Generationcited by 2
  9. 2021An In-depth Summary of Recent Artificial Intelligence Applications in Drug Designcited by 1
  10. 2025Pharmacophore-based design by learning on voxel gridscited by 1
  11. 2026CORDS: Continuous Representations of Discrete Structurescited by 1
  12. 2026VecMol: Vector-Field Representations for 3D Molecule Generationcited by 1