mlsanityData to 7 Oct 2026

Do Deep Learning Methods Really Perform Better in Molecular Conformation Generation?

arXiv 2302.07061 ↗First posted 14 Feb 2023cs.CE cs.LG q-bio.BMcited by 8

Not on the map. Why?

Abstract (arXiv)

Molecular conformation generation (MCG) is a fundamental and important problem in drug discovery. Many traditional methods have been developed to solve the MCG problem, such as systematic searching, model-building, random searching, distance geometry, molecular dynamics, Monte Carlo methods, etc. However, they have some limitations depending on the molecular structures. Recently, there are plenty of deep learning based MCG methods, which claim they largely outperform the traditional methods. However, to our surprise, we design a simple and cheap algorithm (parameter-free) based on the traditional methods and find it is comparable to or even outperforms deep learning based MCG methods in the widely used GEOM-QM9 and GEOM-Drugs benchmarks. In particular, our design algorithm is simply the clustering of the RDKIT-generated conformations. We hope our findings can help the community to revise the deep learning methods for MCG. The code of the proposed algorithm could be found at https://gist.github.com/ZhouGengmo/5b565f51adafcd911c0bc115b2ef027c.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 11 of its 38 references that are in the map. First posted on arXiv 14 Feb 2023.

Builds on

11 of 38 references.

References · 11

  1. 2022GeoDiff: a Geometric Diffusion Model for Molecular Conformation Generationcited by 511
  2. 2022Torsional Diffusion for Molecular Conformer Generationcited by 262
  3. 2020GEOM: Energy-annotated molecular conformations for property prediction and molecular generationcited by 238
  4. 2021ChemRL-GEM: Geometry Enhanced Molecular Representation Learning for Property Predictioncited by 204
  5. 2021Learning Gradient Fields for Molecular Conformation Generationcited by 162
  6. 2021GeoMol: Torsional Geometric Generation of Molecular 3D Conformer Ensemblescited by 106
  7. 2019Molecular geometry prediction using a deep generative graph neural networkcited by 91
  8. 2021Learning Neural Generative Dynamics for Molecular Conformation Generationcited by 87
  9. 2019A Generative Model for Molecular Distance Geometrycited by 73
  10. 2021An End-to-End Framework for Molecular Conformation Generation via Bilevel Programmingcited by 59
  11. 2022Direct Molecular Conformation Generationcited by 30

Built on by

Cited by year

Citing papers per year: 2023: 3; 2024: 2; 2025: 3.

8 papers.

  1. 2023CoarsenConf: Equivariant Coarsening with Aggregated Attention for Molecular Conformer Generationcited by 7
  2. 20253DMolFormer: A Dual-channel Framework for Structure-based Drug Discoverycited by 4
  3. 2023Molecular Conformation Generation via Shifting Scorescited by 2
  4. 2023Infinite Physical Monkey: Do Deep Learning Methods Really Perform Better in Conformation Generation?cited by 1
  5. 2024REBIND: Enhancing ground-state molecular conformation via force-based graph rewiringcited by 1
  6. 2024RapidDock: Unlocking Proteome-scale Molecular Dockingcited by 0
  7. 2025A standard transformer and attention with linear biases for molecular conformer generationcited by 0
  8. 2025Mamba-driven multi-perspective structural understanding for molecular ground-state conformation predictioncited by 0