mlsanityData to 7 Oct 2026

ChemGAN challenge for drug discovery: can AI reproduce natural chemical diversity?

arXiv 1708.08227 ↗First posted 28 Aug 2017stat.ML cs.AI cs.LGcited by 21

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Abstract (arXiv)

Generating molecules with desired chemical properties is important for drug discovery. The use of generative neural networks is promising for this task. However, from visual inspection, it often appears that generated samples lack diversity. In this paper, we quantify this internal chemical diversity, and we raise the following challenge: can a nontrivial AI model reproduce natural chemical diversity for desired molecules? To illustrate this question, we consider two generative models: a Reinforcement Learning model and the recently introduced ORGAN. Both fail at this challenge. We hope this challenge will stimulate research in this direction.

Key facts. Cited by 21 papers in the map (data through 7 Oct 2026). First posted on arXiv 28 Aug 2017.

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Citing papers per year: 2020: 1; 2021: 2; 2022: 4; 2023: 4; 2024: 2; 2025: 3; 2026: 5.

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  1. 2022The Vendi Score: A Diversity Evaluation Metric for Machine Learningcited by 299
  2. 2021Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Developmentcited by 258
  3. 2022MolGenSurvey: A Systematic Survey in Machine Learning Models for Molecule Designcited by 66
  4. 2023Cousins Of The Vendi Score: A Family Of Similarity-Based Diversity Metrics For Science And Machine Learningcited by 24
  5. 2023Searching for High-Value Molecules Using Reinforcement Learning and Transformerscited by 18
  6. 2025ChemMLLM: Chemical Multimodal Large Language Modelcited by 16
  7. 2025A Survey of Large Language Models for Text-Guided Molecular Discovery: from Molecule Generation to Optimizationcited by 12
  8. 2024Structure-based Drug Design Benchmark: Do 3D Methods Really Dominate?cited by 7
  9. 2023The power of motifs as inductive bias for learning molecular distributionscited by 6
  10. 2022Nonmyopic Multiclass Active Search with Diminishing Returns for Diverse Discoverycited by 4
  11. 2023How to Do Machine Learning with Small Data? -- A Review from an Industrial Perspectivecited by 3
  12. 2020Deep Inverse Reinforcement Learning for Structural Evolution of Small Moleculescited by 2
9 more
  1. 2025QUEST: Quality-aware Semi-supervised Table Extraction for Business Documentscited by 2
  2. 2026Beyond Affinity: A Benchmark of 1D, 2D, and 3D Methods Reveals Critical Trade-offs in Structure-Based Drug Designcited by 2
  3. 2021A reproducibility study of "Augmenting Genetic Algorithms with Deep Neural Networks for Exploring the Chemical Space"cited by 1
  4. 2022Hybrid Quantum Generative Adversarial Networks for Molecular Simulation and Drug Discoverycited by 0
  5. 2024Sculpting Molecules in Text-3D Space: A Flexible Substructure Aware Framework for Text-Oriented Molecular Optimizationcited by 0
  6. 2026Generative Chemical Language Models for Energetic Materials Discoverycited by 0
  7. 2026Evaluating the Diversity of AI-Generated Content with Diversity Profilescited by 0
  8. 2026Recovering molecules from coarse-grained beads: free-energy-conditioned generative backmapping across chemical spacecited by 0
  9. 2026OPDiv: Optimal Selection of Top-K High-Scoring, Diverse Compoundscited by 0