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

arXiv: https://arxiv.org/abs/1708.08227 · HTML: https://mlsanity.com/paper/1708.08227/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Aug 2017.

## Built on by (most cited first, 21 of 21)

- [The Vendi Score: A Diversity Evaluation Metric for Machine Learning](https://mlsanity.com/paper/2210.02410/) (2022)
- [Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development](https://mlsanity.com/paper/2102.09548/) (2021)
- [MolGenSurvey: A Systematic Survey in Machine Learning Models for Molecule Design](https://mlsanity.com/paper/2203.14500/) (2022)
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- [Searching for High-Value Molecules Using Reinforcement Learning and Transformers](https://mlsanity.com/paper/2310.02902/) (2023)
- [ChemMLLM: Chemical Multimodal Large Language Model](https://mlsanity.com/paper/2505.16326/) (2025)
- [A Survey of Large Language Models for Text-Guided Molecular Discovery: from Molecule Generation to Optimization](https://mlsanity.com/paper/2505.16094/) (2025)
- [Structure-based Drug Design Benchmark: Do 3D Methods Really Dominate?](https://mlsanity.com/paper/2406.03403/) (2024)
- [The power of motifs as inductive bias for learning molecular distributions](https://mlsanity.com/paper/2306.17246/) (2023)
- [Nonmyopic Multiclass Active Search with Diminishing Returns for Diverse Discovery](https://mlsanity.com/paper/2202.03593/) (2022)
- [How to Do Machine Learning with Small Data? -- A Review from an Industrial Perspective](https://mlsanity.com/paper/2311.07126/) (2023)
- [Deep Inverse Reinforcement Learning for Structural Evolution of Small Molecules](https://mlsanity.com/paper/2008.11804/) (2020)
- [QUEST: Quality-aware Semi-supervised Table Extraction for Business Documents](https://mlsanity.com/paper/2506.14568/) (2025)
- [Beyond Affinity: A Benchmark of 1D, 2D, and 3D Methods Reveals Critical Trade-offs in Structure-Based Drug Design](https://mlsanity.com/paper/2601.14283/) (2026)
- [A reproducibility study of "Augmenting Genetic Algorithms with Deep Neural Networks for Exploring the Chemical Space"](https://mlsanity.com/paper/2102.00700/) (2021)
- [Hybrid Quantum Generative Adversarial Networks for Molecular Simulation and Drug Discovery](https://mlsanity.com/paper/2212.07826/) (2022)
- [Sculpting Molecules in Text-3D Space: A Flexible Substructure Aware Framework for Text-Oriented Molecular Optimization](https://mlsanity.com/paper/2403.03425/) (2024)
- [Generative Chemical Language Models for Energetic Materials Discovery](https://mlsanity.com/paper/2604.03304/) (2026)
- [Evaluating the Diversity of AI-Generated Content with Diversity Profiles](https://mlsanity.com/paper/2608.17731/) (2026)
- [Recovering molecules from coarse-grained beads: free-energy-conditioned generative backmapping across chemical space](https://mlsanity.com/paper/2609.04432/) (2026)
- [OPDiv: Optimal Selection of Top-K High-Scoring, Diverse Compounds](https://mlsanity.com/paper/2609.28665/) (2026)

## 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.