# Genetic algorithms are strong baselines for molecule generation

arXiv: https://arxiv.org/abs/2310.09267 · HTML: https://mlsanity.com/paper/2310.09267/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 24 references that are in the map.
- First posted on arXiv 13 Oct 2023.

## Builds on (most cited first, 14 of 14)

- [Junction Tree Variational Autoencoder for Molecular Graph Generation](https://mlsanity.com/paper/1802.04364/) (2018)
- [Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation](https://mlsanity.com/paper/1806.02473/) (2018)
- [GraphAF: a Flow-based Autoregressive Model for Molecular Graph Generation](https://mlsanity.com/paper/2001.09382/) (2020)
- [GuacaMol: Benchmarking Models for De Novo Molecular Design](https://mlsanity.com/paper/1811.09621/) (2018)
- [Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models](https://mlsanity.com/paper/1811.12823/) (2018)
- [MoFlow: An Invertible Flow Model for Generating Molecular Graphs](https://mlsanity.com/paper/2006.10137/) (2020)
- [GraphDF: A Discrete Flow Model for Molecular Graph Generation](https://mlsanity.com/paper/2102.01189/) (2021)
- [Sample Efficiency Matters: A Benchmark for Practical Molecular Optimization](https://mlsanity.com/paper/2206.12411/) (2022)
- [GraphNVP: An Invertible Flow Model for Generating Molecular Graphs](https://mlsanity.com/paper/1905.11600/) (2019)
- [GraphEBM: Molecular Graph Generation with Energy-Based Models](https://mlsanity.com/paper/2102.00546/) (2021)
- [MolecularRNN: Generating realistic molecular graphs with optimized properties](https://mlsanity.com/paper/1905.13372/) (2019)
- [MolGenSurvey: A Systematic Survey in Machine Learning Models for Molecule Design](https://mlsanity.com/paper/2203.14500/) (2022)
- [Categorical Normalizing Flows via Continuous Transformations](https://mlsanity.com/paper/2006.09790/) (2020)
- [Modular Flows: Differential Molecular Generation](https://mlsanity.com/paper/2210.06032/) (2022)

## Built on by (most cited first, 30 of 31)

- [GenMol: A Drug Discovery Generalist with Discrete Diffusion](https://mlsanity.com/paper/2501.06158/) (2025)
- [Graph Diffusion Transformers for Multi-Conditional Molecular Generation](https://mlsanity.com/paper/2401.13858/) (2024)
- [Genetic-guided GFlowNets for Sample Efficient Molecular Optimization](https://mlsanity.com/paper/2402.05961/) (2024)
- [A survey and benchmark of high-dimensional Bayesian optimization of discrete sequences](https://mlsanity.com/paper/2406.04739/) (2024)
- [Molecule Generation with Fragment Retrieval Augmentation](https://mlsanity.com/paper/2411.12078/) (2024)
- [Generative Artificial Intelligence for Navigating Synthesizable Chemical Space](https://mlsanity.com/paper/2410.03494/) (2024)
- [Saturn: Sample-efficient Generative Molecular Design using Memory Manipulation](https://mlsanity.com/paper/2405.17066/) (2024)
- [NMR-Solver: Automated Structure Elucidation via Large-Scale Spectral Matching and Physics-Guided Fragment Optimization](https://mlsanity.com/paper/2509.00640/) (2025)
- [Diagnosing and fixing common problems in Bayesian optimization for molecule design](https://mlsanity.com/paper/2406.07709/) (2024)
- [Refine Drugs, Don't Complete Them: Uniform-Source Discrete Flows for Fragment-Based Drug Discovery](https://mlsanity.com/paper/2509.26405/) (2025)
- [Graph-GRPO: Training Graph Flow Models with Reinforcement Learning](https://mlsanity.com/paper/2603.10395/) (2026)
- [Active learning for affinity prediction of antibodies](https://mlsanity.com/paper/2406.07263/) (2024)
- [Gradient GA: Gradient Genetic Algorithm for Drug Molecular Design](https://mlsanity.com/paper/2502.09860/) (2025)
- [A Genetic Algorithm for Navigating Synthesizable Molecular Spaces](https://mlsanity.com/paper/2509.20719/) (2025)
- [MADD: Multi-Agent Drug Discovery Orchestra](https://mlsanity.com/paper/2511.08217/) (2025)
- [Efficient and Programmable Exploration of Synthesizable Chemical Space](https://mlsanity.com/paper/2512.00384/) (2025)
- [VALID-Mol: a Systematic Framework for Validated LLM-Assisted Molecular Design](https://mlsanity.com/paper/2506.23339/) (2025)
- [Using GNN property predictors as molecule generators](https://mlsanity.com/paper/2406.03278/) (2024)
- [Hybrid Generative AI for De Novo Design of Co-Crystals with Enhanced Tabletability](https://mlsanity.com/paper/2410.17005/) (2024)
- [Conditional Latent Space Molecular Scaffold Optimization for Accelerated Molecular Design](https://mlsanity.com/paper/2411.01423/) (2024)
- [Controllable Molecular Generative Foundation Models](https://mlsanity.com/paper/2605.15354/) (2026)
- [Gradient-based Design of Computational Granular Crystals](https://mlsanity.com/paper/2404.04825/) (2024)
- [What Ails Generative Structure-based Drug Design: Expressivity is Too Little or Too Much?](https://mlsanity.com/paper/2408.06050/) (2024)
- [MolLIBRA: Genetic Molecular Optimization with Multi-Fingerprint Surrogates and Text-Molecule Aligned Critic](https://mlsanity.com/paper/2602.07002/) (2026)
- [Amortized Molecular Optimization via Group Relative Policy Optimization](https://mlsanity.com/paper/2602.12162/) (2026)
- [MolRGen: A Training and Evaluation Setting for De Novo Molecular Generation with Reasonning Models](https://mlsanity.com/paper/2603.18256/) (2026)
- [GLACIER: Rethinking Mass Spectrum Prediction as an Object Detection Problem](https://mlsanity.com/paper/2606.29161/) (2026)
- [Beyond Drug Discovery: The Nanotechnology Molecular Optimization (NMO) Benchmark](https://mlsanity.com/paper/2606.30170/) (2026)
- [Adopting Reinforcement Learning with Verifiable Rewards for Molecular Generation](https://mlsanity.com/paper/2607.19044/) (2026)
- [PGFS++: Molecular Property Improvement under Synthesis and Diversity Constraints](https://mlsanity.com/paper/2608.19121/) (2026)

## Abstract (arXiv)

Generating molecules, both in a directed and undirected fashion, is a huge part of the drug discovery pipeline. Genetic algorithms (GAs) generate molecules by randomly modifying known molecules. In this paper we show that GAs are very strong algorithms for such tasks, outperforming many complicated machine learning methods: a result which many researchers may find surprising. We therefore propose insisting during peer review that new algorithms must have some clear advantage over GAs, which we call the GA criterion. Ultimately our work suggests that a lot of research in molecule generation should be re-assessed.