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Augmenting Genetic Algorithms with Deep Neural Networks for Exploring the Chemical Space

arXiv 1909.11655 ↗First posted 25 Sep 2019cs.NE cs.LG physics.chem-phcited by 66

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Abstract (arXiv)

Challenges in natural sciences can often be phrased as optimization problems. Machine learning techniques have recently been applied to solve such problems. One example in chemistry is the design of tailor-made organic materials and molecules, which requires efficient methods to explore the chemical space. We present a genetic algorithm (GA) that is enhanced with a neural network (DNN) based discriminator model to improve the diversity of generated molecules and at the same time steer the GA. We show that our algorithm outperforms other generative models in optimization tasks. We furthermore present a way to increase interpretability of genetic algorithms, which helped us to derive design principles.

Key facts. Cited by 66 papers in the map (data through 7 Oct 2026). First posted on arXiv 25 Sep 2019.

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Citing papers per year: 2021: 16; 2022: 15; 2023: 9; 2024: 10; 2025: 8; 2026: 8.

66 papers.

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  1. 2021MARS: Markov Molecular Sampling for Multi-objective Drug Discoverycited by 125
  2. 2022Accelerating Bayesian Optimization for Biological Sequence Design with Denoising Autoencoderscited by 99
  3. 2022Exploring Chemical Space with Score-based Out-of-distribution Generationcited by 95
  4. 2022Local Latent Space Bayesian Optimization over Structured Inputscited by 78
  5. 2022SELFIES and the future of molecular string representationscited by 76
  6. 2022Equivariant Energy-Guided SDE for Inverse Molecular Designcited by 72
  7. 2022MolGenSurvey: A Systematic Survey in Machine Learning Models for Molecule Designcited by 66
  8. 2021Molecule Generation by Principal Subgraph Mining and Assemblingcited by 53
  9. 2021Differentiable Scaffolding Tree for Molecular Optimizationcited by 53
  10. 2022Reinforced Genetic Algorithm for Structure-based Drug Designcited by 46
  11. 2021Amortized Tree Generation for Bottom-up Synthesis Planning and Synthesizable Molecular Designcited by 44
  12. 2022Retrieval-based Controllable Molecule Generationcited by 44
38 more
  1. 2022LIMO: Latent Inceptionism for Targeted Molecule Generationcited by 40
  2. 2024Genetic-guided GFlowNets for Sample Efficient Molecular Optimizationcited by 36
  3. 2022Group SELFIES: A Robust Fragment-Based Molecular String Representationcited by 32
  4. 2021JANUS: Parallel Tempered Genetic Algorithm Guided by Deep Neural Networks for Inverse Molecular Designcited by 31
  5. 2024DecompOpt: Controllable and Decomposed Diffusion Models for Structure-based Molecular Optimizationcited by 27
  6. 2024Molecule Generation with Fragment Retrieval Augmentationcited by 22
  7. 2023Domain-Agnostic Molecular Generation with Chemical Feedbackcited by 21
  8. 2021Generative Chemical Transformer: Neural Machine Learning of Molecular Geometric Structures from Chemical Language via Attentioncited by 20
  9. 2022Controllable Data Generation by Deep Learning: A Reviewcited by 20
  10. 2023Drug Discovery with Dynamic Goal-aware Fragmentscited by 19
  11. 2021Assigning Confidence to Molecular Property Predictioncited by 14
  12. 2024Saturn: Sample-efficient Generative Molecular Design using Memory Manipulationcited by 14
  13. 2022Molecule Generation for Drug Design: a Graph Learning Perspectivecited by 13
  14. 2021Unifying Likelihood-free Inference with Black-box Optimization and Beyondcited by 10
  15. 2022Improving Small Molecule Generation using Mutual Information Machinecited by 10
  16. 2023Deep Learning Methods for Small Molecule Drug Discovery: A Surveycited by 10
  17. 2024Diagnosing and fixing common problems in Bayesian optimization for molecule designcited by 9
  18. 2023Molecule Design by Latent Space Energy-Based Modeling and Gradual Distribution Shiftingcited by 8
  19. 2023Molecule Design by Latent Prompt Transformercited by 8
  20. 2021Golem: An algorithm for robust experiment and process optimizationcited by 6
  21. 2021Fragment-based Sequential Translation for Molecular Optimizationcited by 6
  22. 2023The power of motifs as inductive bias for learning molecular distributionscited by 6
  23. 2024Empower Structure-Based Molecule Optimization with Gradient Guided Bayesian Flow Networkscited by 6
  24. 2025ExLLM: Experience-Enhanced LLM Optimization for Molecular Design and Beyondcited by 6
  25. 2025Gradient GA: Gradient Genetic Algorithm for Drug Molecular Designcited by 4
  26. 2025A Genetic Algorithm for Navigating Synthesizable Molecular Spacescited by 4
  27. 2025Refine Drugs, Don't Complete Them: Uniform-Source Discrete Flows for Fragment-Based Drug Discoverycited by 4
  28. 2021Sample-Efficient Generation of Novel Photo-acid Generator Molecules using a Deep Generative Modelcited by 3
  29. 2023PyTrial: Machine Learning Software and Benchmark for Clinical Trial Applicationscited by 3
  30. 2024A Review on Fragment-based De Novo 2D Molecule Generationcited by 3
  31. 2025Survey on Recent Progress of AI for Chemistry: Methods, Applications, and Opportunitiescited by 3
  32. 2025Pretraining Generative Flow Networks with Inexpensive Rewards for Molecular Graph Generationcited by 3
  33. 2025Toward Closed-loop Molecular Discovery via Language Model, Property Alignment and Strategic Searchcited by 3
  34. 2021Artificial Intelligence based Autonomous Molecular Design for Medical Therapeutic: A Perspectivecited by 2
  35. 2022Actively Learning Costly Reward Functions for Reinforcement Learningcited by 2
  36. 2026On the Reliability of AI Methods in Drug Discovery: Evaluation of Boltz-2 for Structure and Binding Affinity Predictioncited by 2
  37. 2021A reproducibility study of "Augmenting Genetic Algorithms with Deep Neural Networks for Exploring the Chemical Space"cited by 1
  38. 2021An In-depth Summary of Recent Artificial Intelligence Applications in Drug Designcited by 1

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