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Optimization of Molecules via Deep Reinforcement Learning

arXiv 1810.08678 ↗First posted 19 Oct 2018cs.LG cs.AI stat.MLcited by 199

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Abstract (arXiv)

We present a framework, which we call Molecule Deep Q-Networks (MolDQN), for molecule optimization by combining domain knowledge of chemistry and state-of-the-art reinforcement learning techniques (double Q-learning and randomized value functions). We directly define modifications on molecules, thereby ensuring 100% chemical validity. Further, we operate without pre-training on any dataset to avoid possible bias from the choice of that set. Inspired by problems faced during medicinal chemistry lead optimization, we extend our model with multi-objective reinforcement learning, which maximizes drug-likeness while maintaining similarity to the original molecule. We further show the path through chemical space to achieve optimization for a molecule to understand how the model works.

Key facts. Cited by 199 papers in the map (data through 7 Oct 2026). First posted on arXiv 19 Oct 2018.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 38; 2022: 38; 2023: 37; 2024: 37; 2025: 21; 2026: 28.

199 papers.

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  1. 2021Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Developmentcited by 258
  2. 2021A Practical Guide to Multi-Objective Reinforcement Learning and Planningcited by 240
  3. 20233D Equivariant Diffusion for Target-Aware Molecule Generation and Affinity Predictioncited by 176
  4. 2023Machine Learning for Synthetic Data Generation: A Reviewcited by 118
  5. 2021Geometric Deep Learning on Molecular Representationscited by 113
  6. 2022Exploring Chemical Space with Score-based Out-of-distribution Generationcited by 95
  7. 2024Understanding Reinforcement Learning-Based Fine-Tuning of Diffusion Models: A Tutorial and Reviewcited by 92
  8. 2024LLM and Simulation as Bilevel Optimizers: A New Paradigm to Advance Physical Scientific Discoverycited by 82
  9. 2023Generative Diffusion Models on Graphs: Methods and Applicationscited by 79
  10. 2022Local Latent Space Bayesian Optimization over Structured Inputscited by 78
  11. 2022Equivariant Energy-Guided SDE for Inverse Molecular Designcited by 72
  12. 2021Hit and Lead Discovery with Explorative RL and Fragment-based Molecule Generationcited by 68
38 more
  1. 2022MolGenSurvey: A Systematic Survey in Machine Learning Models for Molecule Designcited by 66
  2. 2023MolDiff: Addressing the Atom-Bond Inconsistency Problem in 3D Molecule Diffusion Generationcited by 65
  3. 2021Differentiable Scaffolding Tree for Molecular Optimizationcited by 53
  4. 2021Improving black-box optimization in VAE latent space using decoder uncertaintycited by 51
  5. 2022Reinforced Genetic Algorithm for Structure-based Drug Designcited by 46
  6. 2021Combining Latent Space and Structured Kernels for Bayesian Optimization over Combinatorial Spacescited by 45
  7. 2021Amortized Tree Generation for Bottom-up Synthesis Planning and Synthesizable Molecular Designcited by 44
  8. 2022Retrieval-based Controllable Molecule Generationcited by 44
  9. 2022LIMO: Latent Inceptionism for Targeted Molecule Generationcited by 40
  10. 2024Genetic-guided GFlowNets for Sample Efficient Molecular Optimizationcited by 36
  11. 2021Learning Long-Term Reward Redistribution via Randomized Return Decompositioncited by 33
  12. 2021MEG: Generating Molecular Counterfactual Explanations for Deep Graph Networkscited by 32
  13. 2021JANUS: Parallel Tempered Genetic Algorithm Guided by Deep Neural Networks for Inverse Molecular Designcited by 31
  14. 2022Computer-Aided Multi-Objective Optimization in Small Molecule Discoverycited by 31
  15. 2022A Survey on Graph Counterfactual Explanations: Definitions, Methods, Evaluation, and Research Challengescited by 30
  16. 2021Artificial Intelligence in Drug Discovery: Applications and Techniquescited by 28
  17. 2024DecompOpt: Controllable and Decomposed Diffusion Models for Structure-based Molecular Optimizationcited by 27
  18. 2021Is High Variance Unavoidable in RL? A Case Study in Continuous Controlcited by 26
  19. 2022Bidirectional Generation of Structure and Properties Through a Single Molecular Foundation Modelcited by 25
  20. 2025LIDDIA: Language-based Intelligent Drug Discovery Agentcited by 24
  21. 2022Graph Neural Networks for Moleculescited by 21
  22. 2023Domain-Agnostic Molecular Generation with Chemical Feedbackcited by 21
  23. 2022Controllable Data Generation by Deep Learning: A Reviewcited by 20
  24. 2023Drug Discovery with Dynamic Goal-aware Fragmentscited by 19
  25. 2023De novo Drug Design using Reinforcement Learning with Multiple GPT Agentscited by 19
  26. 2022Deep Reinforcement Learning for Inverse Inorganic Materials Designcited by 18
  27. 2022Proactive Robot Assistance via Spatio-Temporal Object Modelingcited by 18
  28. 2023Searching for High-Value Molecules Using Reinforcement Learning and Transformerscited by 18
  29. 2023Advancing Bayesian Optimization via Learning Correlated Latent Spacecited by 18
  30. 2023Goal-conditioned GFlowNets for Controllable Multi-Objective Molecular Designcited by 17
  31. 2024Graph Reinforcement Learning for Combinatorial Optimization: A Survey and Unifying Perspectivecited by 16
  32. 2025Multi-domain Distribution Learning for De Novo Drug Designcited by 15
  33. 2021Assigning Confidence to Molecular Property Predictioncited by 14
  34. 2023MatSciML: A Broad, Multi-Task Benchmark for Solid-State Materials Modelingcited by 14
  35. 2024SmileyLlama: Modifying Large Language Models for Directed Chemical Space Explorationcited by 14
  36. 2022Molecule Generation for Drug Design: a Graph Learning Perspectivecited by 13
  37. 2022Multi-Objective Latent Space Optimization of Generative Molecular Design Modelscited by 13
  38. 2022Near-Optimal Deployment Efficiency in Reward-Free Reinforcement Learning with Linear Function Approximationcited by 13

50 most cited of 199 All 199 (JSON)