mlsanityData to 14 Oct 2026

Deep Reinforcement Learning for De-Novo Drug Design

arXiv 1711.10907 ↗First posted 29 Nov 2017cs.AI cs.LG stat.MLcited by 161

Not on the map. Why?

Cited by 20 in Deep reinforcement learning methods, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

We propose a novel computational strategy for de novo design of molecules with desired properties termed ReLeaSE (Reinforcement Learning for Structural Evolution). Based on deep and reinforcement learning approaches, ReLeaSE integrates two deep neural networks - generative and predictive - that are trained separately but employed jointly to generate novel targeted chemical libraries. ReLeaSE employs simple representation of molecules by their SMILES strings only. Generative models are trained with stack-augmented memory network to produce chemically feasible SMILES strings, and predictive models are derived to forecast the desired properties of the de novo generated compounds. In the first phase of the method, generative and predictive models are trained separately with a supervised learning algorithm. In the second phase, both models are trained jointly with the reinforcement learning approach to bias the generation of new chemical structures towards those with the desired physical and/or biological properties. In the proof-of-concept study, we have employed the ReLeaSE method to design chemical libraries with a bias toward structural complexity or biased toward compounds with either maximal, minimal, or specific range of physical properties such as melting point or hydrophobicity, as well as to develop novel putative inhibitors of JAK2. The approach proposed herein can find a general use for generating targeted chemical libraries of novel compounds optimized for either a single desired property or multiple properties.

Key facts. Cited by 161 papers in the map (data through 14 Oct 2026). First posted on arXiv 29 Nov 2017.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2020: 1; 2021: 29; 2022: 32; 2023: 25; 2024: 34; 2025: 25; 2026: 15.

161 papers.

Sort:

By area:

  1. 2024Diffusion Policy Policy Optimizationcited by 280
  2. 2021MARS: Markov Molecular Sampling for Multi-objective Drug Discoverycited by 125
  3. 2021Geometric Deep Learning on Molecular Representationscited by 113
  4. 2022SELFIES and the future of molecular string representationscited by 76
  5. 2021Hit and Lead Discovery with Explorative RL and Fragment-based Molecule Generationcited by 68
  6. 2023TorchRL: A data-driven decision-making library for PyTorchcited by 60
  7. 2022Conformal Prediction Under Feedback Covariate Shift for Biomolecular Designcited by 54
  8. 2021Combining Latent Space and Structured Kernels for Bayesian Optimization over Combinatorial Spacescited by 45
  9. 2022Retrieval-based Controllable Molecule Generationcited by 44
  10. 2021Offline Model-Based Optimization via Normalized Maximum Likelihood Estimationcited by 38
  11. 2023A Survey on Graph Diffusion Models: Generative AI in Science for Molecule, Protein and Materialcited by 35
  12. 2021Learning Long-Term Reward Redistribution via Randomized Return Decompositioncited by 33
38 more
  1. 2021MEG: Generating Molecular Counterfactual Explanations for Deep Graph Networkscited by 32
  2. 2022Computer-Aided Multi-Objective Optimization in Small Molecule Discoverycited by 31
  3. 2021Artificial Intelligence in Drug Discovery: Applications and Techniquescited by 28
  4. 2024SINDy-RL: Interpretable and Efficient Model-Based Reinforcement Learningcited by 26
  5. 2023Domain-Agnostic Molecular Generation with Chemical Feedbackcited by 22
  6. 2022Deep Reinforcement Learning for Inverse Inorganic Materials Designcited by 19
  7. 2021GeneDisco: A Benchmark for Experimental Design in Drug Discoverycited by 18
  8. 2025NExT-Mol: 3D Diffusion Meets 1D Language Modeling for 3D Molecule Generationcited by 17
  9. 2022MGCVAE: Multi-objective Inverse Design via Molecular Graph Conditional Variational Autoencodercited by 16
  10. 2023Recent Advances and Applications of Machine Learning in Experimental Solid Mechanics: A Reviewcited by 15
  11. 2024DrugLLM: Open Large Language Model for Few-shot Molecule Generationcited by 15
  12. 2024Saturn: Sample-efficient Generative Molecular Design using Memory Manipulationcited by 15
  13. 2025Multi-domain Distribution Learning for De Novo Drug Designcited by 15
  14. 2021Assigning Confidence to Molecular Property Predictioncited by 14
  15. 2024A Practical Guide to Sample-based Statistical Distances for Evaluating Generative Models in Sciencecited by 14
  16. 2024ACEGEN: Reinforcement learning of generative chemical agents for drug discoverycited by 14
  17. 2024SmileyLlama: Modifying Large Language Models for Directed Chemical Space Explorationcited by 14
  18. 2025Reinforcement Fine-Tuning for Materials Designcited by 13
  19. 2022Offline Reinforcement Learning with Differentiable Function Approximation is Provably Efficientcited by 12
  20. 2025A Primer on Quantum Machine Learningcited by 12
  21. 2023Reinforcement Learning for Generative AI: A Surveycited by 11
  22. 2023Beyond Average Return in Markov Decision Processescited by 11
  23. 2024Koopman-Assisted Reinforcement Learningcited by 11
  24. 2024Failures Are Fated, But Can Be Faded: Characterizing and Mitigating Unwanted Behaviors in Large-Scale Vision and Language Modelscited by 11
  25. 2025MOFGPT: Generative Design of Metal-Organic Frameworks using Language Modelscited by 11
  26. 2025POLO: Preference-Guided Multi-Turn Reinforcement Learning for Lead Optimizationcited by 11
  27. 2022Flowsheet synthesis through hierarchical reinforcement learning and graph neural networkscited by 10
  28. 2023Generative Pretrained Autoregressive Transformer Graph Neural Network applied to the Analysis and Discovery of Novel Proteinscited by 10
  29. 2023Reinforcement Learning for Generative AI: State of the Art, Opportunities and Open Research Challengescited by 10
  30. 2022Accurate Machine Learned Quantum-Mechanical Force Fields for Biomolecular Simulationscited by 9
  31. 2024Cephalo: Multi-Modal Vision-Language Models for Bio-Inspired Materials Analysis and Designcited by 9
  32. 2021Full Gradient DQN Reinforcement Learning: A Provably Convergent Schemecited by 8
  33. 2021Deep reinforcement learning-designed radiofrequency waveform in MRIcited by 8
  34. 2022Evaluation Beyond Task Performance: Analyzing Concepts in AlphaZero in Hexcited by 8
  35. 2022Re-evaluating sample efficiency in de novo molecule generationcited by 8
  36. 2023Recent advances in the Self-Referencing Embedding Strings (SELFIES) librarycited by 8
  37. 2023Augmented Memory: Capitalizing on Experience Replay to Accelerate De Novo Molecular Designcited by 8
  38. 2023ChemSpaceAL: An Efficient Active Learning Methodology Applied to Protein-Specific Molecular Generationcited by 8

50 most cited of 161 All 161 (JSON)