mlsanityData to 7 Oct 2026

De novo Drug Design using Reinforcement Learning with Multiple GPT Agents

arXiv 2401.06155 ↗First posted 21 Dec 2023q-bio.BM cs.CE cs.LGcited by 19

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Abstract (arXiv)

De novo drug design is a pivotal issue in pharmacology and a new area of focus in AI for science research. A central challenge in this field is to generate molecules with specific properties while also producing a wide range of diverse candidates. Although advanced technologies such as transformer models and reinforcement learning have been applied in drug design, their potential has not been fully realized. Therefore, we propose MolRL-MGPT, a reinforcement learning algorithm with multiple GPT agents for drug molecular generation. To promote molecular diversity, we encourage the agents to collaborate in searching for desirable molecules in diverse directions. Our algorithm has shown promising results on the GuacaMol benchmark and exhibits efficacy in designing inhibitors against SARS-CoV-2 protein targets. The codes are available at: https://github.com/HXYfighter/MolRL-MGPT.

Key facts. Cited by 19 papers in the map (data through 7 Oct 2026). Builds on 28 of its 68 references that are in the map. First posted on arXiv 21 Dec 2023.

Builds on

28 of 68 references.

References · 28

  1. 2017Attention Is All You Needcited by 59,932
  2. 2020Language Models are Few-Shot Learnerscited by 32,199
  3. 2016Automatic chemical design using a data-driven continuous representation of moleculescited by 762
  4. 2018Junction Tree Variational Autoencoder for Molecular Graph Generationcited by 647
  5. 2021Flow Network based Generative Models for Non-Iterative Diverse Candidate Generationcited by 384
  6. 2018Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generationcited by 363
  7. 2018GuacaMol: Benchmarking Models for De Novo Molecular Designcited by 294
  8. 2018Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Modelscited by 270
  9. 2017Molecular De Novo Design through Deep Reinforcement Learningcited by 264
  10. 2018Optimization of Molecules via Deep Reinforcement Learningcited by 199
  11. 2019MAVEN: Multi-Agent Variational Explorationcited by 181
  12. 2017Learning to Plan Chemical Synthesescited by 175
16 more
  1. 2019Symmetry-adapted generation of 3d point sets for the targeted discovery of moleculescited by 171
  2. 2021MARS: Markov Molecular Sampling for Multi-objective Drug Discoverycited by 125
  3. 2017Objective-Reinforced Generative Adversarial Networks (ORGAN) for Sequence Generation Modelscited by 116
  4. 2018Multi-Objective De Novo Drug Design with Conditional Graph Generative Modelcited by 101
  5. 2021Learning to Extend Molecular Scaffolds with Structural Motifscited by 70
  6. 2021Hit and Lead Discovery with Explorative RL and Fragment-based Molecule Generationcited by 68
  7. 2022MolGenSurvey: A Systematic Survey in Machine Learning Models for Molecule Designcited by 66
  8. 2020Symmetry-Aware Actor-Critic for 3D Molecular Designcited by 55
  9. 2020BOSS: Bayesian Optimization over String Spacescited by 54
  10. 2020Reinforcement Learning for Molecular Design Guided by Quantum Mechanicscited by 51
  11. 2020Guiding Deep Molecular Optimization with Genetic Explorationcited by 51
  12. 2022Reinforced Genetic Algorithm for Structure-based Drug Designcited by 46
  13. 2023De Novo Molecular Generation via Connection-aware Motif Miningcited by 45
  14. 2022LIMO: Latent Inceptionism for Targeted Molecule Generationcited by 40
  15. 2021Artificial Intelligence in Drug Discovery: Applications and Techniquescited by 28
  16. 2022Tailoring Molecules for Protein Pockets: a Transformer-based Generative Solution for Structured-based Drug Designcited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 8; 2025: 9; 2026: 2.

19 papers.

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  1. 2024Empowering Biomedical Discovery with AI Agentscited by 117
  2. 2025Towards Scientific Intelligence: A Survey of LLM-based Scientific Agentscited by 96
  3. 2024Genetic-guided GFlowNets for Sample Efficient Molecular Optimizationcited by 36
  4. 2024Saturn: Sample-efficient Generative Molecular Design using Memory Manipulationcited by 14
  5. 2024From Generalist to Specialist: A Survey of Large Language Models for Chemistrycited by 11
  6. 2024Can LLMs Generate Diverse Molecules? Towards Alignment with Structural Diversitycited by 9
  7. 2024GraphXForm: Graph transformer for computer-aided molecular designcited by 6
  8. 2024Small Molecule Optimization with Large Language Modelscited by 5
  9. 20253DMolFormer: A Dual-channel Framework for Structure-based Drug Discoverycited by 4
  10. 2025De Novo Molecular Design Enabled by Direct Preference Optimization and Curriculum Learningcited by 4
  11. 2025Multi-level Value Alignment in Agentic AI Systems: Survey and Perspectivescited by 4
  12. 2025Leveraging Partial SMILES Validation Scheme for Enhanced Drug Design in Reinforcement Learning Frameworkscited by 3
7 more
  1. 2025Test-Time Training Scaling Laws for Chemical Exploration in Drug Designcited by 1
  2. 2025Large Language Models Transform Organic Synthesis From Reaction Prediction to Automationcited by 1
  3. 2025SpecMol: A Spectroscopy-Grounded Foundation Model for Multi-Task Molecular Learningcited by 1
  4. 2026COGTRL: Training LLMs for Scientific Discovery Assistance using Cognitive Traces via Reinforcement Learningcited by 1
  5. 2024Empirical Evidence for the Fragment level Understanding on Drug Molecular Structure of LLMscited by 0
  6. 2025Accelerating High-Efficiency Organic Photovoltaic Discovery via Pretrained Graph Neural Networks and Generative Reinforcement Learningcited by 0
  7. 2026Molecular LLM Agents: From Architectural Design to Scientific Autonomycited by 0