mlsanityData to 7 Oct 2026

ACEGEN: Reinforcement learning of generative chemical agents for drug discovery

arXiv 2405.04657 ↗First posted 7 May 2024cs.LG cs.AI q-bio.BMcited by 14

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Abstract (arXiv)

In recent years, reinforcement learning (RL) has emerged as a valuable tool in drug design, offering the potential to propose and optimize molecules with desired properties. However, striking a balance between capabilities, flexibility, reliability, and efficiency remains challenging due to the complexity of advanced RL algorithms and the significant reliance on specialized code. In this work, we introduce ACEGEN, a comprehensive and streamlined toolkit tailored for generative drug design, built using TorchRL, a modern RL library that offers thoroughly tested reusable components. We validate ACEGEN by benchmarking against other published generative modeling algorithms and show comparable or improved performance. We also show examples of ACEGEN applied in multiple drug discovery case studies. ACEGEN is accessible at urlhttps://github.com/acellera/acegen-open and available for use under the MIT license.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). Builds on 25 of its 71 references that are in the map. First posted on arXiv 7 May 2024.

Builds on

25 of 71 references.

References · 25

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  2. 2022Training language models to follow instructions with human feedbackcited by 15,712
  3. 2017Proximal Policy Optimization Algorithmscited by 14,441
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13 more
  1. 2022Teaching language models to support answers with verified quotescited by 290
  2. 2018Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Modelscited by 270
  3. 2017Molecular De Novo Design through Deep Reinforcement Learningcited by 264
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 3; 2025: 8; 2026: 3.

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  1. 2024A Review of Large Language Models and Autonomous Agents in Chemistrycited by 96
  2. 2025An Empirical Study of Agent Developer Practices in AI Agent Frameworkscited by 14
  3. 2024Aligning Transformers with Continuous Feedback via Energy Rank Alignmentcited by 6
  4. 2024Small Molecule Optimization with Large Language Modelscited by 5
  5. 2025Refine Drugs, Don't Complete Them: Uniform-Source Discrete Flows for Fragment-Based Drug Discoverycited by 4
  6. 2025REINFORCE-ING Chemical Language Models for Drug Discoverycited by 3
  7. 2025Discrete Compositional Generation via General Soft Operators and Robust Reinforcement Learningcited by 2
  8. 2025Test-Time Training Scaling Laws for Chemical Exploration in Drug Designcited by 1
  9. 2025Generative molecule evolution using 3D pharmacophore for efficient Structure-Based Drug Designcited by 1
  10. 2025NovoMolGen: Rethinking Molecular Language Model Pretrainingcited by 0
  11. 2025Valid Property-Enhanced Contrastive Learning for Targeted Optimization & Resampling for Novel Drug Designcited by 0
  12. 2026Fairness Definitions and Metrics in Deep Reinforcement Learning for Drug Discovery in Healthcare: A Rapid Evidence Reviewcited by 0
2 more
  1. 2026Uncertainty-aware reinforcement learning for chemical language modelscited by 0
  2. 2026A Systematic Evaluation of Molecule Generation Models for De Novo Drug Design: From Benchmarks to Practical Insightscited by 0