ArticleJournal of chemical information and modeling2024
ACEGEN: Reinforcement Learning of Generative Chemical Agents for Drug Discovery.
Article in Journal of chemical information and modeling, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
13 citing papers in PubMed.
- Targeting WEE1 kinase: an integrated machine learning-cheminformatics framework for ultra-large-scale virtual screening and novel inhibitor discovery.Journal of computer-aided molecular design · 2026Article
- Deep learning for small-molecule drug discovery: From molecular design to clinical translation.Journal of pharmaceutical analysis · 2026Review
- The architecture of computational antiviralism: a multi-scale framework from molecular targeting to viral ecosystem engineering.Molecular diversity · 2026Review
- Artificial intelligence revolutionizing CNS drug discovery and development.Drug discovery today · 2026Review
- A Reinforcement Learning-Guided Genetic Algorithm Integrating Medicinal Chemistry-Inspired Molecular Transformations.Journal of chemical information and modeling · 2026Article
- Test-Time Training Scaling Laws for Chemical Exploration in Drug Design.Journal of chemical information and modeling · 2025Article
- REINFORCE-ING Chemical Language Models for Drug Discovery.Journal of chemical information and modeling · 2025Article
- Artificial intelligence in bioinformatics: a survey.Briefings in bioinformatics · 2025Review
- In Search of Beautiful Molecules: A Perspective on Generative Modeling for Drug Design.Journal of chemical information and modeling · 2025Review
- Identification of nanomolar adenosine ANature communications · 2025Article
- Multimodal AI in Biomedicine: Pioneering the Future of Biomaterials, Diagnostics, and Personalized Healthcare.Nanomaterials (Basel, Switzerland) · 2025Review
- Exploration of the Application of Data-Driven and Generation Models in the Design of Thermoplastic Vulcanizate Rubbers.Polymers · 2025Article
- A review of large language models and autonomous agents in chemistry.Chemical science · 2025Review
Corrections and comments
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Authors and funding
13 authors.
Funding
Abstract
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 https://github.com/acellera/acegen-open and available for use under the MIT license.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.