ArticleJournal of cheminformatics2020
Memory-assisted reinforcement learning for diverse molecular de novo design.
Article in Journal of cheminformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers.
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Who cites it
48 citing papers in PubMed.
- The impact of reward scalarization and weight scheduling on optimization dynamics in multi-objective molecular design.Journal of cheminformatics · 2026Article
- PyMolGen: Database-Driven Molecular Generation of Drug-Like Compounds.Journal of chemical information and modeling · 2026Article
- Diversity Beats Size Scaling for Chemical Language Models.npj drug discovery · 2026Article
- A geometry-aware generative framework integrating GPS-VAE and Transformer-SELFIES for structure-based de novo drug design.Journal of molecular modeling · 2026Article
- Artificial Intelligence Across the Drug Development Lifecycle.Medical sciences (Basel, Switzerland) · 2026Review
- Sample-efficient generative molecular design using memory manipulation.Nature machine intelligence · 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
- Integrating artificial intelligence into small molecule development for precision cancer immunomodulation therapy.npj drug discovery · 2025Review
- Generative design of singlet fission materials leveraging a fragment-oriented database.Chemical science · 2025Article
- Article
- Generative artificial intelligence based models optimization towards molecule design enhancement.Journal of cheminformatics · 2025Review
- Generative Deep Learning for de Novo Drug Design─A Chemical Space Odyssey.Journal of chemical information and modeling · 2025Review
- A Deep-Learning-Aided Drug Screening Based on Visualization of a Hidden Layer as Chemical Space.ACS medicinal chemistry letters · 2025Article
- A Deep Generative Model for the Inverse Design of Transition Metal Ligands and Complexes.JACS Au · 2025Article
- Activity cliff-aware reinforcement learning for de novo drug design.Journal of cheminformatics · 2025Article
- Integrating QSAR modelling with reinforcement learning for Syk inhibitor discovery.Journal of cheminformatics · 2025Article
- CardioGenAI: a machine learning-based framework for re-engineering drugs for reduced hERG liability.Journal of cheminformatics · 2025Article
- Design of the Inhibitors for Pseudorabies Virus Replication by Reinforcement Learning from HSV-1 DNA Polymerase Inhibitors.ACS omega · 2025Article
- Generation of Rational Drug-like Molecular Structures Through a Multiple-Objective Reinforcement Learning Framework.Molecules (Basel, Switzerland) · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
In de novo molecular design, recurrent neural networks (RNN) have been shown to be effective methods for sampling and generating novel chemical structures. Using a technique called reinforcement learning (RL), an RNN can be tuned to target a particular section of chemical space with optimized desirable properties using a scoring function. However, ligands generated by current RL methods so far tend to have relatively low diversity, and sometimes even result in duplicate structures when optimizing towards desired properties. Here, we propose a new method to address the low diversity issue in RL for molecular design. Memory-assisted RL is an extension of the known RL, with the introduction of a so-called memory unit. As proof of concept, we applied our method to generate structures with a desired AlogP value. In a second case study, we applied our method to design ligands for the dopamine type 2 receptor and the 5-hydroxytryptamine type 1A receptor. For both receptors, a machine learning model was developed to predict whether generated molecules were active or not for the receptor. In both case studies, it was found that memory-assisted RL led to the generation of more compounds predicted to be active having higher chemical diversity, thus achieving better coverage of chemical space of known ligands compared to established RL methods.
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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.