ArticleJournal of cheminformatics2019
An exploration strategy improves the diversity of de novo ligands using deep reinforcement learning: a case for the adenosine A
Article in Journal of cheminformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
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Who cites it
38 citing papers in PubMed.
- Diversity Beats Size Scaling for Chemical Language Models.npj drug discovery · 2026Article
- Applying Deep-Learning-DrivenJournal of medicinal chemistry · 2026Article
- Scaffold-based evaluation metrics for fair comparison of molecular generators.Journal of cheminformatics · 2026Article
- Application of Reinforcement Learning Techniques in De Novo Drug Design: A Systematic Literature Review.Health science reports · 2026Article
- Artificial intelligence in drug discovery from advanced molecular representation to pipeline applications.Frontiers in bioinformatics · 2026Review
- Test-Time Training Scaling Laws for Chemical Exploration in Drug Design.Journal of chemical information and modeling · 2025Article
- ICVAE: Interpretable Conditional Variational Autoencoder for De Novo Molecular Design.International journal of molecular sciences · 2025Article
- Design of the Inhibitors for Pseudorabies Virus Replication by Reinforcement Learning from HSV-1 DNA Polymerase Inhibitors.ACS omega · 2025Article
- Diverse Hits in De Novo Molecule Design: Diversity-Based Comparison of Goal-Directed Generators.Journal of chemical information and modeling · 2024Article
- Adapt-cMolGPT: A Conditional Generative Pre-Trained Transformer with Adapter-Based Fine-Tuning for Target-Specific Molecular Generation.International journal of molecular sciences · 2024Article
- A comprehensive review of artificial intelligence for pharmacology research.Frontiers in genetics · 2024Review
- Artificial intelligence for natural product drug discovery.Nature reviews. Drug discovery · 2023Review
- LOGICS: Learning optimal generative distribution for designing de novo chemical structures.Journal of cheminformatics · 2023Article
- DrugEx: Deep Learning Models and Tools for Exploration of Drug-Like Chemical Space.Journal of chemical information and modeling · 2023Article
- MolFilterGAN: a progressively augmented generative adversarial network for triaging AI-designed molecules.Journal of cheminformatics · 2023Article
- DrugEx v3: scaffold-constrained drug design with graph transformer-based reinforcement learning.Journal of cheminformatics · 2023Article
- Universal Approach to De Novo Drug Design for Target Proteins Using Deep Reinforcement Learning.ACS omega · 2023Article
- Opportunities and challenges in application of artificial intelligence in pharmacology.Pharmacological reports : PR · 2023Review
- Multi-and many-objective optimization: present and future inFrontiers in chemistry · 2023Review
- Predicting chemical structure using reinforcement learning with a stack-augmented conditional variational autoencoder.Journal of cheminformatics · 2022Article
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Authors and funding
5 authors.
Funding
Abstract
Over the last 5 years deep learning has progressed tremendously in both image recognition and natural language processing. Now it is increasingly applied to other data rich fields. In drug discovery, recurrent neural networks (RNNs) have been shown to be an effective method to generate novel chemical structures in the form of SMILES. However, ligands generated by current methods have so far provided relatively low diversity and do not fully cover the whole chemical space occupied by known ligands. Here, we propose a new method (DrugEx) to discover de novo drug-like molecules. DrugEx is an RNN model (generator) trained through reinforcement learning which was integrated with a special exploration strategy. As a case study we applied our method to design ligands against the adenosine A
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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.