ArticleScientific reports2020
Autonomous molecule generation using reinforcement learning and docking to develop potential novel inhibitors.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed, 93 citations in OpenAlex.
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- Sampling out-of-distribution chemical spaces via Bayesian flow.Journal of cheminformatics · 2026Article
- Assessing the factors influencing the quality of pocket-conditioned 3D generative models.Journal of cheminformatics · 2026Article
- MolOrgGPT:Journal of chemical information and modeling · 2026Article
- Recent Advances and Emerging Directions in Machine Learning-Based Breast Cancer Drug Discovery: A Comprehensive Review.Breast cancer (Dove Medical Press) · 2026Review
- Sample-efficient generative molecular design using memory manipulation.Nature machine intelligence · 2026Article
- SHARP: Generating Synthesizable Molecules via Fragment-Based Hierarchical Action-Space Reinforcement Learning for Pareto Optimization.Journal of chemical information and modeling · 2025Article
- PURE: policy-guided unbiased REpresentations for structure-constrained molecular generation.Journal of cheminformatics · 2025Article
- Integrated AI and machine learning pipeline identifies novel WEE1 kinase inhibitors for targeted cancer therapy.Molecular diversity · 2025Article
- Self-awareness of retrosynthesis via chemically inspired contrastive learning for reinforced molecule generation.Briefings in bioinformatics · 2025Article
- Mining for Potent Inhibitors through Artificial Intelligence and Physics: A Unified Methodology for Ligand Based and Structure Based Drug Design.Journal of chemical information and modeling · 2024Article
- Article
- Improving Molecule Generation and Drug Discovery with a Knowledge-enhanced Generative Model.ArXiv · 2024Article
- Structure-aware dual-target drug design through collaborative learning of pharmacophore combination and molecular simulation.Chemical science · 2024Article
- Geometric Deep Learning for Structure-Based Ligand Design.ACS central science · 2023Article
- A flexible data-free framework for structure-basedChemical science · 2023Article
- Improving drug discovery with a hybrid deep generative model using reinforcement learning trained on a Bayesian docking approximation.Journal of computer-aided molecular design · 2023Article
- Generating Potential Protein-Protein Interaction Inhibitor Molecules Based on Physicochemical Properties.Molecules (Basel, Switzerland) · 2023Article
- Augmented Hill-Climb increases reinforcement learning efficiency for language-based de novo molecule generation.Journal of cheminformatics · 2022Article
- DOCKSTRING: Easy Molecular Docking Yields Better Benchmarks for Ligand Design.Journal of chemical information and modeling · 2022Review
Corrections and comments
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Authors and funding
2 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We developed a computational method named Molecule Optimization by Reinforcement Learning and Docking (MORLD) that automatically generates and optimizes lead compounds by combining reinforcement learning and docking to develop predicted novel inhibitors. This model requires only a target protein structure and directly modifies ligand structures to obtain higher predicted binding affinity for the target protein without any other training data. Using MORLD, we were able to generate potential novel inhibitors against discoidin domain receptor 1 kinase (DDR1) in less than 2 days on a moderate computer. We also demonstrated MORLD's ability to generate predicted novel agonists for the D
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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.