ArticleACS omega2020
Molecular Design in Synthetically Accessible Chemical Space via Deep Reinforcement Learning.
Article in ACS omega, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
What it found
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The trial behind it
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Who cites it
28 citing papers in PubMed.
- Strategy and efficiency-redefined discovery of novel nanomolar tyrosinase inhibitors: AI de novo molecular generation + expert-guided structural optimization.Journal of advanced research · 2026Article
- Generating multimillion chemical space based on the Ugi four-center three-component reaction with oxocarboxylic acids.Molecular diversity · 2026Article
- SyntheMol-RL: a flexible reinforcement learning framework for designing easily synthesizable antibiotics.Molecular systems biology · 2026Article
- A Transformer for Reaction-Aware Compound Explorations with GFlowNet in QSAR-Guided Molecular Design.Journal of chemical information and modeling · 2026Article
- Enabling Synthetically Feasible Molecular Editing in Drug Discovery via Reaction-Regulated Graph-Based Genetic Algorithms.JACS Au · 2026Article
- NaviDiv: a web app for monitoring chemical diversity in generative molecular design.Digital discovery · 2026Article
- Molecular Design with Artificial Intelligence: Progress and Perspectives for Small Molecules.Chemical reviews · 2026Review
- Rethinking Nature's Pharmacy: AI Era and Natural Product Drug Discovery.Pharmaceuticals (Basel, Switzerland) · 2026Review
- Artificial Intelligence for Natural Products Drug Discovery in Neurodegenerative Therapies: A Review.Biomolecules · 2026Review
- Generative AI for navigating synthesizable chemical space.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Generative design of singlet fission materials leveraging a fragment-oriented database.Chemical science · 2025Article
- Generative Deep Learning for de Novo Drug Design─A Chemical Space Odyssey.Journal of chemical information and modeling · 2025Review
- Challenging Reaction Prediction Models to Generalize to Novel Chemistry.ACS central science · 2025Article
- Self-awareness of retrosynthesis via chemically inspired contrastive learning for reinforced molecule generation.Briefings in bioinformatics · 2025Article
- Syn-MolOpt: a synthesis planning-driven molecular optimization method using data-derived functional reaction templates.Journal of cheminformatics · 2025Article
- Molecular optimization using a conditional transformer for reaction-aware compound exploration with reinforcement learning.Communications chemistry · 2025Article
- DrugSynthMC: An Atom-Based Generation of Drug-like Molecules with Monte Carlo Search.Journal of chemical information and modeling · 2024Article
- Synthetically accessible de novo design using reaction vectors: Application to PARP1 inhibitors.Molecular informatics · 2024Article
- Evolutionary Monte Carlo of QM Properties in Chemical Space: Electrolyte Design.Journal of chemical theory and computation · 2023Article
- The Hitchhiker's Guide to Deep Learning Driven Generative Chemistry.ACS medicinal chemistry letters · 2023Review
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The fundamental goal of generative drug design is to propose optimized molecules that meet predefined activity, selectivity, and pharmacokinetic criteria. Despite recent progress, we argue that existing generative methods are limited in their ability to favorably shift the distributions of molecular properties during optimization. We instead propose a novel Reinforcement Learning framework for molecular design in which an agent learns to directly optimize through a space of synthetically accessible drug-like molecules. This becomes possible by defining transitions in our Markov decision process as chemical reactions and allows us to leverage synthetic routes as an inductive bias. We validate our method by demonstrating that it outperforms existing state-of-the-art approaches in the optimization of pharmacologically relevant objectives, while results on multi-objective optimization tasks suggest increased scalability to realistic pharmaceutical design problems.
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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.