ArticleScience advances2021
Combining generative artificial intelligence and on-chip synthesis for de novo drug design.
Article in Science advances, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 68 papers.
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Who cites it
68 citing papers in PubMed.
- AI-Driven Synthesis in Medicinal Chemistry: Integrating Large Language Models, Robotic Automation, and Sustainability Metrics to Accelerate Drug Discovery.Medicinal research reviews · 2026Review
- Automated Parallel Synthesis Accelerates Virtual Screening Hit Discovery.Journal of the American Chemical Society · 2026Article
- Towards selecting the virtual series of molecules using the central fragment via R-FBDD at the early stages of drug discovery.Molecular diversity · 2026Article
- Enabling Synthetically Feasible Molecular Editing in Drug Discovery via Reaction-Regulated Graph-Based Genetic Algorithms.JACS Au · 2026Article
- From Prompt to Drug: Toward Pharmaceutical Superintelligence.ACS central science · 2026Review
- Molecular Design with Artificial Intelligence: Progress and Perspectives for Small Molecules.Chemical reviews · 2026Review
- AI-integrated microfluidics for drug screening: From single cell to organ-on-a-chip.Acta pharmaceutica Sinica. B · 2026Review
- Advancing Drug Discovery with AI: Machine and Deep Learning Strategies for Target Identification and Precision Nanomedicine.International journal of nanomedicine · 2026Review
- Optimizing drug combinations to resurrect the potency of failed antibody therapy against emerging COVID-19 variants using IDentif.AI.Frontiers in digital health · 2026Article
- Molecular deep learning at the edge of chemical space.Nature machine intelligence · 2026Article
- Lab on an end: Micromanipulation using the acoustohydrodynamic pillar array as an end effector.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Accelerating compound synthesis in drug discovery: the role of digitalisation and automation.RSC medicinal chemistry · 2025Article
- REINFORCE-ING Chemical Language Models for Drug Discovery.Journal of chemical information and modeling · 2025Article
- Expediting hit-to-lead progression in drug discovery through reaction prediction and multi-dimensional optimization.Nature communications · 2025Article
- How evaluation choices distort the outcome of generative drug discovery.Journal of cheminformatics · 2025Article
- Stereochemistry-aware string-based molecular generation.PNAS nexus · 2025Article
- Going beyond SMILES enumeration for data augmentation in generative drug discovery.Digital discovery · 2025Article
- Editorial for the Special Issue on the Application of Microfluidic Technology in Bioengineering.Micromachines · 2025Article
- Graph-sequence enhanced transformer for template-free prediction of natural product biosynthesis.Patterns (New York, N.Y.) · 2025Article
- Optimizing drug design by merging generative AI with a physics-based active learning framework.Communications chemistry · 2025Article
8 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Automating the molecular design-make-test-analyze cycle accelerates hit and lead finding for drug discovery. Using deep learning for molecular design and a microfluidics platform for on-chip chemical synthesis, liver X receptor (LXR) agonists were generated from scratch. The computational pipeline was tuned to explore the chemical space of known LXRα agonists and generate novel molecular candidates. To ensure compatibility with automated on-chip synthesis, the chemical space was confined to the virtual products obtainable from 17 one-step reactions. Twenty-five de novo designs were successfully synthesized in flow. In vitro screening of the crude reaction products revealed 17 (68%) hits, with up to 60-fold LXR activation. The batch resynthesis, purification, and retesting of 14 of these compounds confirmed that 12 of them were potent LXR agonists. These results support the suitability of the proposed design-make-test-analyze framework as a blueprint for automated drug design with artificial intelligence and miniaturized bench-top synthesis.
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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.