ArticleNature chemistry2024
Enabling late-stage drug diversification by high-throughput experimentation with geometric deep learning.
Article in Nature chemistry, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
39 citing papers in PubMed.
- High-throughput reaction screening using acoustic ejection mass spectrometry.Nature protocols · 2026Review
- Generalized Analysis of Electrophilic Small Molecules.Angewandte Chemie (International ed. in English) · 2026Article
- Regioselective Iridium-Catalyzed C(7)-H Borylation of FreeACS omega · 2026Article
- Graph-based understanding of isomeric diversity in complex dissolved organic matter.Communications chemistry · 2026Article
- Which Reaction Conditions Work on Drug-Like Molecules? Lessons from 66,000 High-Throughput Experiments.ACS central science · 2026Article
- Developments and challenges in hit progression within fragment-based drug discovery.Nature communications · 2026Review
- Computer-aided drug discovery: historical foundations, practical AI tools, and emerging ethical considerations.Frontiers in pharmacology · 2026Review
- Solvent Sustainability in Drug Discovery: Where Are We Now, and How Can We Improve?Journal of medicinal chemistry · 2025Review
- Accelerating compound synthesis in drug discovery: the role of digitalisation and automation.RSC medicinal chemistry · 2025Article
- ELECTRUM: an electron configuration-based universal metal fingerprint for transition metal compounds.Digital discovery · 2025Article
- Tracking protein kinase targeting advances: integrating QSAR into machine learning for kinase-targeted drug discovery.Future science OA · 2025Review
- Expediting hit-to-lead progression in drug discovery through reaction prediction and multi-dimensional optimization.Nature communications · 2025Article
- Progress of AI-Driven Drug-Target Interaction Prediction and Lead Optimization.International journal of molecular sciences · 2025Review
- Predicting reaction conditions: a data-driven perspective.Chemical science · 2025Review
- Tech-Enhanced Synthesis: Exploring the Synergy between Organic Chemistry and Technology.Journal of the American Chemical Society · 2025Review
- Molecular Machine Learning Approach to Enantioselective C-H Bond Activation Reactions: From Generative AI to Experimental Validation.Chemical science · 2025Article
- Predicting three-component reaction outcomes from ~40,000 miniaturized reactant combinations.Science advances · 2025Article
- The Importance of Atomic Charges for Predicting Site-Selective Ir-, Ru-, and Rh-Catalyzed C-H Borylations.The Journal of organic chemistry · 2025Article
- A meta-learning approach for selectivity prediction in asymmetric catalysis.Nature communications · 2025Article
- Application of Density Functional Theory to Molecular Engineering of Pharmaceutical Formulations.International journal of molecular sciences · 2025Review
Corrections and comments
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Authors and funding
16 authors.
Funding
Abstract
Late-stage functionalization is an economical approach to optimize the properties of drug candidates. However, the chemical complexity of drug molecules often makes late-stage diversification challenging. To address this problem, a late-stage functionalization platform based on geometric deep learning and high-throughput reaction screening was developed. Considering borylation as a critical step in late-stage functionalization, the computational model predicted reaction yields for diverse reaction conditions with a mean absolute error margin of 4-5%, while the reactivity of novel reactions with known and unknown substrates was classified with a balanced accuracy of 92% and 67%, respectively. The regioselectivity of the major products was accurately captured with a classifier F-score of 67%. When applied to 23 diverse commercial drug molecules, the platform successfully identified numerous opportunities for structural diversification. The influence of steric and electronic information on model performance was quantified, and a comprehensive simple user-friendly reaction format was introduced that proved to be a key enabler for seamlessly integrating deep learning and high-throughput experimentation for late-stage functionalization.
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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.