ArticleScience advances2023
High-throughput synthesis provides data for predicting molecular properties and reaction success.
Article in Science advances, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed.
- Modular assembly and property profiling of an expansive, shape-defined library of pentacyclic saturated N-heterocycles.Chemical science · 2026Article
- High-throughput reaction screening using acoustic ejection mass spectrometry.Nature protocols · 2026Review
- Targeting WEE1 kinase: an integrated machine learning-cheminformatics framework for ultra-large-scale virtual screening and novel inhibitor discovery.Journal of computer-aided molecular design · 2026Article
- Yield Smarter, Not Harder: Good Practices for Machine Learning of Reaction Outcomes.Journal of the American Chemical Society · 2026Article
- Artificial Intelligence Across the Drug Development Lifecycle.Medical sciences (Basel, Switzerland) · 2026Review
- Expediting hit-to-lead progression in drug discovery through reaction prediction and multi-dimensional optimization.Nature communications · 2025Article
- Rapid Assessment of Virtually Synthesizable Chemical Structures via Support Vector Machine Models.Molecular informatics · 2025Article
- Applying Active Learning toward Building a Generalizable Model for Ni-Photoredox Cross-Electrophile Coupling of Aryl and Alkyl Bromides.Journal of the American Chemical Society · 2025Article
- Predicting three-component reaction outcomes from ~40,000 miniaturized reactant combinations.Science advances · 2025Article
- Binding-Site Purification of Actives (B-SPA) Enables Efficient Large-Scale Progression of Fragment Hits by Combining Multi-Step Array Synthesis With HT Crystallography.Angewandte Chemie (International ed. in English) · 2025Article
- Geometric deep learning-guided Suzuki reaction conditions assessment for applications in medicinal chemistry.RSC medicinal chemistry · 2024Article
- Incorporating Synthetic Accessibility in Drug Design: Predicting Reaction Yields of Suzuki Cross-Couplings by Leveraging AbbVie's 15-Year Parallel Library Data Set.Journal of the American Chemical Society · 2024Article
- Improving reproducibility of photocatalytic reactions-how to facilitate broad application of new methods.Nature communications · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The generation of attractive scaffolds for drug discovery efforts requires the expeditious synthesis of diverse analogues from readily available building blocks. This endeavor necessitates a trade-off between diversity and ease of access and is further complicated by uncertainty about the synthesizability and pharmacokinetic properties of the resulting compounds. Here, we document a platform that leverages photocatalytic N-heterocycle synthesis, high-throughput experimentation, automated purification, and physicochemical assays on 1152 discrete reactions. Together, the data generated allow rational predictions of the synthesizability of stereochemically diverse C-substituted N-saturated heterocycles with deep learning and reveal unexpected trends on the relationship between structure and properties. This study exemplifies how organic chemists can exploit state-of-the-art technologies to markedly increase throughput and confidence in the preparation of drug-like molecules.
Indexed as
Identifiers
What OpenQuestion holds
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.