ArticleCommunications chemistry2023
Identifying opportunities for late-stage C-H alkylation with high-throughput experimentation and in silico reaction screening.
Article in Communications chemistry, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Methods for the establishment of enzymatic mechanisms - from QM to ML.Chemical science · 2026Review
- Phosphonium Ions as Activating Groups for the Selective Alkylation of Pyridines and Polyazines.Journal of the American Chemical Society · 2026Article
- Expediting hit-to-lead progression in drug discovery through reaction prediction and multi-dimensional optimization.Nature communications · 2025Article
- Computational tools for the prediction of site- and regioselectivity of organic reactions.Chemical science · 2025Review
- Standardized Approach for Diversification of Complex Small Molecules via Aryl Thianthrenium Salts.Journal of the American Chemical Society · 2025Article
- Simple User-Friendly Reaction Format.Molecular informatics · 2025Article
- CombiningRSC advances · 2024Article
- Geometric deep learning-guided Suzuki reaction conditions assessment for applications in medicinal chemistry.RSC medicinal chemistry · 2024Article
- Prospective de novo drug design with deep interactome learning.Nature communications · 2024Article
- Exploring protein-ligand binding affinity prediction with electron density-based geometric deep learning.RSC advances · 2024Article
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
Enhancing the properties of advanced drug candidates is aided by the direct incorporation of specific chemical groups, avoiding the need to construct the entire compound from the ground up. Nevertheless, their chemical intricacy often poses challenges in predicting reactivity for C-H activation reactions and planning their synthesis. We adopted a reaction screening approach that combines high-throughput experimentation (HTE) at a nanomolar scale with computational graph neural networks (GNNs). This approach aims to identify suitable substrates for late-stage C-H alkylation using Minisci-type chemistry. GNNs were trained using experimentally generated reactions derived from in-house HTE and literature data. These trained models were then used to predict, in a forward-looking manner, the coupling of 3180 advanced heterocyclic building blocks with a diverse set of sp
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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.