ArticleNature communications2024
Predictive Minisci late stage functionalization with transfer learning.
Article in Nature communications, 2024. 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
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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.
- Rega: A Platform for the Prediction of the Regioselectivity of C-H Functionalization Reactions.Journal of chemical information and modeling · 2026Article
- Expediting hit-to-lead progression in drug discovery through reaction prediction and multi-dimensional optimization.Nature communications · 2025Article
- Predicting reaction conditions: a data-driven perspective.Chemical science · 2025Review
- NMRExtractor: leveraging large language models to construct an experimental NMR database from open-source scientific publications.Chemical science · 2025Article
- A meta-learning approach for selectivity prediction in asymmetric catalysis.Nature communications · 2025Article
- Computational tools for the prediction of site- and regioselectivity of organic reactions.Chemical science · 2025Review
- Designing Target-specific Data Sets for Regioselectivity Predictions on Complex Substrates.Journal of the American Chemical Society · 2025Article
- Simple User-Friendly Reaction Format.Molecular informatics · 2025Article
- Exploring Molecular Heteroencoders with Latent Space Arithmetic: Atomic Descriptors and Molecular Operators.Molecules (Basel, Switzerland) · 2024Article
- Geometric deep learning-guided Suzuki reaction conditions assessment for applications in medicinal chemistry.RSC medicinal chemistry · 2024Article
- Transfer learning for a foundational chemistry model.Chemical science · 2024Article
- Predictive Minisci late stage functionalization with transfer learning.Nature communications · 2024Article
- Identifying opportunities for late-stage C-H alkylation with high-throughput experimentation and in silico reaction screening.Communications chemistry · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Structural diversification of lead molecules is a key component of drug discovery to explore chemical space. Late-stage functionalizations (LSFs) are versatile methodologies capable of installing functional handles on richly decorated intermediates to deliver numerous diverse products in a single reaction. Predicting the regioselectivity of LSF is still an open challenge in the field. Numerous efforts from chemoinformatics and machine learning (ML) groups have made strides in this area. However, it is arduous to isolate and characterize the multitude of LSF products generated, limiting available data and hindering pure ML approaches. We report the development of an approach that combines a message passing neural network and
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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.