ArticleiScience2020
Solution-Phase DNA-Compatible Pictet-Spengler Reaction Aided by Machine Learning Building Block Filtering.
Article in iScience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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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
9 citing papers in PubMed.
- On-DNA photochemical methods for expanding DNA-encoded library (DEL) chemical space.RSC medicinal chemistry · 2026Review
- On-DNA Synthesis of Multisubstituted Indoles.Organic letters · 2024Article
- KinomeMETA: meta-learning enhanced kinome-wide polypharmacology profiling.Briefings in bioinformatics · 2023Article
- Impact of organic chemistry conditions on DNA durability in the context of DNA-encoded library technology.iScience · 2023Article
- Translating the Genome into Drugs.Accounts of chemical research · 2023Article
- Opportunities for Expanding Encoded Chemical Diversification and Improving Hit Enrichment in mRNA-Displayed Peptide Libraries.Chembiochem : a European journal of chemical biology · 2022Review
- Chemically Stabilized DNA Barcodes for DNA-Encoded Chemistry.Angewandte Chemie (International ed. in English) · 2021Article
- DNA-Encoded Chemical Libraries: A Comprehensive Review with Succesful Stories and Future Challenges.ACS pharmacology & translational science · 2021Review
- Review
Corrections and comments
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Authors and funding
18 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The application of machine learning toward DNA encoded library (DEL) technology is lacking despite obvious synergy between these two advancing technologies. Herein, a machine learning algorithm has been developed that predicts the conversion rate for the DNA-compatible reaction of a building block with a model DNA-conjugate. We exemplify the value of this technique with a challenging reaction, the Pictet-Spengler, where acidic conditions are normally required to achieve the desired cyclization between tryptophan and aldehydes to provide tryptolines. This is the first demonstration of using a machine learning algorithm to cull potential building blocks prior to their purchase and testing for DNA-encoded library synthesis. Importantly, this allows for a challenging reaction, with an otherwise very low building block pass rate in the test reaction, to still be used in DEL synthesis. Furthermore, because our protocol is solution phase it is directly applicable to standard plate-based DEL synthesis.
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Registered trials
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