ArticleACS central science2020
Deep Learning for Prediction and Optimization of Fast-Flow Peptide Synthesis.
Article in ACS central science, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 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.
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Who cites it
21 citing papers in PubMed.
- Machine learning-based prediction of peptide aggregation during chemical synthesis.Nature chemistry · 2026Article
- Amino acid composition drives aggregation during peptide synthesis.Nature chemistry · 2026Article
- Peptide-Based Therapeutics for Alzheimer's Disease: Medicinal Chemistry, AI-Guided Computational Design, and Blood-Brain Barrier Delivery.Drug design, development and therapy · 2026Review
- Development of ArgTag for Scalable Solid-Phase Synthesis of Aggregating Peptides.ACS chemical biology · 2025Article
- Discovery of Macrocyclic Peptide Binders, Covalent Modifiers, and Degraders of a Structured RNA by mRNA Display.Journal of the American Chemical Society · 2025Article
- Advances in the chemical synthesis of human proteoforms.Science China. Life sciences · 2025Review
- Acceptor-Adaptive Automated Glycosylation Optimization for Automated Glycan Assembly.Chemistry (Weinheim an der Bergstrasse, Germany) · 2025Article
- Natural Cyclic Peptides: Synthetic Strategies and Biomedical Applications.Biomedicines · 2025Review
- A Versatile "Synthesis Tag" (SynTag) for the Chemical Synthesis of Aggregating Peptides and Proteins.Journal of the American Chemical Society · 2024Article
- Therapeutic peptide development revolutionized: Harnessing the power of artificial intelligence for drug discovery.Heliyon · 2024Review
- Deep Learning-Driven Exploration of Pyrroloquinoline Quinone Neuroprotective Activity in Alzheimer's Disease.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Design of Cytotoxic T Cell Epitopes by Machine Learning of Human Degrons.ACS central science · 2024Article
- A robust data analytical method to investigate sequence dependence in flow-based peptide synthesis.Reaction chemistry & engineering · 2024Article
- Computer vision as a new paradigm for monitoring of solution and solid phase peptide synthesis.Chemical science · 2023Article
- Rapid Single-Shot Synthesis of the 214 Amino Acid-Long N-Terminal Domain of Pyocin S2.ACS chemical biology · 2023Article
- Flow-Based Fmoc-SPPS Preparation and SAR Study of Cathelicidin-PY Reveals Selective Antimicrobial Activity.Molecules (Basel, Switzerland) · 2023Article
- Machine Learning Guides Peptide Nucleic Acid Flow Synthesis and Sequence Design.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2022Article
- A graph representation of molecular ensembles for polymer property prediction.Chemical science · 2022Article
- Accurate Models of Substrate Preferences of Post-Translational Modification Enzymes from a Combination of mRNA Display and Deep Learning.ACS central science · 2022Article
- Review
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The chemical synthesis of polypeptides involves stepwise formation of amide bonds on an immobilized solid support. The high yields required for efficient incorporation of each individual amino acid in the growing chain are often impacted by sequence-dependent events such as aggregation. Here, we apply deep learning over ultraviolet-visible (UV-vis) analytical data collected from 35 427 individual fluorenylmethyloxycarbonyl (Fmoc) deprotection reactions performed with an automated fast-flow peptide synthesizer. The integral, height, and width of these time-resolved UV-vis deprotection traces indirectly allow for analysis of the iterative amide coupling cycles on resin. The computational model maps structural representations of amino acids and peptide sequences to experimental synthesis parameters and predicts the outcome of deprotection reactions with less than 6% error. Our deep-learning approach enables experimentally aware computational design for prediction of Fmoc deprotection efficiency and minimization of aggregation events, building the foundation for real-time optimization of peptide synthesis in flow.
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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.