ArticleJournal of chemical theory and computation2021
Beyond Tripeptides Two-Step Active Machine Learning for Very Large Data sets.
Article in Journal of chemical theory and computation, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Artificial intelligence-assisted design of self-assembling peptide hydrogels for neural regeneration: Principles and opportunities.Bioactive materials · 2027Review
- Amphipathic β‑Sheet-Forming Octapeptide Self-Assembly Using the Martini Potential Family.ACS omega · 2026Article
- New Frontiers in AI-Nano Converged Platforms for Intelligent Diagnostics, Therapeutics, and Safety Evaluation.Chem & bio engineering · 2026Review
- Article
- Secondary Structure Bead-Encoded Amphiphilicity Biases Peptide Self-Assembly Prediction in MARTINI Coarse-Grained Simulations.ACS applied materials & interfaces · 2026Article
- Central position of histidine in the sequence of designed alternating polarity peptides enhances pH-responsive assembly with DNA.BMC biotechnology · 2025Article
- Discovery of unconventional and nonintuitive self-assembling peptide materials using experiment-driven machine learning.Science advances · 2025Article
- Directed discovery of high-loading nanoaggregates enabled by drug-matched oligo-peptide excipients.Chem · 2025Article
- Learning the rules of peptide self-assembly through data mining with large language models.Science advances · 2025Article
- Aggregation Rules of Short Peptides.JACS Au · 2024Article
- Review
- Assessment of the MARTINI 3 Performance for Short Peptide Self-Assembly.Journal of chemical theory and computation · 2024Article
- Multiscale Simulations to Discover Self-Assembled Oligopeptides: A Benchmarking Study.Journal of chemical theory and computation · 2024Article
- Deep Learning Empowers the Discovery of Self-Assembling Peptides with Over 10 Trillion Sequences.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2023Article
- Short Peptide Self-Assembly in the Martini Coarse-Grain Force Field Family.Accounts of chemical research · 2023Article
- Constant pH Coarse-Grained Molecular Dynamics with Stochastic Charge Neutralization.The journal of physical chemistry letters · 2022Article
- Discovery of phosphotyrosine-binding oligopeptides with supramolecular target selectivity.Chemical science · 2021Article
- Computational Tools to Rationalize and Predict the Self-Assembly Behavior of Supramolecular Gels.Gels (Basel, Switzerland) · 2021Review
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Self-assembling peptide nanostructures have been shown to be of great importance in nature and have presented many promising applications, for example, in medicine as drug-delivery vehicles, biosensors, and antivirals. Being very promising candidates for the growing field of bottom-up manufacture of functional nanomaterials, previous work (Frederix, et al. 2011 and 2015) has screened all possible amino acid combinations for di- and tripeptides in search of such materials. However, the enormous complexity and variety of linear combinations of the 20 amino acids make exhaustive simulation of all combinations of tetrapeptides and above infeasible. Therefore, we have developed an active machine-learning method (also known as "iterative learning" and "evolutionary search method") which leverages a lower-resolution data set encompassing the whole search space and a just-in-time high-resolution data set which further analyzes those target peptides selected by the lower-resolution model. This model uses newly generated data upon each iteration to improve both lower- and higher-resolution models in the search for ideal candidates. Curation of the lower-resolution data set is explored as a method to control the selected candidates, based on criteria such as log
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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.