ArticleJournal of chemical information and modeling2021
Investigating Active Learning and Meta-Learning for Iterative Peptide Design.
Article in Journal of chemical information and modeling, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
9 citing papers in PubMed.
- MvAl-MFP: A Multi-Label Classification Method on the Functions of Peptides with Multi-View Active Learning.Current issues in molecular biology · 2025Article
- In silico analysis of soybean-derived umami peptides: Discovery and interaction mechanisms with T1R1/T1R3 receptor.Food chemistry: X · 2025Article
- PepFuNN: Novo Nordisk Open-Source Toolkit to Enable Peptide in Silico Analysis.Journal of peptide science : an official publication of the European Peptide Society · 2025Article
- Learning peptide properties with positive examples only.Digital discovery · 2024Article
- Machine Learning Methods for Small Data Challenges in Molecular Science.Chemical reviews · 2023Review
- Serverless Prediction of Peptide Properties with Recurrent Neural Networks.Journal of chemical information and modeling · 2023Article
- Machine Learning in Tissue Engineering.Tissue engineering. Part A · 2023Review
- Integration of Machine Learning and Coarse-Grained Molecular Simulations for Polymer Materials: Physical Understandings and Molecular Design.Frontiers in chemistry · 2021Article
- Deep Learning in Protein Structural Modeling and Design.Patterns (New York, N.Y.) · 2020Review
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Often the development of novel functional peptides is not amenable to high throughput or purely computational screening methods. Peptides must be synthesized one at a time in a process that does not generate large amounts of data. One way this method can be improved is by ensuring that each experiment provides the best improvement in both peptide properties and predictive modeling accuracy. Here, we study the effectiveness of active learning, optimizing experiment order, and meta-learning, transferring knowledge between contexts, to reduce the number of experiments necessary to build a predictive model. We present a multitask benchmark database of peptides designed to advance these methods for experimental design. Each task is a binary classification of peptides represented as a sequence string. We find neither active learning method tested to be better than random choice. The meta-learning method Reptile was found to improve the average accuracy across data sets. Combining meta-learning with active learning offers inconsistent benefits.
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What OpenQuestion holds
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.