ArticlePLoS computational biology2015
Machine learning assisted design of highly active peptides for drug discovery.
Article in PLoS computational biology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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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.
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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
23 citing papers in PubMed, 63 citations in OpenAlex.
- Multidimensional maturation of antibody variable domains with machine-learning assistance.mAbs · 2026Article
- Bioactive peptides targeting dyslipidemia and atherosclerosis: from computational discovery and data bottlenecks to precision medicine translation.Frontiers in molecular biosciences · 2026Review
- A fused weighted federated learning-based adaptive approach for early-stage drug prediction.Scientific reports · 2025Article
- Therapeutic peptide development revolutionized: Harnessing the power of artificial intelligence for drug discovery.Heliyon · 2024Review
- Challenges and opportunities of poly(amino acid) nanomedicines in cancer therapy.Nanomedicine (London, England) · 2024Review
- Prospection of Peptide Inhibitors of Thrombin from Diverse Origins Using a Machine Learning Pipeline.Bioengineering (Basel, Switzerland) · 2023Article
- Data-mining unveils structure-property-activity correlation of viral infectivity enhancing self-assembling peptides.Nature communications · 2023Article
- Article
- A review on antimicrobial peptides databases and the computational tools.Database : the journal of biological databases and curation · 2022Review
- Design of Membrane Active Peptides Considering Multi-Objective Optimization for Biomedical Application.Membranes · 2022Review
- Impact of ANN in Revealing of Viral Peptides.BioMed research international · 2022Article
- Natural antimicrobial peptides as a source of new antiviral agents.The Journal of general virology · 2021Review
- Design, Screening, and Testing of Non-Rational Peptide Libraries with Antimicrobial Activity: In Silico and Experimental Approaches.Antibiotics (Basel, Switzerland) · 2020Review
- Morphing of Amphipathic Helices to Explore the Activity and Selectivity of Membranolytic Antimicrobial Peptides.Biochemistry · 2020Article
- Encodings and models for antimicrobial peptide classification for multi-resistant pathogens.BioData mining · 2019Review
- Machine learning antimicrobial peptide sequences: Some surprising variations on the theme of amphiphilic assembly.Current opinion in colloid & interface science · 2018Article
- Machine learning-enabled discovery and design of membrane-active peptides.Bioorganic & medicinal chemistry · 2018Review
- Computer-aided design of amino acid-based therapeutics: a review.Drug design, development and therapy · 2018Review
- Development of models for classification of action between heat-clearing herbs and blood-activating stasis-resolving herbs based on theory of traditional Chinese medicine.Chinese medicine · 2018Article
- What can machine learning do for antimicrobial peptides, and what can antimicrobial peptides do for machine learning?Interface focus · 2017Review
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 at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The discovery of peptides possessing high biological activity is very challenging due to the enormous diversity for which only a minority have the desired properties. To lower cost and reduce the time to obtain promising peptides, machine learning approaches can greatly assist in the process and even partly replace expensive laboratory experiments by learning a predictor with existing data or with a smaller amount of data generation. Unfortunately, once the model is learned, selecting peptides having the greatest predicted bioactivity often requires a prohibitive amount of computational time. For this combinatorial problem, heuristics and stochastic optimization methods are not guaranteed to find adequate solutions. We focused on recent advances in kernel methods and machine learning to learn a predictive model with proven success. For this type of model, we propose an efficient algorithm based on graph theory, that is guaranteed to find the peptides for which the model predicts maximal bioactivity. We also present a second algorithm capable of sorting the peptides of maximal bioactivity. Extensive analyses demonstrate how these algorithms can be part of an iterative combinatorial chemistry procedure to speed up the discovery and the validation of peptide leads. Moreover, the proposed approach does not require the use of known ligands for the target protein since it can leverage recent multi-target machine learning predictors where ligands for similar targets can serve as initial training data. Finally, we validated the proposed approach in vitro with the discovery of new cationic antimicrobial peptides. Source code freely available at http://graal.ift.ulaval.ca/peptide-design/.
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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.