ArticleACS omega2022
A Machine Learning Classification Model for Gold-Binding Peptides.
Article in ACS omega, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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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
7 citing papers in PubMed.
- Validation ofACS central science · 2026Article
- Discovering Plastic-Binding Peptides with Favorable Affinity, Water Solubility, and Binding Specificity Through Deep Learning and Biophysical Modeling.bioRxiv : the preprint server for biology · 2026Article
- AI-driven rational design of promiscuous and selective plastic-binding peptides.Chemical science · 2025Article
- Ultrashort Peptides as Stabilizing Agents for Colloidal Nanogold.ACS applied materials & interfaces · 2025Article
- Engineered M13-Derived Bacteriophages Capable of Gold Nanoparticle Synthesis and Nanogold Manipulations.International journal of molecular sciences · 2024Article
- Characterization and Classification In Silico of Peptides with Dual Activity (Antimicrobial and Wound Healing).International journal of molecular sciences · 2023Article
- Sequence rules for gold-binding peptides.RSC advances · 2023Article
Corrections and comments
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Authors and funding
1 author.
Funding
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Abstract
There has been growing interest in using peptides for the controlled synthesis of nanomaterials. Peptides play a crucial role not only in regulating the nanostructure formation process but also in influencing the resulting properties of the nanomaterials. Leveraging machine learning (ML) in the biomimetic workflow is anticipated to accelerate peptide discovery, make the process more resource-efficient, and unravel associations among attributes that may be useful in peptide design. In this study, a binary ML classifier is formulated that was trained and tested on 1720 peptide examples. The support vector machine classifier uses Kidera factors to categorize peptides into one of two groups based on their binding ability. The classifier exhibits satisfactory performance, as demonstrated by various performance metrics. In addition, key variables that bear a huge impact on the model were identified, such as peptide hydrophobicity. As these trends were derived from a large and diverse dataset, the insights drawn from the data are expected to be generalizable and robust. Thus, the presented ML model is an important step toward the rational and predictive 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.