• Interpretable support vector c...

*Result*: Interpretable support vector classifier for reliable prediction of antibacterial activity of modified peptides against Escherichia coli.

Title:
Interpretable support vector classifier for reliable prediction of antibacterial activity of modified peptides against Escherichia coli.
Authors:
Salas RL; Institute of Chemistry, College of Science, University of the Philippines, Diliman, Quezon City, 1101, Philippines; Department of Natural Sciences and Mathematics, Visayas State University, Tolosa, Leyte, 6503, Philippines. Electronic address: rlsalas@up.edu.ph., Sabido PMG; Institute of Chemistry, College of Science, University of the Philippines, Diliman, Quezon City, 1101, Philippines., Nellas RB; Institute of Chemistry, College of Science, University of the Philippines, Diliman, Quezon City, 1101, Philippines; UP Intelligent Systems Center, University of the Philippines System, Diliman, Quezon City, 1101, Philippines; Data Science Program, College of Science, University of the Philippines, Diliman, Quezon City, 1101, Philippines.
Source:
Journal of molecular graphics & modelling [J Mol Graph Model] 2026 Jan; Vol. 142, pp. 109188. Date of Electronic Publication: 2025 Oct 04.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Elsevier Science, Inc Country of Publication: United States NLM ID: 9716237 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1873-4243 (Electronic) Linking ISSN: 10933263 NLM ISO Abbreviation: J Mol Graph Model Subsets: MEDLINE
Imprint Name(s):
Original Publication: New York, NY : Elsevier Science, Inc., c1997-
MeSH Terms:
Escherichia coli*/drug effects , Anti-Bacterial Agents*/pharmacology , Anti-Bacterial Agents*/chemistry , Antimicrobial Peptides*/chemistry , Antimicrobial Peptides*/pharmacology , Peptides*/chemistry , Peptides*/pharmacology , Support Vector Machine*, Microbial Sensitivity Tests ; Machine Learning
Contributed Indexing:
Keywords: Antimicrobial peptides; Antimicrobial resistance; Artificial intelligence; Drug discovery; Machine learning; Molecular fingerprints
Substance Nomenclature:
0 (Anti-Bacterial Agents)
0 (Antimicrobial Peptides)
0 (Peptides)
Entry Date(s):
Date Created: 20251010 Date Completed: 20251202 Latest Revision: 20251202
Update Code:
20260130
DOI:
10.1016/j.jmgm.2025.109188
PMID:
41072192
Database:
MEDLINE

*Further Information*

*Antimicrobial peptides (AMPs) are promising alternatives to traditional antibiotics, whose effectiveness is declining due to rising antimicrobial resistance (AMR). To accelerate AMP discovery, we developed ISCAPE (Interpretable Support Vector Classifier of Antibacterial Activity of Peptides against Escherichia coli), a machine learning (ML) model that addresses the limitations of current AMP predictors. ISCAPE requires only a Simplified Molecular-Input Line-Entry System (SMILES) string as input and can predict the activity of both natural and chemically modified peptides against E. coli ATCC 25922. Activity is defined by a minimum inhibitory concentration (MIC) threshold of ≤16 μg/mL. To ensure reliability, only MIC values obtained under comparable experimental conditions were included in our curated dataset. ISCAPE outperformed the state-of-the-art AntiMPmod, achieving an area under the receiver operating characteristic curve (AUROC) of 91.83% and a Matthew's correlation coefficient (MCC) of 71.86%. Features driving this performance include the fraction of carbon-carbon pairs and feature- and count-based extended connectivity fingerprints (ECFPs). Model interpretability is enhanced through SHapley Additive exPlanations (SHAP), which identifies the molecular features most critical for AMP activity. To our knowledge, ISCAPE is the first interpretable ML predictor capable of predicting antibacterial activity for both natural and modified peptides against a specific E. coli strain. It is a user-friendly tool that allows experimentalists to pinpoint key molecular features, reducing the need for extensive structure-activity relationship (SAR) studies and guiding the design of novel AMPs.
(Copyright © 2025 Elsevier Inc. All rights reserved.)*

*Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.*