Researchers from the University of the Philippines Diliman – College of Science (UPD-CS) have created an artificial intelligence (AI) tool to help accelerate the search for new antibacterial treatments amid rising global concerns over antimicrobial resistance (AMR).
The team — composed of Remmer Salas, Dr. Portia Mahal Sabido, and Dr. Ricky Nellas of the UPD-CS Institute of Chemistry — developed ISCAPE (Interpretable Support Vector Classifier of Antibacterial Activity of Peptides against Escherichia coli), which predicts whether a peptide can kill or inhibit the growth of E. coli.
The system requires only a SMILES string as input, making it easier for researchers to evaluate candidate molecules.
“Traditionally, discovering antibacterial peptides means synthesizing many candidates and testing them one by one. This process is time-consuming,” said Salas.
“We used AI to learn from existing data and identify patterns that distinguish active peptides from inactive ones.”
Unlike many AI models, ISCAPE also highlights which molecular features make a peptide effective, helping scientists design better candidates while reducing trial-and-error experiments.
Salas explained: “ISCAPE helps address antimicrobial resistance by accelerating early-stage screening through data-driven peptide design. It doesn’t replace laboratory experiments, but it makes discovery more efficient and helps researchers focus on the most promising candidates.”
The tool can be retrained to predict activity against other bacterial strains, provided high-quality datasets are available. It may also be adapted for other bioactive peptides.
ISCAPE is publicly accessible via Hugging Face Spaces, with training data and code available on GitHub.
The team’s research paper, “Interpretable support vector classifier for reliable prediction of antibacterial activity of modified peptides against Escherichia coli,” was published in the Journal of Molecular Graphics and Modelling.
