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Updated: Jul 15, 2026

A Tripeptide-Stabilized Nanoemulsion of Oleic Acid

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Published on: February 27, 2019

Scaling SMILES-Based Chemical Language Models for Therapeutic Peptide Engineering.

Aaron L Feller^1,2, Maxim Secor², Sebastian Swanson²

¹Integrative Biology, The University of Texas at Austin, 2500 Speedway, Austin, Texas 78712, United States.

Journal of Chemical Information and Modeling

|July 13, 2026

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We developed PeptideCLM-2, a new chemical language model, to effectively represent therapeutic peptides in drug discovery. This advances machine learning for predicting peptide development endpoints like diffusion and half-life.

Area of Science:

Computational chemistry
Drug discovery
Machine learning

Background:

Therapeutic peptides offer high specificity but face computational challenges in drug discovery.
Current models (protein or chemical) inadequately represent complex peptide chemistry.
Existing methods rely on limited descriptors or complex, dataset-specific pipelines.

Purpose of the Study:

To bridge the computational gap for therapeutic peptides.
To introduce PeptideCLM-2, a novel suite of chemical language models.
To enable native representation of complex peptide chemistry.

Main Methods:

Trained chemical language models on over 100 million molecules.
Developed PeptideCLM-2 for native representation of peptide chemistry.

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Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions

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Last Updated: Jul 15, 2026

A Tripeptide-Stabilized Nanoemulsion of Oleic Acid

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Published on: February 27, 2019

Constructing Thioether/Vinyl Sulfide-tethered Helical Peptides Via Photo-induced Thiol-ene/yne Hydrothiolation

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Published on: August 1, 2018

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions

Published on: January 26, 2024

Benchmarked performance against existing methods.

Main Results:

PeptideCLM-2 effectively represents complex peptide chemistry.
Demonstrated strong performance in predicting key development endpoints.
Outperformed prior methods for membrane diffusion, biological function, and half-life prediction.

Conclusions:

PeptideCLM-2 expands machine learning capabilities for therapeutic peptide research.
This approach offers a more effective computational toolkit for peptide drug discovery.
The model shows promise for accelerating the development of peptide-based therapeutics.