# Artificial intelligence based platform for peptide lead optimization.

> **NIH NIH R43** · KOLIBER BIOSCIENCES, INC. · 2024 · $350,000

## Abstract

ABSTRACT
Peptides possess exceptional therapeutic qualities, with high potency, selectivity, and low toxicity.
Challenges like short half-life and poor oral bioavailability of peptides have been addressed
through innovative strategies such as unnatural amino acids, conjugates, and cyclization.
However, the complexity of peptide datasets has grown, complicating drug discovery processes.
Machine learning (ML) has shown promise, with various algorithms showcased on numerous
applications. Yet current approaches lack encodings for non-canonical amino acids (NCAAs) and
struggle with small datasets. This proposal aims to develop methods for encoding NCAAs and
cyclic peptides and demonstrate high performance machine learning on commercial
sequence/activity as well as stability and permeability datasets. In addition, methods to assess
data diversity and minimal dataset requirements will be addressed. The platform, commercialized
in a browser-based software, will empower wet lab researchers to train potency models and utilize
pre-trained solubility, stability and permeability models. This comprehensive platform is poised to
expedite development schedules by extracting valuable insights from limited datasets and
preemptively addressing development challenges through property predictions. More importantly,
this endeavor has the potential to greatly benefit patients by introducing novel synthetic peptide
treatments that are not only safe and effective but also more affordable.

## Key facts

- **NIH application ID:** 11006711
- **Project number:** 1R43GM154505-01A1
- **Recipient organization:** KOLIBER BIOSCIENCES, INC.
- **Principal Investigator:** Ewa Lis
- **Activity code:** R43 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $350,000
- **Award type:** 1
- **Project period:** 2024-09-20 → 2026-09-19

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/11006711

## Citation

> US National Institutes of Health, RePORTER application 11006711, Artificial intelligence based platform for peptide lead optimization. (1R43GM154505-01A1). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/11006711. Licensed CC0.

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