SCIENCE

Academic Research Foundations

Peptaris builds on a body of academic research from our scientific founders and their collaborators at the University of Pennsylvania and other institutions. The work spans AI-guided peptide discovery, generative molecular design, experimental optimization, and computational approaches to understanding biological activity and molecular properties.

The Peptaris team in the lab

MACHINE LEARNING FOR BIOMATERIALS DESIGN •MOLECULAR DE-EXTINCTION •PEPTIDE ANTIBIOTIC OPTIMIZATION •KEY-CUTTING MACHINE APPROACH •PEPTIDE STRUCTURAL PLASTICITY •MULTI-TASK BAYESIAN OPTIMIZATION •AZAPEPTIDE GLP-1 ANALOGUE •KNOWLEDGE PRIORS FROM LITERATURE •SELF-DRIVING DATASETS •AI-PROGRAMMABLE THERAPEUTICS •MACHINE LEARNING FOR BIOMATERIALS DESIGN •MOLECULAR DE-EXTINCTION •PEPTIDE ANTIBIOTIC OPTIMIZATION •KEY-CUTTING MACHINE APPROACH •PEPTIDE STRUCTURAL PLASTICITY •MULTI-TASK BAYESIAN OPTIMIZATION •AZAPEPTIDE GLP-1 ANALOGUE •KNOWLEDGE PRIORS FROM LITERATURE •SELF-DRIVING DATASETS •AI-PROGRAMMABLE THERAPEUTICS •

PUBLICATIONS

Selected Work

Attribution note. These publications describe academic research and collaborative contributions, not studies conducted by Peptaris. They inform the company's scientific foundations but should not be interpreted as evidence of the current performance of its commercial platform.

Broader Perspectives on AI-Guided Molecular Design

Machine learning for biomaterials designNature Reviews Bioengineering / 2026 / Xia et al.A review of computational approaches to design and optimization across biomaterial classes, spanning surrogate modeling, generative design and active learning.

AI-Guided Peptide Discovery and Design

Deep-learning-enabled antibiotic discovery through molecular de-extinctionNature Biomedical Engineering / 2024 / Wan et al.Academic research using computational methods to identify antimicrobial peptide candidates from extinct organisms, with experimental follow-up.A generative artificial intelligence approach for peptide antibiotic optimizationNature Machine Intelligence / 2026 / Torres et al.Research on generative optimization of peptide antibiotics, including synthesis and experimental characterization of candidates.Tailored structured peptide design with a key-cutting machine approachNature Machine Intelligence / 2025 / Leyva et al.Research relevant to the computational design of peptides and exploration of molecular design space.

Predictive Modeling and Molecular Optimization

Peptide structural plasticity is predictable from sequence and environmentbioRxiv / 2026 / Preprint / Torres et al.A preprint describing a computational approach to modeling peptide structure in varying chemical contexts.Scaling multi-task Bayesian optimization with large language modelsICLR / 2026 / Zeng et al.Conference research on methods for transferring information between optimization tasks.Protease-resistant azapeptide GLP-1 analogue improves metabolic control in diet-induced obesityAcademic research / 2025 / Preprint / He et al.Collaborative academic study of chemical modification and biological evaluation of a peptide therapeutic.

Scientific Data and AI for Therapeutic Design

A dataset for distilling knowledge priors from literature for therapeutic designNeurIPS / 2025 / Jones et al.Conference research describing structured data resources derived from scientific literature to support modeling and molecular design.Self-driving datasets: from 20 million papers to nuanced biomedical knowledge at scalearXiv / 2026 / Preprint / Jones et al.Preprint investigating computational extraction of structured biomedical information from scientific literature.Designing AI-programmable therapeutics with the EDEN family of foundation modelsbioRxiv / 2026 / Preprint / Munsamy et al.Collaborative preprint exploring foundation models and therapeutic molecular design.

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