Therapeutic peptides offer potential advantages over small molecules in terms of selectivity, affinity, and their ability to target "undruggable" proteins that are associated with a wide range of pathologies. Despite their importance, current molecular design capabilities that inform medicinal chemistry decisions on peptide programs are limited. More specifically, there are unmet needs for structure-activity relationship (SAR) analysis and visualization of linear, cyclic, and cross-linked peptides containing non-natural motifs, which are widely used in drug discovery. To bridge this gap, we developed PepSeA (Peptide Sequence Alignment and Visualization), an open-source, freely available package of sequence-based tools (https://github.com/Merck/PepSeA). PepSeA enables multiple sequence alignment of non-natural amino acids and enhanced visualization with the hierarchical editing language for macromolecules (HELM). Via stepwise SAR analysis of a ChEMBL peptide data set, we demonstrate the utility of PepSeA to accelerate decision making in lead optimization campaigns in pharmaceutical setting. PepSeA represents an initial attempt to expand cheminformatics capabilities for therapeutic peptides and to enable rapid and more efficient design-make-test cycles.

AI and Big Data Analytics MSD Czech Republic s r o Prague 150 00 Czech Republic

Computational and Structural Chemistry Merck and Co Inc Boston Massachusetts 02115 United States

Department of Informatics and Chemistry Faculty of Chemical Technology University of Chemistry and Technology Prague 166 28 Czech Republic

Discovery Research IT Merck and Co Inc Boston Massachusetts 02115 United States

Foundational Data and Analytics MSD Czech Republic s r o Prague 150 00 Czech Republic

R and D Informatics Solutions MSD Czech Republic s r o Prague 150 00 Czech Republic

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