MultiCycPermea: accurate and interpretable prediction of cyclic peptide permeability using a multimodal image-sequence model.

ABSTRACT:

Background

Cyclic peptides, known for their high binding affinity and low toxicity, show potential as innovative drugs for targeting "undruggable" proteins. However, their therapeutic efficacy is often hindered by poor membrane permeability. Over the past decade, the FDA has approved an average of one macrocyclic peptide drug per year, with romidepsin being the only one targeting an intracellular site. Biological experiments to measure permeability are time-consuming and labor-intensive. Rapid assessment of cyclic peptide permeability is crucial for their development.

Results

In this work, we proposed a novel deep learning model, dubbed as MultiCycPermea, for predicting cyclic peptide permeability. MultiCycPermea extracts features from both the image information (2D structur

SUBMITTER: Wang Z 

PROVIDER: S-EPMC11866622 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature