Link to bioRxiv paper:
http://biorxiv.org/cgi/content/short/2023.06.20.545822v1?rss=1
Authors: Cao, l., Xu, z., Zhang, c., Shang, t., Wu, x., Wu, y., Zhai, s., Ma, l., duan, h.
Abstract:
As a highly versatile therapeutic modality, cyclic peptides have gained signifi-cant attention due to their exceptional binding affinity, minimal toxicity and capaci-ty to target the surface of conventionally "undruggable" proteins. However, the de-velopment of cyclic peptides with therapeutic effects by targeting intracellular bio-logical targets has been hindered by the issue of limited membrane permeability. In this paper, we have conducted an extensive benchmarking analysis of a proprietary dataset consisting of 6941 cyclic peptides, employing machine learning and deep learning models. In addition, we propose an innovative multimodal model called Multi_CycGT which combines a Graph Convolutional Network (GCN) and a Trans-former to extract 1D and 2D features. These encoded features are then fused for the prediction of cyclic peptide permeability. The cross-validation experiments demon-strate that the proposed Multi_CycGT model achieved the highest level of accuracy on the test set, with an accuracy value of 0.8206 and an AUC value of 0.8650. This paper introduces a pioneering deep learning-based approach that demonstrates en-hanced effectiveness in predicting the membrane permeability of cyclic peptides. It also represents the first attempt in this field. We hope that this work will help to ac-celerate the design of cyclic peptide active drugs in medicinal chemistry and chem-ical biology applications.
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