1 research outputs found
Multi_CycGT: A Deep Learning-Based Multimodal Model for Predicting the Membrane Permeability of Cyclic Peptides
Cyclic
peptides are gaining attention for their strong binding
affinity, low toxicity, and ability to target “undruggable”
proteins; however, their therapeutic potential against intracellular
targets is constrained by their limited membrane permeability, and
researchers need much time and money to test this property in the
laboratory. Herein, we propose an innovative multimodal model called
Multi_CycGT, which combines a graph convolutional network (GCN) and
a transformer to extract one- and two-dimensional features for predicting
cyclic peptide permeability. The extensive benchmarking experiments
show that our Multi_CycGT model can attain state-of-the-art performance,
with an average accuracy of 0.8206 and an area under the curve of
0.8650, and demonstrates satisfactory generalization ability on several
external data sets. To the best of our knowledge, it is the first
deep learning-based attempt to predict the membrane permeability of
cyclic peptides, which is beneficial in accelerating the design of
cyclic peptide active drugs in medicinal chemistry and chemical biology
applications