As part of our effort to develop a robust image-based artificial intelligence (AI) platform for drug discovery and development, we designed a new method based on a concept of Chaos Game Representation (CGR) to characterize peptide sequences. The CGRs were encoded into images for peptides based on a supervised autoencoder (SAE), and these images were used to build deep learning neural network models for prediction of peptide hemolytic properties. The approach can be employed for lead optimization to derive safe peptidic agents. Concurrently, such CGR-SAE combination has also been devised to model the susceptibility/resistance of AIDS patients with different HIV protease mutations to a list of FDA approved drugs, freely available here.

Input of Peptide Sequence(s) in FASTA

Output of Predicted Hemolytic Properties