Peptide Design using RNN
LSTM-Based Deep Learning Model for the Discovery of Antimicrobial Peptides Targeting Mycobacterium tuberculosis
Developed LSTM-based classifiers and generators using transfer learning to predict and generate antimicrobial peptides (AMPs) specific to Mycobacterium tuberculosis.
Benchmarked multiple LSTM architectures, achieving 90% accuracy and 0.97 AUC in TB-AMP classification, with transfer learning enhancing performance under limited data.
Designed a generative pipeline that produced 94 novel non-toxic peptide candidates, 10 of which met physicochemical criteria and structural validation for AMP-like properties.
Performed comparative motif and structural divergence analysis to ensure novelty and function of generated peptides, with full reproducibility provided via public code.
Generated protein structure prediction and visualization