ChemistryNature Communications
Design of target specific peptide inhibitors using generative deep learning and molecular dynamics simulations
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This innovative study by Sijie Chen, Tong Lin, Ruchira Basu, Jeremy Ritchey, Shen Wang, Yichuan Luo, Xingcan Li, Dehua Pei, Levent Burak Kara, and Xiaolin Cheng showcases a groundbreaking computational method combining GRU-VAE with Rosetta FlexPepDock, leading to significant enhancements in peptide inhibitors targeting β-catenin and NEMO. With some inhibitors demonstrating up to a 15-fold improvement in binding affinity, this research elegantly merges deep learning with molecular modeling.
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