Chungong Yu

ComplexDesign is a method for designing multichain protein complexes by optimizing sequences to fold proteins and form inter-chain interactions, achieving high success rates in designing dimers, trimers, and tetramers.

This paper introduces OCDesign, a curriculum-guided framework for multi-property protein design, showing that staged objective introduction improves success rates compared to one-shot optimization.

This paper introduces ProTCR, a reinforcement learning method for predicting TCR-pMHC binding, achieving higher accuracy than existing methods on benchmark datasets.

This paper introduces NeuralNJ, a deep learning method for phylogenetic inference that improves accuracy and efficiency by using an end-to-end framework and reinforcement learning.

CarbonDesign improves protein sequence design by integrating AlphaFold concepts, achieving high accuracy on benchmark datasets and showing robustness in de novo design and variant effect prediction.

ProDESIGN-LE designs protein sequences efficiently by learning residue local environments, achieving high accuracy in structure prediction and experimental validation.

This study introduces DeepNovo Peptidome and DeepSelf for neoantigen discovery and validation using immunopeptidomics and deep learning, demonstrating their application in cervical cancer tissues.