Haowen Zhao

This paper introduces AbNatiV2 and p-AbNatiV2, deep learning models for assessing nativeness and pairing likelihood in antibodies and nanobodies, showing improved performance on held-out tests.

This paper introduces NbForge, a nanobody folding model that improves prediction of HCDR3 blueprint and disulphide bonds, achieving accuracy comparable to large predictors with faster speed.

This study improves nanobody structure prediction by using synthetic data and a classifier to filter structural accuracy, achieving better results than existing methods with lower computational cost.

This paper introduces a model-agnostic framework to condition Boltzmann generators for sampling cyclic peptide conformations without retraining, using chemically informed loss functions and geometric constraints.