Jixian Zhang

ProTact improves protein-protein interaction prediction using a geometric graph neural network with physics-informed constraints, outperforming existing methods on benchmark datasets.

The paper evaluates computational hit-finding methods for targeting the WDR domain of LRRK2, a Parkinson's disease protein, finding that diverse strategies yielded weakly potent compounds.

The paper proposes a cross-modal graph contrastive learning method that uses cellular images to improve molecular representation learning for downstream tasks.

This paper benchmarks AlphaFold3 against SKEMPI to assess its ability to predict mutational effects on protein-protein interactions and compares it with force field and deep learning methods.