Geoffrey I. Webb

CellReg3D integrates DNA language models and 3D genome structure to model cell-type-specific gene regulation, achieving state-of-the-art performance in predicting epigenetic marks and gene expression.

PSICHIC+ is a bioactivity foundation model that predicts binding affinities from sequence data, improving hit identification and hit-to-lead optimization using heterogeneous bioactivity data.

ACENet predicts enzyme pHmin using a graph neural network integrating structural and evolutionary features, achieving high accuracy and stability across conditions.

The paper develops a disease-specific histone modification prediction model using a large language model and MoE architecture, achieving high AUROC scores and identifying AD-associated variants.

KcatNet predicts enzyme catalytic efficiency using geometric deep learning, outperforming existing methods and providing insights into enzyme-substrate interactions.

The paper introduces Mimosa, a Transformer-based model for miRNA target prediction that incorporates non-canonical base-pairing patterns, achieving better performance than traditional methods.

SCREEN is a graph neural network that predicts catalytic residues and assesses mutation tolerance in enzymes by integrating structural and functional information.

PSICHIC predicts protein-ligand interactions from sequence data using a physicochemical graph neural network, achieving state-of-the-art accuracy and interpretability in binding affinity and functional effect prediction.

PRISMOID is a 3D structure database for post-translational modifications and mutations, providing structural and functional insights into PTM sites and their interactions.