Huan Yee Koh

Bioactivity foundation model for hit identification and hit-to-lead optimization from sequence data

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.

OmniSyn unifies target-aware molecular generation and optimization within a synthesis-native LLM framework across the human proteome

OmniSyn generates and optimizes target-specific molecules with synthesis traces using a sequence-conditioned LLM, achieving state-of-the-art performance on drug discovery benchmarks.

Systematic Targeting of Protein Complexes with Molecular COUPLrs

The paper introduces molecular COUPLrs to systematically target protein complexes, identifying new glue classes and their interactions in cancer cell lines.

ACENet: a graph neural network for predicting enzyme pHmin using structure and sequence insights

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

KcatNet: A Geometric Deep Learning Framework for Genome-Wide Prediction of Enzyme Catalytic Efficiency

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

PSICHIC: physicochemical graph neural network for learning protein-ligand interaction fingerprints from sequence data

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.