Jiahua Rao

This paper introduces InterAb and InterAb-Opt, computational frameworks that predict and optimize broadly neutralizing antibodies using all-atom modeling and pre-trained language models, showing significant improvements in binding affinity and neutralization activity.

V3Cell predicts organoid responses to chemical perturbations using non-invasive bright-field microscopy, creating virtual 3D cell models that mimic real organoids in structure and developmental dynamics.

This paper introduces EvoSyn, an evolutionary multi-agent framework that combines LLM reasoning with domain validation to generate chemically feasible molecules.

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

MAProt improves protein design by combining structure-based and language model approaches using multi-agent reinforcement learning with Pareto-based optimization, achieving better performance across multiple tasks.

GerNA-Bind predicts RNA-ligand binding specificity using a geometric deep learning framework, achieving improved precision and identifying compounds with experimentally confirmed activity against MALAT1 RNA.

EnzymeCAGE is a geometric foundation model trained on enzyme-reaction pairs to predict enzyme function, de-orphan reactions, identify catalytic sites, and reconstruct biosynthetic pathways.

DrugVNN predicts drug responses with high accuracy and interpretability by integrating gene features and drug representations using a novel attention mechanism.

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.

This paper introduces a graph convolutional network to predict disease-gene associations by leveraging external collaborative graphs and reducing noise through mutual information maximization.

The paper introduces GraphSite, a method using AlphaFold2-predicted structures and graph transformers to predict DNA-binding residues, outperforming existing sequence and structure-based methods.

The paper develops a deep learning framework to integrate multi-omics data for cancer prognosis, showing improved risk prediction across 15 cancers and identifying prognostic markers in breast cancer.

This paper introduces GraphSCI, a method that imputes dropout events in scRNA-seq data using graph convolution networks, outperforming existing methods on simulated and real data.

The paper develops MolRep, a library unifying 16 molecular representation models, and evaluates them on 12 benchmarks to identify top-performing models for property prediction.

This paper introduces TH-GRASP, an XGBoost-based method for predicting genome-wide RNA secondary structure profiles, achieving high AUC values and demonstrating effectiveness in reducing experimental noise.