Wei Lu

Accurate Protein-Protein Interactions Modeling through Physics-informed Geometric Invariant Learning

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

OriGene: A Self-Evolving Virtual Disease Biologist Automating Therapeutic Target Discovery

OriGene identifies therapeutic targets by integrating diverse data modalities and outperforms human experts and models in accuracy and robustness.

BindingGYM: A Large-Scale Mutational Dataset Toward Deciphering Protein-Protein Interactions

The paper curates a large dataset of protein-protein interaction mutations to improve deep learning models for predicting interaction strengths and applications in drug discovery.

CACHE Challenge #1: targeting the WDR domain of LRRK2, a Parkinson's Disease associated protein.

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.

AlphaFold3, a secret sauce for predicting mutational effects on protein-protein interactions

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.

A structural dynamics model for how CPEB3 binding to SUMO2 can regulate translational control in dendritic spines

This paper proposes a computational model showing how CPEB3-SUMO2 interactions regulate translational control in dendritic spines by shifting mRNA binding equilibrium.

OpenAWSEM with Open3SPN2: a fast, flexible, and accessible framework for large-scale coarse-grained biomolecular simulations

This paper introduces OpenAWSEM and Open3SPN2, GPU-accelerated coarse-grained simulation tools for proteins and DNA, enabling faster and more flexible modeling of biomolecular systems.