Weifeng Li

Filling the Missing Puzzle: Integrating Interaction and Evolutionary Information to Enhance Protein Fitness Prediction in Inverse Folding

This paper introduces zPolaron, a graph neural network for functional protein design that integrates structural, interaction, and evolutionary information, improving fitness prediction beyond structural compatibility.

Learning the native-like codons with a 5'UTR and RNA secondary structure aided species-informed transformer model

TransCodon is a Transformer-based model that predicts optimal synonymous codons by integrating species-specific information, 5'UTRs, and RNA structure, improving translational efficiency in heterologous hosts.

Benchmarking AlphaFold3-like Methods for Protein-Peptide Complex Prediction

This study benchmarks AlphaFold3-like methods for predicting protein-peptide complex structures, showing improved accuracy but challenges in binding site prediction.

ThermoLink: Bridging Disulfide Bond and EnzymeThermostability through Database Construction andMachine Learning Prediction

The paper curates a database of disulfide bonds and their effects on protein thermostability, develops machine learning models to predict beneficial disulfide bonds, and demonstrates the utility of AlphaFold2 for this task.

A New Paradigm for Applying Deep Learning to Protein-Ligand Interaction Prediction

This paper introduces IGModel, a deep learning framework that predicts protein-ligand docking RMSD and binding strength, achieving state-of-the-art performance on multiple benchmarks.

Fully flexible molecular alignment enables accurate ligand structure modelling

The paper introduces Z-align, a molecular alignment method that improves binding pose prediction by using topological structural information and flexible optimization of bond lengths and angles.

Variational graph encoders: a surprisingly effective generalist algorithm for holistic computer-aided drug design

The paper introduces a generalist model for drug design that predicts multiple properties simultaneously, achieving faster virtual screening and drug development with explainability.