Meet MolXProt: A Fresh Way to Predict How Drugs Bind

Tue, 21 Oct 2025 00:00:00 +0000

What if you could predict how a drug molecules binds to its target protein, while being fast, reliable, and with more chemical interpretability than ever? That’s what MolXProt was designed to achieve.

In the world of drug discovery, predicting the binding affinities between protein and ligand pairs is key. However, traditional methods such as docking and empirical scoring often fail to generalize to novel protein families and new chemistries.

MolXProt was created to give a fresh new look to the problem: A transformer-based graph neural network that fuses graph-based ligand representations with protein language model embeddings via a bidirectional multi-head cross-attention. The results? We were able to obtain a scalable and interpretable model that delivers high-quality predictions and pave the way to understand how drugs and proteins “talk” at the atomic level.

Research on AI-for-Science Systems (AIS²) Lab

Meet MolXProt: A Fresh Way to Predict How Drugs Bind