03 PUBLICATIONS
Our Research
Our team publishes cutting-edge research in the fields of artificial intelligence, computational biology, and drug discovery. Below you'll find our latest peer-reviewed publications.
A Review Paper on AI-Driven Protein Design (AIP)
Nature Reviews Bioengineering (2025)
Large language models for scientific discovery in molecular property prediction
Nature Machine Intelligence 7, 437–447 (2025)
DOI: https://doi.org/10.1038/s42256-025-00994-z
Abstract
Recent advances in large language models have demonstrated remarkable capabilities in various domains, including natural language processing and code generation. In this work, we extend these capabilities to molecular property prediction, a critical task in drug discovery. We propose a novel approach that leverages the knowledge embedded within large language models to enhance the prediction of molecular properties, leading to more accurate and interpretable results compared to traditional methods.
Physicochemical graph neural network for learning protein–ligand interaction fingerprints from sequence data
Nature Machine Intelligence 6, 673–687 (2024)
DOI: https://doi.org/10.1038/s42256-024-00847-1
Abstract
Understanding protein-ligand interactions is fundamental to drug discovery and development. We present a novel physicochemical graph neural network architecture designed to learn protein-ligand interaction fingerprints directly from sequence data. Our approach integrates physicochemical properties of amino acids and small molecules to create more informative representations, leading to improved prediction accuracy and interpretability of binding interactions.