This project focuses on developing advanced machine learning and foundational models for molecular and protein representation learning. The research aims to integrate physicochemical properties, structural information, and dynamic biological signals into scalable deep learning architectures. By leveraging large-scale pretraining, self-supervised learning, and multimodal modeling, the project seeks to bridge the gap between data-driven representations and real-world biological functionality. The resulting models enable a wide range of applications, including molecular property prediction, drug discovery, protein function annotation, and biomedical data analysis. Ultimately, this research aims to advance AI-driven approaches that can improve our understanding of biological systems and accelerate translational applications in precision medicine and therapeutics.
Methods
Method development for this work is ongoing.