Dr. Pal and his team's research has been published in Nature Communications.

The latest on topological regression for Quantitative Structure-Activity Relationship (QSAR) modeling has been published in Nature Communications. The paper, titled "Topological Regression as an Interpretable and Efficient Tool for Quantitative Structure-Activity Relationship Modeling," is authored by Ruibo Zhang, Daniel Nolte, César Sánchez, S. Ghosh, and Ranadip Pal from the Electrical and Computer Engineering Department at Texas Tech University. 

They proposed a similarity-based regression framework, topological regression (TR), that offers a statistically grounded, computationally fast, and interpretable technique to predict drug responses. Our results indicate that the TR framework provides predictive performance similar to state-of-the-art deep learning models while being significantly more interpretable and scalable.

Read the full article here.