Ziv Bar-Joseph

Abstract Large Language Models (LLMs) are transforming the process of drug discovery through their ability to operate on chemical data, generate novel chemical structures, and enable natural language interaction between human experts and computational tools. A key question is how to effectively integrate LLMs into drug design projects to generate biologically active drug-like molecules.

Abstract Age is a major risk factor for lung disease. We characterized the changing cellular, transcriptional, and genomic landscape of human lung aging using single-cell RNA sequencing. We find that lung aging is cell-type dyssynchronous, with alveolar epithelial and endothelial cells exhibiting the greatest transcriptional changes. Among alveolar epithelial cells, aging is associated with a decreased relative proportion of surfactant-expressing SPChigh AT2 cells.

1,3 1Ray and Stephanie Lane Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA; 2Department of Computer Science, University of Illinois Chicago, Chicago, Illinois 60607, USA; 3Machine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA Corresponding author: zivbj{at}cs.cmu.edu Footnotes [Supplemental material is available for this article.] Article published...