David Earl Hostallero

AtomWeaver enables non-canonical peptide design by using a geometric prior to generate side-chain atoms without restricting residue identity, achieving high agreement with experimental results.

This study evaluates interpretable deep learning models for drug response prediction, finding that explicit pathway integration reduces accuracy and that black-box models often perform best.

The paper introduces two deep learning models for drug response prediction that use bipartite graphs to improve accuracy by incorporating drug structure and cell line relationships.

This paper develops a deep learning framework called TINDL to predict cancer patient drug response and identify drug sensitivity biomarkers using preclinical samples, showing improved performance over other models.