26 citations · 33 across the 5 of their papers we have counts for
5 papers
Assessing Reusability of Deep Learning-Based Monotherapy Drug Response Prediction Models Trained with Omics Data
Jamie C. Overbeek, Alexander Partin, Thomas S. Brettin +21
Cancer drug response prediction (DRP) models present a promising approach towards precision oncology, tailoring treatments to individual patient profiles. While deep learning (DL)…
CACTUS: Chemistry Agent Connecting Tool-Usage to Science
Andrew D. McNaughton, Gautham Ramalaxmi, Agustin Kruel +3
Large language models (LLMs) have shown remarkable potential in various domains, but they often lack the ability to access and reason over domain-specific knowledge and tools. In t…
Dynamic Molecular Graph-based Implementation for Biophysical Properties Prediction
Carter Knutson, Gihan Panapitiya, Rohith Varikoti +1
Neural Networks (GNNs) have revolutionized the molecular discovery to understand patterns and identify unknown features that can aid in predicting biophysical properties and protei…
De novo design of protein target specific scaffold-based Inhibitors via Reinforcement Learning
Andrew D. McNaughton, Mridula S. Bontha, Carter R. Knutson +2
Efficient design and discovery of target-driven molecules is a critical step in facilitating lead optimization in drug discovery. Current approaches to develop molecules for a targ…
Decoding the Protein-ligand Interactions Using Parallel Graph Neural Networks
Carter Knutson, Mridula Bontha, Jenna A. Bilbrey +1
Protein-ligand interactions (PLIs) are fundamental to biochemical research and their identification is crucial for estimating biophysical and biochemical properties for rational th…