8 citations · 20 across the 8 of their papers we have counts for
8 papers
Scaffold-Based Multi-Objective Drug Candidate Optimization
Agustin Kruel, Andrew D. McNaughton, Neeraj Kumar
In therapeutic design, balancing various physiochemical properties is crucial for molecule development, similar to how Multiparameter Optimization (MPO) evaluates multiple variable…
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…
Spatial Graph Attention and Curiosity-driven Policy for Antiviral Drug Discovery
Yulun Wu, Mikaela Cashman, Nicholas Choma +13
We developed Distilled Graph Attention Policy Network (DGAPN), a reinforcement learning model to generate novel graph-structured chemical representations that optimize user-defined…
Evening the Score: Targeting SARS-CoV-2 Protease Inhibition in Graph Generative Models for Therapeutic Candidates
Jenna Bilbrey, Logan Ward, Sutanay Choudhury +2
We examine a pair of graph generative models for the therapeutic design of novel drug candidates targeting SARS-CoV-2 viral proteins. Due to a sense of urgency, we chose well-valid…
Artificial Intelligence based Autonomous Molecular Design for Medical Therapeutic: A Perspective
Rajendra P. Joshi, Neeraj Kumar
Domain-aware machine learning (ML) models have been increasingly adopted for accelerating small molecule therapeutic design in the recent years. These models have been enabled by s…
Benchmarking Deep Graph Generative Models for Optimizing New Drug Molecules for COVID-19
Logan Ward, Jenna A. Bilbrey, Sutanay Choudhury +2
Design of new drug compounds with target properties is a key area of research in generative modeling. We present a small drug molecule design pipeline based on graph-generative mod…