20 citations · 47 across the 8 of their papers we have counts for
13 papers
Deep learning methods for drug response prediction in cancer: predominant and emerging trends
Alexander Partin, Thomas S. Brettin, Yitan Zhu +4
Cancer claims millions of lives yearly worldwide. While many therapies have been made available in recent years, by in large cancer remains unsolved. Exploiting computational predi…
Deep Surrogate Docking: Accelerating Automated Drug Discovery with Graph Neural Networks
Ryien Hosseini, Filippo Simini, Austin Clyde +1
The process of screening molecules for desirable properties is a key step in several applications, ranging from drug discovery to material design. During the process of drug discov…
Scaffold-Induced Molecular Graph (SIMG): Effective Graph Sampling Methods for High-Throughput Computational Drug Discovery
Austin Clyde, Ashka Shah, Max Zvyagin +2
Scaffold based drug discovery (SBDD) is a technique for drug discovery which pins chemical scaffolds as the framework of design. Scaffolds, or molecular frameworks, organize the de…
Protein-Ligand Docking Surrogate Models: A SARS-CoV-2 Benchmark for Deep Learning Accelerated Virtual Screening
Austin Clyde, Thomas Brettin, Alexander Partin +8
We propose a benchmark to study surrogate model accuracy for protein-ligand docking. We share a dataset consisting of 200 million 3D complex structures and 2D structure scores acro…
A cross-study analysis of drug response prediction in cancer cell lines
Fangfang Xia, Jonathan Allen, Prasanna Balaprakash +21
To enable personalized cancer treatment, machine learning models have been developed to predict drug response as a function of tumor and drug features. However, most algorithm deve…
Scaffold Embeddings: Learning the Structure Spanned by Chemical Fragments, Scaffolds and Compounds
Austin Clyde, Arvind Ramanathan, Rick Stevens
Molecules have seemed like a natural fit to deep learning's tendency to handle a complex structure through representation learning, given enough data. However, this often continuou…