20 citations · 55 across the 11 of their papers we have counts for
6 papers · 1 filter
Learning Curves for Drug Response Prediction in Cancer Cell Lines
Alexander Partin, Thomas Brettin, Yvonne A. Evrard +9
Motivated by the size of cell line drug sensitivity data, researchers have been developing machine learning (ML) models for predicting drug response to advance cancer treatment. As…
Scalable HPC and AI Infrastructure for COVID-19 Therapeutics
Hyungro Lee, Andre Merzky, Li Tan +15
COVID-19 has claimed more 1 million lives and resulted in over 40 million infections. There is an urgent need to identify drugs that can inhibit SARS-CoV-2. In response, the DOE re…
IMPECCABLE: Integrated Modeling PipelinE for COVID Cure by Assessing Better LEads
Aymen Al Saadi, Dario Alfe, Yadu Babuji +33
The drug discovery process currently employed in the pharmaceutical industry typically requires about 10 years and $2-3 billion to deliver one new drug. This is both too expensive…
Regression Enrichment Surfaces: a Simple Analysis Technique for Virtual Drug Screening Models
Austin Clyde, Xiaotian Duan, Rick Stevens
We present a new method for understanding the performance of a model in virtual drug screening tasks. While most virtual screening problems present as a mix between ranking and cla…
Targeting SARS-CoV-2 with AI- and HPC-enabled Lead Generation: A First Data Release
Yadu Babuji, Ben Blaiszik, Tom Brettin +15
Researchers across the globe are seeking to rapidly repurpose existing drugs or discover new drugs to counter the the novel coronavirus disease (COVID-19) caused by severe acute re…
A Systematic Approach to Featurization for Cancer Drug Sensitivity Predictions with Deep Learning
Austin Clyde, Tom Brettin, Alexander Partin +6
By combining various cancer cell line (CCL) drug screening panels, the size of the data has grown significantly to begin understanding how advances in deep learning can advance dru…