A Systematic Approach to Featurization for Cancer Drug Sensitivity Predictions with Deep Learning. - Austin Clyde; Tom Brettin; Alexander Partin; Maulik Shaulik; Hyun Seung Yoo; Yvonne A. Evrard; Yitan Zhu; Fangfang Xia; Rick Stevens (CoRR, 2020)

Ensemble Transfer Learning for the Prediction of Anti-Cancer Drug Response. - Yitan Zhu; Thomas S. Brettin; Yvonne A. Evrard; Alexander Partin; Fangfang Xia; Maulik Shukla; Hyun Seung Yoo; James H. Doroshow; Rick Stevens (CoRR, 2020)

IMPECCABLE: Integrated Modeling PipelinE for COVID Cure by Assessing Better LEads. - Aymen Al Saadi; Dario Alfè; Yadu N. Babuji; Agastya Bhati; Ben Blaiszik; Alexander Brace; Thomas S. Brettin; Kyle Chard; Ryan Chard; Austin Clyde; Peter V. Coveney; Ian T. Foster; et al. (ICPP, 2021)

Learning curves for drug response prediction in cancer cell lines. - Alexander Partin; Thomas S. Brettin; Yvonne A. Evrard; Yitan Zhu; Hyun Seung Yoo; Fangfang Xia; Songhao Jiang; Austin Clyde; Maulik Shukla; Michael Fonstein; James H. Doroshow; Rick L. Stevens (BMC Bioinform., 2021)

Pandemic Drugs at Pandemic Speed: Accelerating COVID-19 Drug Discovery with Hybrid Machine Learning- and Physics-based Simulations on High Performance Computers. - Agastya P. Bhati; Shunzhou Wan; Dario Alfè; Austin R. Clyde; Mathis Bode; Li Tan; Mikhail Titov; André Merzky; Matteo Turilli; Shantenu Jha; Roger R. Highfield; Walter Rocchia; et al. (CoRR, 2021)

Protein-Ligand Docking Surrogate Models: A SARS-CoV-2 Benchmark for Deep Learning Accelerated Virtual Screening. - Austin Clyde; Thomas S. Brettin; Alexander Partin; Hyun Seung Yoo; Yadu N. Babuji; Ben Blaiszik; André Merzky; Matteo Turilli; Shantenu Jha; Arvind Ramanathan; Rick Stevens (CoRR, 2021)

A cross-study analysis of drug response prediction in cancer cell lines. - Fangfang Xia; Jonathan E. Allen; Prasanna Balaprakash; Thomas S. Brettin; Cristina Garcia-Cardona; Austin Clyde; Judith D. Cohn; James H. Doroshow; Xiaotian Duan; Veronika Dubinkina; Yvonne A. Evrard; Ya Ju Fan; et al. (Briefings Bioinform., 2022)

Deep learning methods for drug response prediction in cancer: predominant and emerging trends. - Alexander Partin; Thomas S. Brettin; Yitan Zhu; Oleksandr Narykov; Austin Clyde; Jamie C. Overbeek; Rick L. Stevens (CoRR, 2022)

An Automation Framework for Comparison of Cancer Response Models Across Configurations. - Justin M. Wozniak; Rajeev Jain; Andreas Wilke; Rylie Weaver; Alexander Partin; Thomas S. Brettin; Rick Stevens (e-Science, 2023)

Entropy-Based Regularization on Deep Learning Models for Anti-Cancer Drug Response Prediction. - Oleksandr Narykov; Yitan Zhu; Thomas S. Brettin; Yvonne A. Evrard; Alexander Partin; Maulik Shukla; Priyanka Vasanthakumari; James H. Doroshow; Rick Stevens (SC Workshops, 2023)

Variational and Explanatory Neural Networks for Encoding Cancer Profiles and Predicting Drug Responses. - Tianshu Feng; Rohan Gnanaolivu; Abolfazl Safikhani; Yuanhang Liu; Jun Jiang; Nicholas Chia; Alexander Partin; Priyanka Vasanthakumari; Yitan Zhu; Chen Wang (CoRR, 2024)

Assessing Reusability of Deep Learning-Based Monotherapy Drug Response Prediction Models Trained with Omics Data. - Jamie C. Overbeek; Alexander Partin; Thomas S. Brettin; Nicholas Chia; Oleksandr Narykov; Priyanka Vasanthakumari; Andreas Wilke; Yitan Zhu; Austin Clyde; Sara Jones; Rohan Gnanaolivu; Yuanhang Liu; et al. (CoRR, 2024)

Data imbalance in drug response prediction: multi-objective optimization approach in deep learning setting. - Oleksandr Narykov; Yitan Zhu; Thomas S. Brettin; Yvonne A. Evrard; Alexander Partin; Fangfang Xia; Maulik Shukla; Priyanka Vasanthakumari; James H. Doroshow; Rick L. Stevens (Briefings Bioinform., 2025)

Benchmarking community drug response prediction models: datasets, models, tools, and metrics for cross-dataset generalization analysis. - Alexander Partin; Priyanka Vasanthakumari; Oleksandr Narykov; Andreas Wilke; Natasha Koussa; Sara E. Jones; Yitan Zhu; Jamie C. Overbeek; Rajeev Jain; Gayara Demini Fernando; Cesar Sanchez-Villalobos; Cristina Garcia-Cardona; et al. (CoRR, 2025)