Search Results for author: Austin Clyde

Found 9 papers, 6 papers with code

Protein-Ligand Docking Surrogate Models: A SARS-CoV-2 Benchmark for Deep Learning Accelerated Virtual Screening

1 code implementation13 Jun 2021 Austin Clyde, Thomas Brettin, Alexander Partin, Hyunseung Yoo, Yadu Babuji, Ben Blaiszik, Andre Merzky, Matteo Turilli, Shantenu Jha, Arvind Ramanathan, Rick Stevens

Our analysis of the speedup explains that to screen more molecules under a docking paradigm, another order of magnitude speedup must come from model accuracy rather than computing speed (which, if increased, will not anymore alter our throughput to screen molecules).

Spatial Graph Attention and Curiosity-driven Policy for Antiviral Drug Discovery

4 code implementations ICLR 2022 Yulun Wu, Mikaela Cashman, Nicholas Choma, Érica T. Prates, Verónica G. Melesse Vergara, Manesh Shah, Andrew Chen, Austin Clyde, Thomas S. Brettin, Wibe A. de Jong, Neeraj Kumar, Martha S. Head, Rick L. Stevens, Peter Nugent, Daniel A. Jacobson, James B. Brown

We developed Distilled Graph Attention Policy Network (DGAPN), a reinforcement learning model to generate novel graph-structured chemical representations that optimize user-defined objectives by efficiently navigating a physically constrained domain.

Drug Discovery Graph Attention

Scaffold Embeddings: Learning the Structure Spanned by Chemical Fragments, Scaffolds and Compounds

no code implementations11 Mar 2021 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.

Drug Discovery Representation Learning

Learning Curves for Drug Response Prediction in Cancer Cell Lines

1 code implementation25 Nov 2020 Alexander Partin, Thomas Brettin, Yvonne A. Evrard, Yitan Zhu, Hyunseung Yoo, Fangfang Xia, Songhao Jiang, Austin Clyde, Maulik Shukla, Michael Fonstein, James H. Doroshow, Rick Stevens

In contrast, a GBDT with hyperparameter tuning exhibits superior performance as compared with both NNs at the lower range of training sizes for two of the datasets, whereas the mNN performs better at the higher range of training sizes.

Regression Enrichment Surfaces: a Simple Analysis Technique for Virtual Drug Screening Models

1 code implementation1 Jun 2020 Austin Clyde, Xiaotian Duan, Rick Stevens

We present a new method for understanding the performance of a model in virtual drug screening tasks.

Targeting SARS-CoV-2 with AI- and HPC-enabled Lead Generation: A First Data Release

1 code implementation28 May 2020 Yadu Babuji, Ben Blaiszik, Tom Brettin, Kyle Chard, Ryan Chard, Austin Clyde, Ian Foster, Zhi Hong, Shantenu Jha, Zhuozhao Li, Xuefeng Liu, Arvind Ramanathan, Yi Ren, Nicholaus Saint, Marcus Schwarting, Rick Stevens, Hubertus van Dam, Rick Wagner

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 respiratory syndrome coronavirus 2 (SARS-CoV-2).

A Systematic Approach to Featurization for Cancer Drug Sensitivity Predictions with Deep Learning

1 code implementation30 Apr 2020 Austin Clyde, Tom Brettin, Alexander Partin, Maulik Shaulik, Hyunseung Yoo, Yvonne Evrard, Yitan Zhu, Fangfang Xia, Rick Stevens

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 drug response predictions.

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