CAFCW115 Massively Parallel Large-Scale Multi-Model Simulation of Tumor Development including Treatments
19 Dec 2019 | Contributor(s):: Marco Berghoff, Jakob Rosenbauer, Alexander Schug
The temporal and spatial resolution in the microscopy of tissues has increased significantly within the last years, yielding new insights into the dynamics of tissue development and the role of the single-cell within it. A thorough theoretical description of the connection of single-cell...
CAFCW117 A Scalable, Validated Platform for Generative Lead Optimization of De Novo Molecules: Case Study in Discovery of Potent, Selective Aurora Kinase Inhibitors with Favorable Secondary Pharmacology
19 Dec 2019 | Contributor(s):: Andrew Weber
De Novo design of therapeutic agents is currently a slow, expensive process generally relying on a large high throughput screen and several follow up cycles of iterative design to enhance the potency, eliminate safety liabilities, and enable favorable pharmacokinetic behavior. Computer aided drug...
CAFCW 122 Fusion of Structure Based Deep Learning to Accelerate Molecular Docking Predictions
09 Dec 2019 | Contributor(s):: Derek Jones
Modeling interactions with biological targets is a necessary step to begin reasoning about the therapeutic potential of a novel molecule in the drug discovery process. Molecular docking aids drug discovery researchers by searching over potential binding ‘poses’ of a drug molecule,...
CAFCW 104 Deep Kernel Learning for Information Extraction from Cancer Pathology Reports
09 Dec 2019 | Contributor(s):: Devanshu Agrawal, Abhishek Dubey, Georgia Tourassi, Jacob Hinkle
Cancer pathology reports comprise a rich source of data for surveilling cancer incidents and tracking cancer trends across the United States. Cancer registries manually extract key pieces of information from these reports including tumor site, histology, laterality, behavior, grade, and...
CAFCW 105 Acceleration of Hyperparameter Optimization via Task Parallelism for Information Extraction from Cancer Pathology Reports
09 Dec 2019 | Contributor(s):: John Gounley, Hong-Jun Yoon
Recent advances in high-performance computing systems for artificial intelligence enable large-scale training of information extraction models from free-form natural language texts. The development of these models is essential to the cancer surveillance research and automation. In this study, we...