The transformational role of GPU computing and deep learning in drug discovery
Created on April 1|Last edited on April 1
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Is there a relation between advancements in drug discovery and GPU computing? How has modern drug discovery benefited from the recent explosion of DL models and GPU parallel computing? Who have been the major beneficiaries of advancements in GPU computation? All these questions have been answered in a recently published review paper on Nature titled: The transformational role of GPU computing and deep learning in drug discovery.
Abstract
Deep learning has disrupted nearly every field of research, including those of direct importance to drug discovery, such as medicinal chemistry and pharmacology. This revolution has largely been attributed to the unprecedented advances in highly parallelizable graphics processing units (GPUs) and the development of GPU-enabled algorithms.
In this Review, the authors present a comprehensive overview of historical trends and recent advances in GPU algorithms and discuss their immediate impact on the discovery of new drugs and drug targets. They also cover the state-of-the-art of deep learning architectures that have found practical applications in both early drug discovery and consequent hit-to-lead optimization stages, including the acceleration of molecular docking, the evaluation of off-target effects and the prediction of pharmacological properties.
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