![]() ![]() Further advances came from the release of common maths libraries such as fast Fourier transforms and basic linear algebra subroutines, which were foundational to scientific computing. In 2007, NVIDIA released Compute Unified Device Architecture (CUDA) as an extension of the C programming language, together with compilers and debuggers, opening the floodgates for porting computationally intensive workloads into GPU accelerators. The earliest attempts to use GPUs for scientific purposes employed the programmable shader language to run calculations. Originally developed to accelerate three-dimensional graphics, the benefits of GPUs for powerful parallel computing were quickly praised by the scientific community. ![]() We conclude by discussing the impacts of GPU acceleration and deep learning models on the global democratization of the field of drug discovery that may lead to efficient exploration of the ever-expanding chemical universe to accelerate the discovery of novel medicines. We 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. In this Review, we 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. This revolution has largely been attributed to the unprecedented advances in highly parallelizable graphics processing units (GPUs) and the development of GPU-enabled algorithms. ![]() Deep learning has disrupted nearly every field of research, including those of direct importance to drug discovery, such as medicinal chemistry and pharmacology. ![]()
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