Practical Cheminformatics

Graphics Processing Units (GPUs) have revolutionized scientific computing.  Scientists have been using GPUs to achieve significant speed-ups in fields ranging from molecular dynamics to machine learning.  Unfortunately, programming GPUs is a rather painful process that requires considerable expertise. Fortunately for those of us who'd prefer to forgo the travails of CUDA programming, NVIDIA has released the RAPIDS library, which makes it easy to perform a wide array of data science operations on a GPU.  In this post, I'll present a few examples of how we can use RAPIDS to speed-up a few tasks that we commonly perform in Cheminformatics.  As usual, a Jupyter notebook containing all of the code associated with this post is available on GitHub . 2020 -06-23 I made a couple of changes to the code that slightly changed the runtimes and the trustworthiness values for t-SNE.  The conclusions are the same, RAPIDS ROCKS! Installation I've been following RAPIDS since its ini