AMPD Ventures Inc. has signed a deal with Variational AI Incto supply machine learning infrastructure hosted at AMPD’s DC1 sustainable data centre. Variational AI is the developer of Enki, an artificial intelligence-powered small molecule discovery service designed to help discover new molecules for pharma research.
Variational is building on the founders’ expertise in machine learning, reflected in more than forty publications in the area of artificial intelligence research.
Variational uses Graphics Processing Units (GPU) to perform the complex calculations that enable them to discover novel molecules that may be used to develop new drugs. GPU’s are a critical component of high-performance computing for artificial intelligence and machine learning. While a Central Processing Unit (CPU) is good at handling multiple tasks, GPU’s are typically designed to handle a few specific tasks very fast in parallel. So far, these tasks have also included the mining of crypto currencies and processing the high quality graphics of the latest 3D games. GPU’s can solve the complex math problems for certain things like machine learning better than traditional CPUs.
About AMPD Ventures Inc.
AMPD specializes in providing high-performance cloud and computing solutions for low-latency applications, including video games and eSports, digital animation and visual effects, and big data collection, analysis and visualization. For further information concerning the company and its business, please see the long-form prospectus dated October 11, 2019, supporting its application for listing on the CSE. A copy of the prospectus was filed under the company’s profile at SEDAR.
For more information on AMPD, please visit http://www.ampd.tech.
About Variational AI
Variational AI offers an early drug discovery service that leverages state-of-the-art generative machine learning to discover novel, efficacious, safe and synthesizable small molecules for customers in biopharmaceuticals. Founded in 2019, Variational AI performs multi-property molecular optimization to dramatically reduce the time to discover high-quality drug-like molecules with a higher probability of success in clinical trials.