How AI and ML Are Fueling Drug Discovery: Taking Compute and Storage to the Next Level

How AI and ML Are Fueling Drug Discovery: Taking Compute and Storage to the Next Level

This IDC Perspective highlights the critical focus of the life science industry on leveraging Al and ML to accelerate drug discovery. It dwells upon some of the infrastructural challenges and complexities (including compute and storage) that the industry has been facing, provides an insight into the burgeoning and diverse market landscape of players both small and large, and calls out some of the unique success stories that are transforming the drug discovery landscape. It provides guidance to the technology buyer on the implementation of these technologies."Applications of AI and ML in drug discovery are vast and range from target identification and validation, compound screening and lead discovery, and identification of prognostic biomarkers to drug repurposing, to name a few. Strategies for drug discovery may range from the use of DL technologies, such as convolutional neural networks (CNNs) to identify new molecules based on the accurate predictions of binding profiles, to network-driven drug discovery, wherein a potential drug's ability to influence disease networks, rather than specific targets, is evaluated using large, proprietary databases and tailored computational tools, to the use of de novo drug design, wherein compounds that accurately meet the structural criteria required to bind specific targets are identified. Technologies such as AI and ML, knowledge graphs, and GPU-powered transformer models are being more than successfully implemented across all these modalities today. What was once considered hype is today rapidly becoming reality," says Dr. Nimita Limaye, research VP, Life Sciences R&D Strategy and Technology at IDC.

Please Note: Extended description available upon request.