Le projet THINK

An Intel® Student Ambassador’s success story

New Services on the Intel® Tiber™ Developer Cloud

In this blog post, we introduce an optimized importance sampling routine for GGX materials. Such materials are ubiquitous in games and VFX so our optimizations will benefit to a large number of content creators.

Intel is proud to announce support for PyTorch with DirectML.

Propelling AI workloads with solutions to enable LLMs on a vast range of Intel client platforms

Explore how Intel® Liftoff for Startups is transforming manufacturing through cutting-edge AI innovation.

Developers working on AI applications for Intel platforms have flexibility in their choices to make the development process more streamlined.

Highlights from the University of California Davis HackDavis collegiate hackathon

Drug discovery is becoming slower and more expensive. Eroom’s law –Moore’s Law in reverse – is that the cost of Research and Development (R&D) of all new drugs approved has risen exponentially over the last 60 years. One of the early steps in drug discovery is predicting the absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties of candidate drug molecules. ADMET prediction is essential for understanding how drugs are absorbed, distributed, metabolized, and eliminated within the body, which is crucial for determining the pharmacokinetics and safety of potential drug candidates.  

Machine/Deep Learning, Graph Neural Networks in particular, have become indispensable to predicting these properties. However, the databases containing the chemical structure of compounds and their corresponding ADMET properties are typically proprietary and a closely guarded secret. Federated Machine learning has shown promising results due to its ability to address challenges related to privacy, data decentralization, and collaboration across multiple institutions to realize more accurate and generalizable models.