Genentech, a member of the Roche Group, has established a long-term research partnership with hardware giant NVIDIA. In this strategic alliance, Genentech will leverage its expertise in artificial intelligence and rich biological datasets, combined with NVIDIA's computing power and AI capabilities. The collaboration aims to enhance Genentech's machine learning algorithms through NVIDIA's DGX Cloud.

Under the leadership of CEO Jensen Huang, this move will not only improve Genentech's scientific models but also potentially open up broader platforms for NVIDIA. Huang believes that this partnership will serve as a catalyst for transformative change in the healthcare field, particularly in accelerating the drug discovery process.

This collaboration primarily supports Genentech's AI and machine learning (ML) teams, who are actively developing foundational models in various therapeutic areas. Additionally, it accelerates the implementation of Genentech's innovative "lab in a loop" strategy, where experimental data guides computational models. These models then generate predictions, which are tested in the laboratory, and the results are fed back to enhance and refine the computational models, forming a continuous improvement loop.

During their collaboration, both Genentech's public and proprietary data will be utilized, with strict control over access to the company's private data. It is worth noting that NVIDIA will not directly access Genentech's proprietary data without explicit permission for specific projects.

Earlier this year, NVIDIA also made waves with its BioNeMo Cloud, an integral part of its AI infrastructure suite. The platform offers pre-trained AI models, which have proven crucial in accelerating the drug discovery process for renowned companies like Amgen and various startups.

Not only BioNeMo, but NVIDIA also plays a significant role in advancing drug discovery through its GPU-accelerated platform, NVIDIA Clara. NVIDIA Clara integrates AI, data analytics, simulation, and visualization technologies to rapidly screen vast chemical libraries, identify potential drug candidates, model protein structures and dynamics to understand their roles and interactions with drugs, design new molecules with desired properties, simulate the impact of drugs in the human body, and visualize discoveries from drug discovery experiments.