From searching a seemingly endless molecule database to simulating how these molecules interact with the human body’s complex biochemistry, NVIDIA-powered solutions enable pharmaceutical companies to improve analysis, efficiency, and scalability.
New drugs are increasingly expensive to bring to market. Learn how AI and accelerated computing are improving every phase of drug discovery with faster, more accurate insights.
From top industry challenges and use cases to applications for fighting COVID-19, this IDC Perspective highlights everything you need to know about the growing importance of neural networks in drug discovery.
NVIDIA Clara Discovery is powering a new machine learning approach that enables a thousand-fold acceleration in molecular properties prediction for developing next-generation therapeutics.
Using NVIDIA GPUs on Azure, UC Riverside studies quantum forces to determine the likelihood a virus will bind with a ligand, speeding the work of pharmaceutical companies seeking treatments.
Schrödinger, using its GPU-powered platform, helps pharmaceutical companies improve the speed and accuracy of drug discovery efforts.
At GTC, experts from leading institutions like University of Washington, University of Toronto, AstraZeneca, and groundbreaking startups like Entos share about the future of AI and deep learning for drug discovery.
In this webinar, learn how molecular dynamics simulations and AI-powered computational chemistry are playing key roles in the fight against COVID-19, providing atomic-scale insights to viral mechanisms, including virus-to-cell fusion, viral protein function, and ultimately possible therapeutics.
Argonne National Labs, as part of the COVID-19 HPC Consortium and the first to purchase NVIDIA DGX™ A100 in May of this year, is using their system to help researchers explore COVID-19 treatments, vaccines, and the spread of the virus.
Learn about the progress achieved by Argonne National Labs and how other consortium GPU-accelerated systems are being used in several research projects.
NVIDIA Clara™ Discovery, a GPU-accelerated computational drug discovery platform, combines AI, data analytics, simulation, and visualization to support cross-disciplinary workflows in drug design and development. Using Clara Discovery, researchers can apply high performance computing applications, pre-trained AI models and domain specific application frameworks in the areas of genomics, protein structure determination, virtual drug screening, medical imaging, natural language processing, and more.
Built with NVIDIA A100 Tensor Core GPUs, NVIDIA DGX A100, is the world’s most advanced AI system, delivering 5 petaFLOPS of performance. Purpose-built for all accelerated computing workloads at scale, the unprecedented computing power of DGX A100 enables researchers to simulate the complexities of molecular dynamics and perform virtual drug screenings in less time.
Explore our NVIDIA Partner Network (NPN) partners who have enterprise-grade, production-ready, GPU-optimized applications that have been tested and validated for performance by NVIDIA.
Clinical Trial Protocol Assessment and Business Intelligence
Using NVIDIA AI, Vyasa’s smart table technology, Synapse, automatically applies deep learning-powered text analytics to extract insights from clinical trial PDF documents into an easy-to-navigate spreadsheet. Within milliseconds, AI finds structured data within unstructured documents by answering questions that relate to the dataset being analyzed with more than 97 percent query accuracy. Instead of 10 days to manually ingest tens of thousands of data points, Vyasa decreases analysis time by 90 percent, down to one day.
Predicting Chemical Properties at Experimental Accuracy
Schrödinger’s advanced computational platform, powered by NVIDIA GPUs, for drug design and discovery combines physics-based modeling with machine learning to quickly and accurately evaluate billions of molecules to accelerate drug discovery. Major pharmaceutical and biotech companies use Schrödinger’s platform for preclinical drug discovery to explore vast chemical space to identify high-quality, novel molecules more rapidly and at a lower cost compared to traditional methods.
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