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j u ly • a u g u s t • s e p t e m b e r 2 0 2 1 c g w 3 3 can perform physics-based simulations designed to model and compute properties of novel molecules. Schrödinger has devoted decades to refining computational algorithms and uses Nvidia GPUs to generate and evaluate peta- bytes of data to accelerate drug discovery, which is a dramatic improvement over the traditional process of slow and expensive lab work. With accelerated computing, millions of drug candidates can be screened at a time. Researchers at Oak Ridge National Laboratory (ORNL) and Scripps Research have shown that this process, which has traditionally taken years, can be complet- ed in hours with accelerated computing. Using AutoDock on ORNL's supercomputer with 27,648 Nvidia GPUs, they were able to screen more than 25,000 molecules per second and dock one billion compounds in less than 12 hours. This represents a speed- up of more than 50X, compared to running AutoDock with just CPUs. COMPUTING ADVANCES TEACH THE LANGUAGE OF DISCOVERY Transformer-based neural network archi- tectures, which have become available only in the last few years, allow researchers to leverage self-supervised training methods that avoid the need for large labeled data- sets, which is a significant barrier in building any AI model. BioMegatron, a transformer- based AI natural language processing (NLP) model that understands biomedical language, offers great promise of bringing together all types of clinical and scientif- ic data from R&D biology and chemistry e-notebooks, clinical trial data at pharma- ceutical companies, and hospital patient reports and lab work. It's just like if you went to visit a new country to learn a new language, transform- ers are skilled at learning the language of many diverse data types using unsupervised learning and fine-tuned to a specific task using supervised learning. NLP models have demonstrated human capability in Q&A, summarization, and language generation, and most recently, transformers are being used for image generation and analysis. Nvidia is collaborating with AstraZeneca on a transformer-based generative AI NLP model that reads SMILES, the text language for chemical compounds. This is based on Megatron, a giant transformer model that is fast, powerful, and utilizes multiple GPUs in parallel. Large transformer models can be trained in a similar amount of time compared to their smaller counterparts and demonstrate improved performance. Even with Megatron, a trillion-parameter model will take about three to four months to train on Nvidia's Selen supercomputer. With biomedical data at the scale of peta- bytes and learning at the scale of billions, and soon trillions, of parameters, transform- er AI models are helping life sciences do and find much more than expected. ROAD TO PERSONALIZED MEDICINE End-to-end genomic analysis, from a patient's blood sample, to sequencing, to analysis, to final clinical results, is a data- intensive process. The cost of genomic se- quencers has dramatically decreased, which is wonderful, as more people can afford to sequence patients' DNA. With more people sequencing DNA, there is more data being generated that needs to be interpreted accurately and efficiently. Large population studies are in a race to analyze thousands of whole genomes to find genetic variants of diseases in specific populations, cancer centers are eager to identify the right variants for specific can- cers, pediatric units utilize genomic analysis when a child is not thriving to identify a rare disease, and researchers on the cutting edge of genomic discovery need fast tools to publish their findings. Lightning-speed genomic analysis in minutes versus hours or days can have a significant impact on scientific discovery and therapeutic treatments for patients. A hospital can now quickly sequence a baby's DNA to figure out if the infant has a genetic variant that is causing symptoms or a dis- ease, and offer the right treatments based on that variant. Besides utilizing GPUs to expedite genomic analysis, GPUs are being used to build AI models for genomic discovery. There are AI-based variant callers that learn statistical relationships that are showing amazing accuracy for secondary genomic analysis. There are also AI models to help denoise data, such as with AtacWorks for ATAC-seq data. AtacWorks brings down the cost and time needed for rare and single-cell experiments. AI AT THE HOSPITAL EDGE One place you might not expect AI to be is in the operating room. This new generation of medical devices is equipped with dozens of real-time AI applications that provide support at each step of the clinical experi- ence, automating patient setup, improving image quality, and analyzing data streams to deliver critical insights to caregivers. Medical Instruments, like surgical robots and endoscopes, are mounted with camer- as, sending a live video feed to the clinicians operating the devices. Capturing these video streams and applying computer vision AI to the video content can equip medical professionals with the tools to guide surgeons, detect and measure anomalies, or provide alerts for urgent cases, such as a stroke. AI models for streaming data in health care will help the clinical community greatly to identify, measure, and report on surgical findings more quickly. These smart sensors are soware- defined, which allows them to be regularly up- dated with AI algorithms as they continuously learn and improve — a capability that is essen- tial to connecting research breakthroughs to the day-to-day practice of medicine. Medical imaging from radiology and pathol- ogy was one of the first places to witness the benefits of AI models and has been adopted in many areas, from reporting, to detection, to measurements. AI is already helping radiolo- gists to quickly detect and classify anomalies, as well as prioritize work lists based on urgency of cases. In areas where there is a shortage of clinical teams, AI is helping to triage patients based on a "first pass" of a DICOM, the stan- dard for communications and management of medical imagery information, to detect any emergency findings. The health-care and life-sciences industry is embracing and adopting AI at every step. With the deluge of patient data, R&D drug development data, and clinical data, institutions are investing in AI to accelerate their workflows, bring disparate patient data together, accurately analyze genomes and visualize protein 3D structures, monitor pa- tients, and optimize patient experiences and findings. Continued advances in AI models and uses of GPUs will continue to revolution- ize life-science research and health care. Vanessa Braunstein is the health-care AI product marketing lead at Nvidia.