Generative chemistry with AI has the potential to revolutionize how scientists approach drug discovery and development, health, and materials science and engineering. Instead of manually designing molecules with ��chemical intuition�� or screening millions of existing chemicals, researchers can train neural networks to propose novel molecular structures tailored to the desired properties.
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Generative AI, especially with breakthroughs like AlphaFold and RosettaFold, is transforming drug discovery and how biotech companies and research laboratories study protein structures, unlocking groundbreaking insights into protein interactions. Proteins are dynamic entities. It has been postulated that a protein��s native state is known by its sequence of amino acids alone��
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AI has evolved from an experimental curiosity to a driving force within biological research. The convergence of deep learning algorithms, massive omics datasets, and automated laboratory workflows has allowed scientists to tackle problems once thought intractable��from rapid protein structure prediction to generative drug design, increasing the need for AI literacy among scientists.
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Rare diseases are difficult to diagnose due to limitations in traditional genomic sequencing. Wolfgang Pernice, assistant professor at Columbia University, is using AI-powered cellular profiling to bridge these gaps and advance personalized medicine. At NVIDIA GTC 2024, Pernice shared insights from his lab��s work with diseases like Charcot-Marie-Tooth (CMT) and mitochondrial disorders.
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Traditional computational drug discovery relies almost exclusively on highly task-specific computational models for hit identification and lead optimization. Adapting these specialized models to new tasks requires substantial time, computational power, and expertise��challenges that grow when researchers simultaneously work across multiple targets or properties.
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Designing a therapeutic protein that specifically binds its target in drug discovery is a staggering challenge. Traditional workflows are often a painstaking trial-and-error process��iterating through thousands of candidates, each synthesis and validation round taking months if not years. Considering the average human protein is 430 amino acids long, the number of possible designs translates to��
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Antibodies have become the most prevalent class of therapeutics, primarily due to their ability to target specific antigens, enabling them to treat a wide range of diseases, from cancer to autoimmune disorders. Their specificity reduces the likelihood of off-target effects, making them safer and often more effective than small-molecule drugs for complex conditions. As a result��
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AI models for science are often trained to make predictions about the workings of nature, such as predicting the structure of a biomolecule or the properties of a new solid that can become the next battery material. These tasks require high precision and accuracy. What makes AI for science even more challenging is that highly accurate and precise scientific data is often scarce��
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The ability to compare the sequences of multiple related proteins is a foundational task for many life science researchers. This is often done in the form of a multiple sequence alignment (MSA), and the evolutionary information retrieved from these alignments can yield insights into protein structure, function, and evolutionary history. Now, with MMseqs2-GPU, an updated GPU-accelerated��
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The integration of AI in drug discovery is revolutionizing the way researchers approach the development of new treatments for various diseases. Traditional methods are often time-consuming and costly, with the process of bringing a new drug to market taking up to 15 years and costing between $1�C2B. By using AI and advanced computational tools, researchers can now accelerate the��
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Pharmaceutical research demands fast, efficient simulations to predict how molecules interact, speeding up drug discovery. Jiqun Tu, a senior developer technology engineer at NVIDIA, and Ellery Russell, tech lead for the Desmond engine at Schr?dinger, explore advanced GPU optimization techniques designed to accelerate molecular dynamics simulations. In this NVIDIA GTC 2024 session��
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A groundbreaking drug-repurposing AI model could bring new hope to doctors and patients trying to treat diseases with limited or no existing treatment options. Called TxGNN, this zero-shot tool helps doctors find new uses for existing drugs for conditions that might otherwise go untreated. The study, recently published in Nature Medicine and led by scientists from Harvard University��
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Now available��NIM Agent Blueprints for digital humans, multimodal PDF data extraction, and drug discovery.
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Missed GTC or want to replay your favorite training labs? Find it on demand with the NVIDIA GTC Training Labs playlist.
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Predicting 3D protein structures from amino acid sequences has been an important long-standing question in bioinformatics. In recent years, deep learning�Cbased computational methods have been emerging and have shown promising results. Among these lines of work, AlphaFold2 is the first method that has achieved results comparable to slower physics-based computational methods.
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The quest for new, effective treatments for diseases that remain stubbornly resistant to current therapies is at the heart of drug discovery. This traditionally long and expensive process has been radically improved by AI techniques like deep learning, empowered by the rise of accelerated computing. Receptor.AI, a London-based drug discovery company and NVIDIA Inception member��
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The search for viable drugs is one of the most formidable challenges at the intersection of science, technology, and medicine. Mathematically, the odds of randomly stumbling across a good therapeutic candidate are staggeringly small. This is owed primarily to the astronomically large number of ways that just a handful of atoms can be connected together to make what appear at first glance to be��
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Learn how the Francis Crick Institute is using NVIDIA Clara Parabricks to enable key parts of TRACERx EVO, a new program that builds on the discoveries made in the world��s largest long-term lung study.
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Enzymes are vital biological catalysts for a multitude of processes, from cellular metabolism to industrial manufacturing. The applications of artificial intelligence for enzyme generation is an exciting field of research with direct applications in the life sciences. Advances in these scientific challenges are a critical necessity to further advance drug discovery, environmental science��
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NVIDIA T4 was introduced 4 years ago as a universal GPU for use in mainstream servers. T4 GPUs achieved widespread adoption and are now the highest-volume NVIDIA data center GPU. T4 GPUs were deployed into use cases for AI inference, cloud gaming, video, and visual computing. At the NVIDIA GTC 2023 keynote, NVIDIA introduced several inference platforms for AI workloads��
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Creating new drug candidates is a heroic endeavor, often taking over 10 years to bring a drug to market. New supercomputing-scale large language models (LLMs) that understand biology and chemistry text are helping scientists understand proteins, small molecules, DNA, and biomedical text. These state-of-the-art AI models help generate de novo proteins and molecules and predict the 3D��
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The NVIDIA BioNeMo service is now available for early access. At GTC Fall 2022, NVIDIA unveiled BioNeMo, a domain-specific framework and service for training and serving biomolecular large language models (LLMs) for chemistry and biology at supercomputing scale across billions of parameters. The BioNeMo service is domain-optimized for chemical, proteomic, and genomic applications��
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A virtual event designed for healthcare developers and startups, this summit on November 10, 2022 offers a full day of technical talks to reach developers and technical leaders in the EMEA region. Get best practices and insights for applications, from biopharma to medical imaging.
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Drug discovery startup Insilico Medicine��alongside researchers from Harvard Medical School, Johns Hopkins School of Medicine, the Mayo Clinic, and others��used AI to identify more than two dozen gene targets related to amyotrophic lateral sclerosis (ALS). The research findings, which included 17 high-confidence and 11 novel therapeutic targets, were recently published in Frontiers in Aging��
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The field of drug discovery is at a fascinating inflection point. The physics of the problem are understood and calculable, yet quantum mechanical calculations are far too expensive and time consuming. Eroom��s Law observes that drug discovery is becoming slower and more expensive over time, despite improvements in technology. A recent article examining the transformational role of GPU��
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Computational molecular design involves compute-intense calculations that require exceptional processing power. Whether working in pharmaceuticals, biotechnology, agrochemicals, or the fragrance industry, researchers are oftentimes dealing with datasets that encompass millions to billions of compounds. Until recently, this required that companies invest in expensive��
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A new study out of the Massachusetts Institute of Technology (MIT) could arm healthcare workers with the information needed to effectively treat COVID-19 patients. Recently published in the Proceedings of the National Academy of Sciences, the research develops a deep learning model that determines the best drug combinations for fighting the virus, despite having relatively limited data.
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SE(3)-Transformers are versatile graph neural networks unveiled at NeurIPS 2020. NVIDIA just released an open-source optimized implementation that uses 43x less memory and is up to 21x faster than the baseline official implementation. SE(3)-Transformers are useful in dealing with problems with geometric symmetries, like small molecules processing, protein refinement��
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Solving a mystery that stumped scientists for decades, last November a group of computational biologists from Alphabet��s DeepMind used AI to predict a protein��s structure from its amino acid sequence. Not even a year later, a new study offers a more powerful model, capable of computing protein structures in as little as 10 minutes, on one gaming computer. The research��
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As the world battles to reach a scientific breakthrough in the fight against COVID-19, scientists are turning to computing resources to accelerate their research. To help make the process for scientists more accessible, we��re spotlighting a few of the GPU-accelerated applications that developers can use right now in the fight against this virus. Applications like AMBER, GROMACS, NAMD��
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