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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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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Drug discovery aims to develop new therapeutic agents that effectively target diseases while minimizing side effects for patients. Using multimodal data��such as molecular structures, cellular images, sequences, and unstructured data��can be highly valuable in identifying novel and safe drug candidates. Yet, creating multimodal AI models for computer-aided drug discovery is challenging.
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Now available��NIM Agent Blueprints for digital humans, multimodal PDF data extraction, and drug discovery.
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Geneformer is a recently introduced and powerful AI model that learns gene network dynamics and interactions using transfer learning from vast single-cell transcriptome data. This tool enables researchers to make accurate predictions about gene behavior and disease mechanisms even with limited data, accelerating drug target discovery and advancing understanding of complex genetic networks in��
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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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NVIDIA Parabricks v4.3 was released at NVIDIA GTC 2024, introducing new tooling and workflows that bring acceleration and the latest AI techniques to multiple omics data types. In addition to analyzing DNA and RNA, you can now also analyze methylation, single-cell, and spatial omics workloads at high speed and high accuracy with the power of GPUs and generative AI. Parabricks v4.3��
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The rise in generative AI adoption has been remarkable. Catalyzed by the launch of OpenAI��s ChatGPT in 2022, the new technology amassed over 100M users within months and drove a surge of development activities across almost every industry. By 2023, developers began POCs using APIs and open-source community models from Meta, Mistral, Stability, and more. Entering 2024��
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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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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 BioNeMo Framework has been released and is now generally available to download on NGC, enabling researchers to build and deploy generative AI, large language models (LLMs), and foundation models in drug discovery applications. The BioNeMo platform includes managed services, API endpoints, and training frameworks to simplify, accelerate, and scale generative AI for drug discovery.
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Generative AI is primed to transform the world��s industries and to solve today��s most important challenges. To enable enterprises to take advantage of the possibilities with generative AI, NVIDIA has launched NVIDIA AI Foundations and the NVIDIA NeMo framework, powered by NVIDIA DGX Cloud. NVIDIA AI Foundations are a family of cloud services that provide enterprises with a simplified��
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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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New SDKs are available in the NGC catalog, a hub of GPU-optimized deep learning, machine learning, and HPC applications. With highly performant software containers, pretrained models, industry-specific SDKs, and Jupyter notebooks available, AI developers and data scientists can simplify and reduce complexities in their end-to-end workflows. This post provides an overview of new and updated��
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Humanity has seen major scientific breakthroughs directly related to discoveries that do not share the glamor of the breakthrough they enabled. Sir Alexander Fleming��s penicillin gave rise to effective treatments for infections like pneumonia, but penicillin��s importance outshines a technology known as the Petri dish, invented by a German physician. It was in a Petri dish that penicillin was��
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At GTC 2022, NVIDIA introduced enhancements to AI frameworks for building real-time speech AI applications, designing high-performing recommenders at scale, applying AI to cybersecurity challenges, creating AI-powered medical devices, and more. Showcased real-world, end-to-end AI frameworks highlighted the customers and partners leading the way in their industries and domains.
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Recent advances in large language models (LLMs) have fueled state-of-the-art performance for NLP applications such as virtual scribes in healthcare, interactive virtual assistants, and many more. To simplify access to LLMs, NVIDIA has announced two services: NeMo LLM for customizing and using LLMs, and BioNeMo, which expands scientific applications of LLMs for the pharmaceutical and��
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