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Sapio Sciences makes AI-Native drug discovery seamless with NVIDIA BioNeMo

Sapio Sciences, the science-aware™ lab informatics platform, today announced the integration of the NVIDIA BioNeMo platform into the Sapio Lab Informatics Platform. This integration brings AI-driven computational drug discovery directly into Sapio ELN (Electronic Lab Notebook), helping to streamline workflows and improve decision-making in drug discovery. With the BioNeMo platform, researchers…

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[govindhtech]

Utilize Power of Nvidia BioNeMo to Promote Drug Discovery

Nvidia BioNeMo Models

With the integration of NVIDIA NIM, a set of cloud-native microservices, with Amazon Web Services, utilising optimised AI models for healthcare is now simpler than ever.

Through industry-standard application programming interfaces, or APIs, NIM, a component of the NVIDIA AI Enterprise software platform offered on the AWS Marketplace, gives developers access to an expanding library of AI models. With enterprise-grade security and support, the library offers foundation models for drug discovery, medical imaging, and genomics.

NIM may now be accessed through AWS ParallelCluster, an open-source platform for managing and deploying high performance computing clusters on AWS, and Amazon SageMaker, a fully managed service for preparing data and building, training, and deploying machine learning models. Another tool for orchestrating NIMs is AWS HealthOmics, a service designed specifically for biological data processing.

The hundreds of healthcare and life sciences businesses that currently use AWS will be able to implement generative AI more quickly thanks to easy access to NIM, eliminating the hassles associated with model building and production packaging. Additionally, it will assist developers in creating workflows that integrate AI models with data from many modalities, including MRI scans, amino acid sequences, and plain-text patient health records.

This initiative, which was presented today at the AWS Life Sciences Leader Symposium in Boston, expands the range of NVIDIA Clara accelerated healthcare software and services that are available on AWS. These services include NVIDIA BioNeMo‘s quick and simple-to-deploy NIMs for drug discovery, NVIDIA MONAI for medical imaging workflows, and NVIDIA Parabricks for accelerated genomics.

Pharmaceutical and Biotech Businesses Use NVIDIA AI on Amazon

Nvidia BioNeMo is a generative AI platform that supports the training and optimisation of biology and chemistry models on private data. It consists of foundation models, training frameworks, domain-specific data loaders, and optimised training recipes. Over a hundred organisations utilise it worldwide.

One of the top biotechnology firms in the world, Amgen, has trained generative models for protein design using the Nvidia BioNeMo framework and is investigating the possibility of integrating Nvidia BioNeMo with AWS.

The Nvidia BioNeMo models for molecular docking, generative chemistry, and protein structure prediction are pretrained and optimised to run on any NVIDIA GPU or cluster of GPUs. They are available as NIM microservices. Combining these models can enable a comprehensive approach for AI-accelerated drug discovery.

A-Alpha Bio is a biotechnology business that uses artificial intelligence (AI) and synthetic biology to quantify, forecast, and design protein-to-protein interactions. Researchers witnessed a speedup of more than 10x as soon as they switched from a generic version of the ESM-2 protein language model to one that was optimised by NVIDIA and ran on NVIDIA H100 Tensor Core GPUs on AWS. As a result, the team is able to sample a far wider range of protein possibilities than they otherwise could have.

Using retrieval-augmented generation, or RAG, also referred to as a lab-in-the-loop architecture, NIM enables developers to improve a model for organisations who wish to supplement these models with their own experimental data.

Accelerated Genomics Pipelines Made Possible by Parabricks

NVIDIA Parabricks genomics models are included in NVIDIA NIM and can be accessed on AWS HealthOmics as Ready2Run workflows, which let users set up pre-made pipelines.

The life sciences company Agilent greatly increased the processing rates for variant calling workflows on its cloud-native Alissa Reporter software by utilising Parabricks genomics analysis tools running on NVIDIA GPU-powered Amazon Elastic Compute Cloud (EC2) instances. Researchers can get quick data analysis in a secure cloud environment by integrating Parabricks with Alissa secondary analysis workflows.

Artificial Conversational Intelligence Promotes Digital Health

NIM microservices provide optimised big language models for conversational AI and visual generative AI models for avatars and digital humans, in addition to models that can read proteins and genetic sequences.

By providing logistical support to clinicians and responding to patient inquiries, AI-powered digital assistants can improve healthcare. After receiving training on RAG-specific data from healthcare organisations, they were able to link to pertinent internal data sources to aggregate research, reveal patterns, and boost efficiency.

startup using generative AI AI-powered healthcare agents that concentrate on a variety of tasks like wellness coaching, preoperative outreach, and post-discharge follow-up are now being tested by Hippocratic AI.

The company is implementing Nvidia BioNeMo Models and NVIDIA ACE microservices to power a generative AI agent for digital health. The company employs NVIDIA GPUs through AWS. The team powered the discussion of an avatar healthcare assistant with NVIDIA Audio2Face facial animation technology, NVIDIA Riva automated voice recognition, text-to-speech capabilities, and more.

NVIDIA created a collection of tools called Nvidia BioNeMo models especially for use in life sciences research, including drug development. They are constructed around the Nemo Megatron framework from NVIDIA, which is a toolkit for creating and honing massive language models.

Features of Nvidia BioNeMo Models

Pre-conditioned AI models

Large volumes of biological data have already been used to train these models. Then, these models can be applied to a range of activities, including determining possible drug targets, assessing the impact of mutations, and forecasting protein function. Pre-trained Nvidia BioNeMo models include, for instance-

DNABERT:

This model is useful for analysing and forecasting the function of DNA sequences.

ScBERT:

This model can be used to identify distinct cell types and forecast the consequences of gene knockouts because it was developed using single-cell RNA sequencing data.

EquiDock:

The 3D structure of protein interactions may be predicted using this approach, which is useful for finding possible therapeutic options.

BioNeMo Service

Researchers can simply access and utilise Nvidia BioNeMo‘s pre-trained models through a web interface by using the BioNeMo Service, a cloud-based solution . For researchers without access to the computational power needed to train their own models, this service can be especially helpful.

All things considered, Nvidia BioNeMo models are an effective instrument that can be utilised to quicken medication discovery research. These models help researchers find novel drug targets and create new treatments more swiftly and effectively.

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[govindhtech]

AI Opening: NVIDIA BioNeMo Improves AWS Drug Discovery

Using Amazon Web Services, researchers and engineers at top pharmaceutical and techbio firms may now quickly and simply use NVIDIA Clara software and services for faster healthcare.

The program, which was unveiled at AWS re:Invent today, allows developers working in the healthcare and life sciences industry to integrate NVIDIA-accelerated products like NVIDIA BioNeMo, a generative AI platform for drug discovery that will soon be available on NVIDIA DGX Cloud on AWS. It is also currently accessible through the AWS ParallelCluster cluster management tool for high performance computing and the Amazon SageMaker machine learning service.

AWS is used by thousands of healthcare and life sciences businesses worldwide. With proprietary data, they will now have access to BioNeMo, enabling them to construct or modify foundation models for digital biology. Model training and deployment will be sped by the use of NVIDIA GPU-accelerated cloud servers on AWS.

Techbio innovators employing BioNeMo for generative AI-accelerated drug discovery and development include LabGenius, Alchemab Therapeutics, Basecamp Research, Character Biosciences, Evozyne, Etcembly, and AWS customers. They now have additional options to quickly scale up cloud computing resources for creating generative AI models that have been trained on biomolecular data thanks to this collaboration.

With this release, NVIDIA expands its portfolio of healthcare-oriented products on AWS, which includes NVIDIA Parabricks for accelerated genomics and NVIDIA MONAI for medical imaging processes.

NVIDIA BioNeMo: Introducing AWS and Advancing Generative AI for Drug Discovery

BioNeMo is a domain-specific framework for generative AI in digital biology that includes data loaders, pretrained large language models (LLMs), and optimized training recipes that can accelerate target identification, protein structure prediction, and drug candidate screening in computer-aided drug discovery.

Teams working on drug development can utilize BioNeMo to build or optimize models using their proprietary data, which can then be performed on cloud-based high performance computing clusters.

Using 256 NVIDIA H100 Tensor Core GPUs, one of these models the potent LLM ESM-2 achieves nearly linear scalability for protein structure prediction. Instead of taking a month to complete training, as stated in the original report, researchers may scale to 512 H100 GPUs and finish in a few days.

ESM-2 may be trained at scale by developers with checkpoints of 3 billion or 650 million parameters. The BioNeMo training framework supports other AI models, such as the protein sequence generation model ProtT5 and the small-molecule generative model MegaMolBART.

Using self-managed services like AWS ParallelCluster and Amazon ECS as well as integrated, managed services like NVIDIA DGX Cloud and Amazon SageMaker, BioNeMo’s pretrained models and optimized training recipes can help R&D teams build foundation models that can explore more drug candidates, optimize wet lab experimentation, and find promising clinical candidates more quickly.

NVIDIA Clara for Medical Imaging and Genomics is also accessible on AWS

With over 1.8 million downloads, Project MONAI, which NVIDIA cofounded and is enterprise-supported to support medical imaging workflows, may be deployed on AWS. Using their own healthcare datasets that are currently saved on AWS cloud services, developers may quickly annotate and construct AI models for medical imaging.

These models can be used for interactive annotation and fine-tuning for medical imaging segmentation, classification, registration, and detection tasks. They were trained on NVIDIA GPU-powered Amazon EC2 instances. Additionally, MRI image synthesis models included in MONAI can be used by developers to enhance training datasets.

In order to speed up genomics workflows, variant calling on the entire human genome can be accomplished with Parabricks in about 15 minutes as opposed to a day on a CPU-only system. Developers can easily scale up to handle massive volumes of genomic data over numerous GPU nodes on AWS.

AWS HealthOmics offers over twelve Parabricks workflows as Ready2Run workflows, allowing users to quickly run pre-configured pipelines.

Start accelerating AI workflows for drug development, genomics, and medical imaging with NVIDIA Clara on AWS.

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[moremedtech]

NVIDIA BioNeMo Enables Generative AI for Drug Discovery on AWS

NVIDIA BioNeMo Enables Generative AI for Drug Discovery on AWS. Pharma and techbio companies can access the NVIDIA Clara healthcare suite, including BioNeMo, now via Amazon SageMaker and AWS ParallelCluster, and the NVIDIA DGX Cloud on AWS. New to AWS: NVIDIA BioNeMo Advances Generative AI for Drug Discovery Also Available on AWS: NVIDIA Clara for Medical Imaging and Genomics November 28, 2023 - Leading pharmaceutical and biotech companies' researchers and developers can now easily deploy NVIDIA Clara software and services for accelerated healthcare via Amazon Web Services. The initiative, announced today at AWS re:Invent, allows healthcare and life sciences developers who use AWS cloud resources to integrate NVIDIA-accelerated offerings such as NVIDIA BioNeMo—a generative AI platform for drug discovery—which is coming to NVIDIA DGX Cloud on AWS and is currently available via the AWS ParallelCluster cluster management tool for high-performance computing and the Amazon SageMaker machine learning service. AWS is used by thousands of healthcare and life sciences companies worldwide. They can now use BioNeMo to build or customize digital biology foundation models with proprietary data, scaling up model training and deployment on AWS using NVIDIA GPU-accelerated cloud servers. Alchemab Therapeutics, Basecamp Research, Character Biosciences, Evozyne, Etcembly, and LabGenius are among the AWS users who have already started using BioNeMo for generative AI-accelerated drug discovery and development. This collaboration provides them with additional options for rapidly scaling up cloud computing resources for developing generative AI models trained on biomolecular data. This announcement extends NVIDIA’s existing healthcare-focused offerings available on AWS — NVIDIA MONAI for medical imaging workflows and NVIDIA Parabricks for accelerated genomics.

New to AWS: NVIDIA BioNeMo Advances Generative AI for Drug Discovery

BioNeMo is a domain-specific framework for digital biology generative AI, including pretrained large language models (LLMs), data loaders, and optimized training recipes that can help advance computer-aided drug discovery by speeding target identification, protein structure prediction, and drug candidate screening. Drug discovery teams can use their proprietary data to build or optimize models with BioNeMo and run them on cloud-based high-performance computing clusters. One of these models, ESM-2, a powerful LLM that supports protein structure prediction, achieves almost linear scaling on 256 NVIDIA H100 Tensor Core GPUs. Researchers can scale to 512 H100 GPUs to complete training in a few days instead of a month, the training time published in the original paper. Developers can train ESM-2 at scale using checkpoints of 650 million or 3 billion parameters. Additional AI models supported in the BioNeMo training framework include small-molecule generative model MegaMolBART and protein sequence generation model ProtT5. BioNeMo’s pretrained models and optimized training recipes — which are available using self-managed services like AWS ParallelCluster and Amazon ECS as well as integrated, managed services through NVIDIA DGX Cloud and Amazon SageMaker — can help R&D teams build foundation models that can explore more drug candidates, optimize wet lab experimentation and find promising clinical candidates faster

Also Available on AWS: NVIDIA Clara for Medical Imaging and Genomics

Project MONAI, cofounded and enterprise-supported by NVIDIA to support medical imaging workflows, has been downloaded more than 1.8 million times and is available for deployment on AWS. Developers can harness their proprietary healthcare datasets already stored on AWS cloud resources to rapidly annotate and build AI models for medical imaging. These models, trained on NVIDIA GPU-powered Amazon EC2 instances, can be used for interactive annotation and fine-tuning for segmentation, classification, registration, and detection tasks in medical imaging. Developers can also harness the MRI image synthesis models available in MONAI to augment training datasets. To accelerate genomics pipelines, Parabricks enables variant calling on a whole human genome in around 15 minutes, compared to a day on a CPU-only system. On AWS, developers can quickly scale up to process large amounts of genomic data across multiple GPU nodes. More than a dozen Parabricks workflows are available on AWS HealthOmics as Ready2Run workflows, which enable customers to easily run pre-built pipelines. Read the full article

[pavel-nosok]

Nvidia boosts generative AI for biology with BioNeMo

Nvidia boosts generative AI for biology with BioNeMo

Nvidia and Evozyne announced BioNeMo helped build a new generative AI model that could help improve human health as well as climate change.Read More

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[moremedtech]

NVIDIA Expands Large Language Models to Biology with BioNeMo

NVIDIA Expands Large Language Models to Biology with BioNeMo.Leading pharma companies, biotech startups and pioneering biology researchers are developing AI applications with the NVIDIA BioNeMo LLM service and framework to generate, predict and understand biomolecular data.As scientists probe for new insights about DNA, proteins and other building blocks of life, the NVIDIA BioNeMo framework — announced today at NVIDIA GTC — will accelerate their research.NVIDIA BioNeMo is a framework for training and deploying large biomolecular language models at supercomputing scale — helping scientists better understand disease and find therapies for patients. The large language model (LLM) framework will support chemistry, protein, DNA and RNA data formats.It’s part of the NVIDIA Clara Discovery collection of frameworks, applications and AI models for drug discovery.Just as AI is learning to understand human languages with LLMs, it’s also learning the languages of biology and chemistry. By making it easier to train massive neural networks on biomolecular data, NVIDIA BioNeMo helps researchers discover new patterns and insights in biological sequences — insights that researchers can connect to biological properties or functions, and even human health conditions.NVIDIA BioNeMo provides a framework for scientists to train large-scale language models using bigger datasets, resulting in better-performing neural networks. The framework will be available in early access on NVIDIA NGC, a hub for GPU-optimized software.In addition to the language model framework, NVIDIA BioNeMo has a cloud API service that will support a growing list of pretrained AI models.BioNeMo Framework Supports Bigger Models, Better PredictionsScientists using natural language processing models for biological data today often train relatively small neural networks that require custom preprocessing. By adopting BioNeMo, they can scale up to LLMs with billions of parameters that capture information about molecular structure, protein solubility and more.BioNeMo is an extension of the NVIDIA NeMo Megatron framework for GPU-accelerated training of large-scale, self-supervised language models. It’s domain specific, designed to support molecular data represented in the SMILES notation for chemical structures, and in FASTA sequence strings for amino acids and nucleic acids.“The framework allows researchers across the healthcare and life sciences industry to take advantage of their rapidly growing biological and chemical datasets,” said Mohammed AlQuraishi, founding member of the OpenFold Consortium and assistant professor at Columbia University’s Department of Systems Biology. “This makes it easier to discover and design therapeutics that precisely target the molecular signature of a disease.”BioNeMo Service Features LLMs for Chemistry and BiologyFor developers looking to quickly get started with LLMs for digital biology and chemistry applications, the NVIDIA BioNeMo LLM service will include four pretrained language models. These are optimized for inference and will be available under early access through a cloud API running on NVIDIA DGX Foundry.- ESM-1: This protein LLM, based on the state-of-the-art ESM-1b model published by Meta AI, processes amino acid sequences to generate representations that can be used to predict a wide variety of protein properties and functions. It also improves scientists’ ability to understand protein structure.- OpenFold: The public-private consortium creating state-of-the-art protein modeling tools will make its open-source AI pipeline accessible through the BioNeMo service.- MegaMolBART: Trained on 1.4 billion molecules, this generative chemistry model can be used for reaction prediction, molecular optimization and de novo molecular generation.- ProtT5: The model, developed in a collaboration led by the Technical University of Munich’s RostLab and including NVIDIA, extends the capabilities of protein LLMs like Meta AI’s ESM-1b to sequence generation.In the future, researchers using the BioNeMo LLM service will be able to customize the LLM models for higher accuracy on their applications in a few hours — with fine-tuning and new techniques such as p-tuning, a training method that requires a dataset with just a few hundred examples instead of millions.Startups, Researchers and Pharma Adopting NVIDIA BioNeMoA wave of experts in biotech and pharma are adopting NVIDIA BioNeMo to support drug discovery research.- AstraZeneca and NVIDIA have used the Cambridge-1 supercomputer to develop the MegaMolBART model included in the BioNeMo LLM service. The global biopharmaceuticals company will use the BioNeMo framework to help train some of the world’s largest language models on datasets of small molecules, proteins and, soon, DNA.- Researchers at the Broad Institute of MIT and Harvard are working with NVIDIA to develop next-generation DNA language models using the BioNeMo framework. These models will be integrated into Terra, a cloud platform co-developed by the Broad Institute, Microsoft and Verily that enables biomedical researchers to share, access and analyze data securely and at scale. The AI models will also be added to the BioNeMo service’s collection.- The OpenFold consortium plans to use the BioNeMo framework to advance its work developing AI models that can predict molecular structures from amino acid sequences with near-experimental accuracy.- Peptone is focused on modeling intrinsically disordered proteins — proteins that lack a stable 3D structure. The company is working with NVIDIA to develop versions of the ESM model using the NeMo framework, which BioNeMo is also based on. The project, which is scheduled to run on NVIDIA’s Cambridge-1 supercomputer, will advance Peptone’s drug discovery work.- Evozyne, a Chicago-based biotechnology company, combines engineering and deep learning technology to design novel proteins to solve long-standing challenges in therapeutics and sustainability.“The BioNeMo framework is an enabling technology to efficiently leverage the power of LLMs for data-driven protein design within our design-build-test cycle,” said Andrew Ferguson, co-founder and head of computation at Evozyne. “This will have an immediate impact on our design of novel functional proteins, with applications in human health and sustainability.”“As we see the ever-widening adoption of large language models in the protein space, being able to efficiently train LLMs and quickly modulate model architectures is becoming hugely important,” said Istvan Redl, machine learning lead at Peptone, a biotech startup in the NVIDIA Inception program. “We believe that these two engineering aspects — scalability and rapid experimentation — are exactly what the BioNeMo framework could provide.”