Aiming at making the process faster and smarter, NVIDIA on Wednesday released the NIM Agent Blueprint for generative AI-based virtual screening.
This innovative approach will reduce the time and cost of developing life-saving drugs, enabling quicker access to critical treatments for patients.
This NIM Agent Blueprint introduces a paradigm shift in the drug discovery process, particularly in the crucial hit-to-lead transition, by moving from traditional fixed database screening to generative AI-driven molecule design and pre-optimization, enabling researchers to design better molecules faster.
What's a NIM? What's a NIM Agent Blueprint? NVIDIA NIM microservices are modular, cloud-native components that accelerate AI model deployment and execution. These microservices allow researchers to integrate and scale advanced AI models within their workflows, enabling faster and more efficient processing of complex data.
The NIM Agent Blueprint, a comprehensive guide, shows how these microservices can optimize key stages of drug discovery, such as hit identification and lead optimization.
How Are They Used? Drug discovery is a complex process with three critical stages: target identification, hit identification and lead optimization. Target identification involves choosing the right biology to modify to treat the disease; hit identification is identifying potential molecules that will bind to that target; and lead optimization is improving the design of those molecules to be safer and more effective.
This NVIDIA NIM Agent Blueprint, called generative virtual screening for accelerated drug discovery, identifies and improves virtual hits in a smarter and more efficient way.
At its core are three essential AI models, now including the recently integrated AlphaFold2 as part of NVIDIA's NIM microservices.
AlphaFold2, renowned for its groundbreaking impact on protein structure prediction, is now available as an NVIDIA NIM.
MolMIM is a novel model developed by NVIDIA that generates molecules while simultaneously optimizing for multiple properties, such as high solubility and low toxicity.
DiffDock is an advanced tool for quickly modeling the binding of small molecules to their protein targets.
These models work in concert to improve the hit-to-lead process, making it more efficient and faster.
Each of these AI models is packaged within NVIDIA NIM microservices - portable containers designed to accelerate the performance, shorten time-to-market and simplify the deployment of generative AI models anywhere.
The NIM Agent Blueprint integrates these microservices into a flexible, scalable, generative AI workflow that can help transform drug discovery.
Leading computational drug discovery and biotechnology software providers that are using NIM microservices now, such as Benchling, Dotmatics, Terray, TetraScience and Cadence Molecular Sciences (OpenEye), are using NIM Agent Blueprints in their computer-aided drug discovery platforms.
These integrations aim to make the hit-to-lead process faster and more intelligent, leading to the identification of more viable drug candidates in less time and at lower cost.
Global professional services company Accenture is poised to tailor the NIM Agent Blueprint to the specific needs of drug development programs by optimizing the molecule generation step with input from pharmaceutical partners to inform the MolMIM NIM.
In addition, the NIM microservices that comprise the NIM Agent Blueprint will soon be available on AWS HealthOmics, a purpose-built service that helps customers orchestrate biological analyses. This includes streamlining the integration of AI into existing drug discovery workflows.
Revolutionizing Drug Development With AI The stakes in drug discovery are high.
Developing a new drug typically costs around $2.6 billion and can take 10-15 years, with a success rate of less than 10%.
By making molecular design smarter with NVIDIA's AI-powered NIM Agent Blueprint, pharmaceutical companies can reduce these costs and shorten development timelines in the $1.5 trillion global pharmaceutical market.
This NIM Agent Blueprint represents a significant shift from traditional drug discovery methods, offering a generative AI approach that pre-optimizes molecules for desired therapeutic properties.
For example, MolMIM, the generative model for molecules within this NIM Agent Blueprint, uses advanced functions to steer the generation of molecules with optimized pharmacokinetic properties - such as absorption rate, protein binding, half-life and other properties - a marked advancement over previous methods.
This smarter approach to small molecule design enhances the potential for successful lead optimization, accelerating the overall drug discovery process.
This leap in technology could lead to faster, more targeted treatments, addressing growing challenges in healthcare, from rising costs to an aging population.
NVIDIA's commitment to supporting researchers with the latest advancements in accelerated computing underscores its role in solving the most complex problems in drug discovery.
Visit build.nvidia.com to download the NIM Agent Blueprint for generative AI-based virtual screening and take the first step toward faster, more efficient drug development.
See notice regarding software product information.
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Aiming at making the process faster and smarter, NVIDIA on Wednesday released the NIM Agent Blueprint for generative AI-based virtual screening.
