NVIDIA NIM Agent Blueprint redefines hit identification in drug discovery

With the goal of making the process faster and smarter, NVIDIA on Wednesday released the NIM Agent Blueprint for generative AI-based virtual screening.

This innovative approach reduces the time and cost of developing life-saving medicines and gives patients faster access to vital treatments.

This NIM Agent Blueprint introduces a paradigm shift in the drug discovery process, particularly in the critical transition from hit to lead, by moving from traditional fixed-database screening to generative, AI-driven molecule design and pre-optimization, enabling researchers to develop better molecules faster.

What is a NIM? What is a NIM agent blueprint?

NVIDIA NIM microservices are modular, cloud-native components that accelerate the deployment and execution of AI models. These microservices enable researchers to integrate and scale advanced AI models into 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 phases of drug discovery, such as hit identification and lead optimization.

How are they used?

Drug discovery is a complex process with three critical phases: target identification, hit identification, and lead optimization. Target identification is about selecting the right biology that needs to be modified to treat the disease; hit identification is about identifying potential molecules that will bind to that target; and lead optimization is about improving the design of those molecules to make them 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 the core are three key AI models, including the recently integrated AlphaFold2 as part of NVIDIA's NIM microservices.

• AlphaFold2, known for its groundbreaking impact on protein structure prediction, is now available as NVIDIA NIM.
• MolMIM is a novel model developed by NVIDIA that generates molecules while optimizing multiple properties, such as high solubility and low toxicity.
• DiffDock is an advanced tool for rapidly modeling the binding of small molecules to their protein targets.

These models work together to improve the hit-to-lead process, making it more efficient and faster.

Each of these AI models is packaged in NVIDIA NIM microservices – portable containers designed to accelerate performance, reduce 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 vendors now using NIM microservices, such as Benchling, Dotmatics, Terray, TetraScience, and Cadence Molecular Sciences (OpenEye), are leveraging NIM Agent Blueprints in their computational drug discovery platforms.

These integrations aim to make the hit-to-lead process faster and smarter, enabling more promising drug candidates to be identified in less time and at a lower cost.

Leading computational drug discovery and biotechnology software vendors now using NIM microservices, such as Schrödinger, Benchling, Dotmatics, Terray, TetraScience, and Cadence Molecular Sciences (OpenEye), are leveraging NIM Agent Blueprints in their computational drug discovery platforms.

These integrations aim to make the hit-to-lead process faster and smarter, enabling more promising drug candidates to be identified in less time and at a lower cost.

Global consulting firm Accenture is ready to adapt the NIM Agent Blueprint to the specific needs of drug development programs by optimizing the molecule generation step using input from pharma partners to inform the MolMIM NIM.

In addition, the NIM microservices that make up the NIM Agent Blueprint will soon be available on AWS HealthOmics, a purpose-built service that helps customers orchestrate biological analyses, including streamlining the integration of AI into existing drug discovery workflows.

Revolutionizing drug development with AI

There is a lot at stake in drug discovery.

Developing a new drug typically costs around $2.6 billion and can take 10 to 15 years, with a success rate of less than 10 percent.

By making molecule design smarter with NVIDIA's AI-powered NIM Agent Blueprint, pharmaceutical companies can reduce these costs and shorten development times in the $1.5 trillion global pharmaceutical market.

This NIM agent blueprint represents a significant departure from traditional drug discovery methods and provides a generative AI approach that pre-optimizes molecules for desired therapeutic properties.

For example, MolMIM, the generative model for molecules in this NIM Agent Blueprint, uses advanced features to guide the generation of molecules with optimized pharmacokinetic properties – such as absorption rate, protein binding, half-life, and other properties – a significant advancement over previous methods.

This smarter approach to small molecule design increases the potential for successful lead optimization and accelerates the entire drug discovery process.

This technological leap could lead to faster, more targeted treatments, addressing growing healthcare challenges ranging from rising costs to an aging population.

NVIDIA's commitment to empowering researchers with the latest advances 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 observe regarding software product information.