Utilizing NVIDIA NIM microservices and NIM Agent Blueprints, Benching, Dogmatic, Terrey, Tetra Science, and Cadence Molecular Sciences are pushing the frontiers of drug discovery with “Better Molecules, Faster” a novel approach to hit identification via generative AI-based virtual screening.
- NVIDIA unveiled the NIM Agent Blueprint on Wednesday for generative AI-based virtual screening with the goal of streamlining and improving the process.
- Patients will have speedier access to vital therapies thanks to this creative method, which will shorten the time and expense associated with creating life-saving medications.
By switching from traditional fixed database screening to generative AI-driven molecule design and pre-optimization, this NIM Agent Blueprint introduces a paradigm shift in the drug discovery process, especially in the critical “hit-to-lead” transition, allowing researchers to design better molecules more quickly.
What is A NIM? What Is A NIM Agent Blueprint?
The modular, cloud-native NVIDIA NIM microservices speed up the deployment and operation of AI models. Researchers may include and expand sophisticated AI models into their workflows with the help of these microservices, which makes it possible to analyze complicated data more quickly and effectively.
A thorough manual called the NIM Agent Blueprint demonstrates how these microservices might improve crucial phases of drug development including lead optimization and hit detection.
NVIDIA NIM Agent Blueprints: What Are They?
The standard processes for classic generative AI use cases are NVIDIA NIM Agent Blueprints. As part of the NVIDIA AI Enterprise Platform, enterprises may use NIM Agent Blueprints, NVIDIA NIM microservices, and NVIDIA NeMo framework to develop and implement unique AI applications that create data-driven AI flywheels. Partner microservices, one or more AI agents, reference code, customization documentation, and a Helm chart for deployment are all included in the NIM Agent Blueprints.
What Uses Do They Serve?
The three crucial steps in the complicated process of drug development are lead optimization, hit identification, and target identification. Selecting the appropriate biology to alter for the purpose of treating the illness is known as target identification. Finding possible compounds that attach to that target is known as hit identification. And enhancing the structure of those molecules to make them safer and more efficient is known as lead optimization.
Known as generative virtual screening for rapid drug discovery, this NVIDIA NIM Agent Blueprint finds and enhances virtual hits more intelligently and effectively. Three fundamental AI models including the newly included AlphaFold2 as a component of NVIDIA’s NIM microservices are at the center of it all.
- The NVIDIA NIM version of AlphaFold2, which is well-known for its revolutionary influence on protein structure prediction, is now accessible.
- NVIDIA created a revolutionary model called MolMIM that concurrently optimizes for many attributes, including low toxicity and good solubility, while generating molecules.
- A sophisticated technique for rapidly simulating the binding of tiny compounds to their protein targets is DiffDock.
Together, these models enhance the hit-to-lead process, increasing its speed and efficiency.
- All these AI models are contained in NVIDIA NIM microservices portable containers, which are intended to make generative AI model deployment easier, faster, and faster to market.
- These microservices are integrated by the NIM Agent Blueprint into a generative AI process that is scalable, adaptable, and has the potential to revolutionize drug development.
Cadence Molecular Sciences
NIM Agent Blueprints are being used by top biotechnology and computational drug discovery software providers, including Benchling, Dotmatics, Terray, TetraScience, and Cadence Molecular Sciences (OpenEye), in their computer-aided drug discovery platforms. These providers are now using NIM microservices.
By accelerating and improving the hit-to-lead process, these integrations hope to find more promising drug candidates more quickly and cheaply.
Accenture, a global provider of professional services, is well-positioned to customize the NIM Agent Blueprint to the unique requirements of drug development initiatives by refining the molecule generation stage and incorporating feedback from pharmaceutical partners to influence the MolMIM NIM.
Furthermore, the NIM microservices that make up the NIM Agent Blueprint will soon be accessible on AWS Health Omics, a specially designed platform that aids users in coordinating biological analysis. Streamlining the incorporation of AI into current drug research processes is part of this.
Using AI to Revolutionize Drug Development
Searching for novel medications is dangerous.
A new drug takes 10–15 years and costs $2.6 billion, with a success rate of less than 10%. Pharmaceutical businesses in the $1.5 trillion global pharmaceutical industry may save costs and accelerate development timelines by using NVIDIA’s AI-powered NIM Agent Blueprint to make molecular design smarter.
This NIM Agent Blueprint offers a generative AI technique that pre-optimizes compounds for desired therapeutic qualities, marking a substantial departure from conventional drug development methodologies.
One notable improvement over earlier techniques is the use of advanced functions by MolMIM, the generative model for Cadence Molecular Sciences in this NIM Agent Blueprint, to direct the generation of molecules with optimized pharmacokinetic properties, such as absorption rate, protein binding, half-life, and other attributes.
- This more intelligent approach to small molecules design speeds up the whole drug discovery process by increasing the likelihood of a successful lead optimization.
- This technological advance may result in quicker, more focused medical interventions, tackling mounting issues in healthcare such as escalating expenses and an aging populace.
- NVIDIA’s dedication to provide researchers with the newest developments in accelerated computing highlights its contribution to resolving the trickiest drug discovery issues.
