Accelerate drug discovery with NVIDIA Clara™ for Biopharma, a collection of frameworks, applications, generative AI solutions, and pretrained models.
Accelerate breakthrough drug identification and improve the accuracy of target and compound selection.
Keep pace with AI innovation and drive outcomes within your organization.
Improve developer productivity and accelerate time to outcome.
Drug discovery spans many workflows, from exploring the chemical universe and predicting protein structures to scanning drug candidates and simulating molecules. Drive breakthroughs in these critical research areas with the powerful cloud APIs and tools available in the NVIDIA NGC™ catalog.
The Virtual Screening workflow within NVIDIA® BioNeMo™ leverages state-of-the-art AI models packaged within NVIDIA NIM™ microservices to screen and optimize small molecules against a protein target, accelerating drug discovery. The workflow starts with AlphaFold2, which predicts the 3D structure of the target protein with high accuracy. The initial small molecules are then passed to MolMIM, which is then used to generate diverse small molecules for exploring chemical space to identify potential binders. These small molecules are evaluated by an Oracle model, which scores them based on predicted binding affinity and other crucial properties. Finally, DiffDock is employed to refine the interactions, predicting the optimal binding poses and enhancing the binding configurations. This integrated workflow streamlines the identification and optimization of promising drug-like molecules, significantly reducing the time and cost associated with traditional drug discovery methods.
The Protein Binder Design workflow within NVIDIA BioNeMo leverages AI models packaged within NIMs to design optimized protein sequences and structures. The workflow begins with the user passing an amino acid sequence to AlphaFold2, which predicts the initial 3D structure of the target protein. This structural information is then refined and optimized using RfDiffusion, which explores various conformations to identify the most favorable binding configurations. Next, ProteinMPNN generates and optimizes the amino acid sequences according to the RfDiffusion-generated conformational information, ensuring they exhibit the necessary biochemical properties for effective binding. Finally, AlphaFold-Multimer is used to validate the interactions and stability of the resulting protein complexes. This integrated approach enables the precise and efficient design of protein binders, facilitating advancements in therapeutic protein development and other biomedical applications.
GROMACS is an open-source software package designed for molecular dynamics simulations of biomolecules, such as proteins, nucleic acids, and lipids. It plays a critical role in advancing our understanding of biological systems at the molecular level.
Image by Veronica Falconieri and Sriram Subramaniam, licensed from the National Cancer Institute under public domain
Deep learning-based approaches like RELION are powering high-throughput automation of cryo-EM for protein structure determination. RELION implements an empirical Bayesian approach for analysis of cryo-EM to refine singular or multiple 3D reconstructions as well as 2D class averages.
Learn more about NVIDIA BioNeMo, a platform composed of managed services, software application frameworks, and reference AI workflows that simplify, accelerate, and scale generative AI for drug discovery.
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