The prediction of protein structures is an important frontier in computational biology because it enables a deeper understanding of biological functions and mechanisms. It’s crucial for innovations in drug design, disease comprehension, and the development of novel biotechnological products with various industrial applications. Traditionally, the immense diversity of possible protein structures and the high costs and lengthy timelines associated with experimental methods for protein structure prediction posed significant barriers. Today, these challenges are being met head-on by integrating accelerated computing resources and advanced predictive AI models, such as AlphaFold, ESMFold, and Rosetta.
These sophisticated tools have revolutionized our approach to protein structure prediction by enabling the rapid generation of protein models that often have angstrom-scale accuracy. This technological leap saves valuable time and resources while enhancing our understanding of protein functions and interactions.
NVIDIA’s accelerated computing and AI platform for drug discovery, BioNeMo™, enables researchers and application developers to:
- Accurately predict the 3D structure of proteins, whether it is a known protein sequence or a virtually designed protein.
- Access AlphaFold2 through an NVIDIA NIM™ API for highly accelerated interfaces.
- Experiment and refine AI-driven protein design workflows tailored for enterprise-level applications using NIM’s flexible deployment options across any cloud or on-premise infrastructure.