Artificial intelligence (AI) has revolutionized how scientists study the three-dimensional (3D) structures of proteins, making predicting, visualizing and understanding these complex molecules more efficient. It enables rapid prediction of protein structures and detailed analysis of their folding mechanisms. AI also supports discovery of new information with high accuracy and at a significantly larger scale than before.
Table of Contents
Chapter 1 Key Use Cases for AI Adoption in the 3D Protein Structure Analysis- Overview
- Key Use Cases or Case Studies
- Case 1: Accelerating Drug Discovery Through AI-Powered Structure Prediction - DeepMind's AlphaFold
- Case 2: Cryo-Electron Microscopy in Structural Virology - SARS-CoV-2 Spike Protein Analysis
- Case 3: Integrative Structural Biology in Drug Optimization - Schrodinger's Hybrid Modeling Approach
- Adoption Trends by Region
- Adoption Trends in North America
- Adoption Trends in Europe
- Adoption Trends in Asia-Pacific
- Adoption Trends in the Rest of the World
- Overview
- Impact of AI on Key Industry Verticals
- AI in the Biopharmaceutical Industry
- Impact of AI on Various Stages of the Supply Chain
- R&D and Product Design
- Manufacturing and Quality Control
- Distribution and Logistics
- Hospital Procurement
- Post-Market Surveillance
- Overview
- Abbreviations Used in the Report
Table 1: Major Companies, Market Focus, and AI Developments in the 3D Protein Structure Analysis
Table 2: Abbreviations Used in This Report
List of Figures
Figure 1: Impact of AI on the Supply Chain of the 3D Protein Structure Analysis
