Pharmaceutical R&D – AI-Driven Drug Discovery

Impact:

Achieved accuracy comparable to lab experiments in minutes.
Solved 98.5% of the human proteome structure problem.
Helped speed up research in drug discovery, genetic diseases, and vaccine development.

Use Case: Solving the 50-year-old problem of predicting 3D protein structure from amino acid sequences

Problem:
Traditional experimental protein structure discovery (e.g., X-ray crystallography) is slow and expensive. The protein folding problem was unsolved for decades.

Math-Based Solution (by DeepMind):
AlphaFold used a combination of:

Graph-based spatial modeling of molecular geometry.
Neural networks trained on existing protein data with advanced loss functions to calculate spatial proximity between amino acids.
Statistical mechanics and energy minimization equations to predict stable 3D shapes.
Probabilistic graphical models to infer plausible folding outcomes based on known protein families.