Glossary

An AI system developed by DeepMind that predicts protein 3D structures from amino acid sequences with unprecedented accuracy, solving a 50-year-old grand challenge in biology.

The application of computational methods and algorithms to analyze and model biological systems, including genomics, proteomics, and molecular dynamics.

A class of machine learning algorithms that use multi-layered neural networks to progressively extract higher-level features from raw input.

The process of identifying and developing new therapeutic compounds, traditionally taking 10-15 years but increasingly accelerated by AI methods.

The study of the complete set of DNA (including all genes) in an organism, increasingly analyzed using AI to identify disease markers and therapeutic targets.

An AI-first drug discovery company founded by Demis Hassabis, applying DeepMind's AI capabilities to revolutionize pharmaceutical development.

A subset of artificial intelligence focused on algorithms that improve through experience and data, enabling computers to learn patterns without explicit programming.

Computer simulations that model the physical movements of atoms and molecules over time, used to study protein behavior and drug interactions.

A computing system inspired by biological neural networks, consisting of interconnected nodes (neurons) that can learn to perform tasks by considering examples.

The process by which a protein chain assumes its functional three-dimensional structure from its linear amino acid sequence, critical for understanding biological function.

A branch of molecular biology studying the molecular structure and dynamics of biological macromolecules, particularly how structures relate to function.

The process of identifying biological molecules (usually proteins) that are involved in a disease and could be modulated by a drug.