AI is at the heart of everything we do. We’re re-imagining the entire drug discovery process and re-engineering through the thoughtful application AI and machine learning techniques.
The origin of the term Centaur lies in the first man vs. machine events in chess invented by grandmaster Garry Kasparov after proposing the cooperation of human chess player and machine. Combining human and machine delivered superior results and the innovation gave rise to a new style of chess.
The Exscientia CentaurAI concept represents the interplay between strategic human knowledge and tactical computation to accelerate Drug Discovery
Drug discovery is precision engineering at the molecular scale. Exscientia’s team is delivering key advances across data acquisition, Machine Learning and Generative systems to fulfil sophisticated design objectives.
Human with Machine
Talented teams define projects and strategic goals, while allowing AI to do the heavy lifting, digesting complex biology data and designing new novel molecules aligned to a defined therapeutic profile.
Centaur Biologist® and Centaur Chemist® systems are the result of engineering that specifically empowers researchers to interact strategically with AI design systems.
Therapeutic target selection is one of the biggest challenges of drug discovery.
We have invested in AI approaches that identify emerging hotspots of opportunity from the wealth of genetic data and global biological literature.
Through focused machine learning models we identify non-obvious associations that anticipate future relationships, from which researchers can develop confirmatory experiments.
This strategy allows us to work on both first in class and best in class targets approaches within a unified framework.
Disease & Target
Our systems transform drug discovery into effective formalised design moves.
We were the first to demonstrate that AI algorithms can outperform human experts when given the same drug discovery optimisation challenge.
We formalise each drug discovery project as a learning challenge, where each project has a distinct set of issues to be solved. The faster the systems learn, the more efficiently compounds with a profile suitable for clinical assessment are identified.
This benefits the human researchers freeing their time to work on other strategic aspects of each project