Potential Sciences bets on AI and synthetic data to speed drug discovery

British startup Potential Sciences plans to simulate interactions among atoms, molecules, amino acids, and proteins using basic principles of physics. It aims to use this synthetic data to train AI models and prediction systems that can identify drugs targeting specific diseases, make candidates unlikely to work fail earlier, and reduce costly, time-consuming experiments. According to Wired, the startup launched on November 5 and says it has received tens of millions of dollars in seed funding from SovAI, the UK government’s AI venture capital fund, Twin Path Ventures, and other investors.

The approach depends on accurately predicting how drug candidates bind to disease-related proteins. Chemist Peter Wolynes told Wired that there will be improvement but assessed that protein engineering is still at a very early stage. Professor Alan Fersht said accurate simulations of molecular interactions could revolutionize drug development, although the article presents no results from the startup proving that capability.