Potential Sciences uses AI to speed up drug discovery
Wired AI: Finding New Drugs Is Laborious. This AI Startup Is Skipping The Lab Work
Potential Sciences, a startup backed by the UK government, is betting that training AI models on computer-generated biochemical data can speed up the plodding process of developing new drugs.
Get smart on it
A British startup is using AI trained on computer-generated biochemical data to speed up drug discovery by simulating molecular interactions instead of conducting lengthy lab experiments. The approach aims to help AI models predict which drug candidates will work effectively, reducing the need for costly experimentation and potentially making it economically viable to develop treatments for rare diseases. The startup faces a key challenge: current AI drug discovery systems rely on a single public database that is too small to train sufficiently advanced models, so the company is attempting to build much larger datasets using simulations of chemical interactions. While academics see promise in this physics-based simulation approach, drug discovery remains a crowded field, and even faster candidate identification does not eliminate the substantial time and cost required for safety and efficacy testing before treatments reach patients.
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