GoKawiilLayla Hosseini-Gerami draws on her background in chemistry and bioinformatics to help identify failed drugs and fix issues around their toxicity.Credit: Ignota Labs
Ignota Labs, a company based in Cambridge, UK, uses artificial-intelligence technology to determine why drugs have failed in clinical trials, before re-engineering the most promising therapies to give them another shot at making it to the patients who need them.
The company was set up in 2021 by Layla Hosseini-Gerami, Jordan Lane and Sam Windsor. In February 2025, they closed a US$6.9-million deal to fund the company, and they have since built a strong pipeline of promising drugs, including treatments for autoimmune diseases and blood cancers.
Hosseini-Gerami, the company’s chief data-science officer, has a background in chemistry and bioinformatics, which she combines with data science to understand how drugs affect the body. In April 2025, she was included in Forbes magazine’s ‘30 under 30’ list for European science and health care for her work using AI to accelerate the process of bringing safe drugs to market. She describes how the company, and her role in it, came about.
When did you become interested in AI?
There were no AI modules on offer when I studied for my undergraduate degree in chemistry at the University of Leeds, UK, between 2014 and 2018. At that time, AI was being used across many industries, but was still an emerging technology. My first exposure to AI came in 2016, during a one-year industrial placement as a machine-learning intern at Optibrium, a drug-discovery software company based in Cambridge, UK.
Nature Spotlight: Drug discovery
At Optibrium, I was building models to predict the molecular properties of different drugs. Specifically, I focused on pKa — a measure of the acidity of a molecule — which influences a compound’s solubility, permeability and ability to bind to its target. Building these models and then seeing them incorporated into software used by pharmaceutical-industry professionals motivated me to stay in the field and pursue a PhD.
Towards the end of my Optibrium internship, I reached out to Andreas Bender, a molecular informatician who has since moved to Khalifa University in Abu Dhabi, and he became my PhD supervisor at the University of Cambridge. I had been looking at different research groups, but he drew me in because he talked about rigour and developing AI that truly has an impact on drug discovery in the pharmaceutical industry. In drug discovery, many compounds are approved on the basis of clinical efficacy, without researchers having a clear understanding of their mechanism of action — for example, the proteins they target or the pathways they regulate. I developed computational algorithms that bridge this gap, identifying the biological targets and pathways that drive a drug’s therapeutic effect.
At that time — around ten years ago — it really felt as if we were one of the pioneering groups at the crossover of AI and science, because AI drug discovery hadn’t attained the level of interest it currently attracts. Since then, there has been rapid progress. The ongoing challenge is how to use AI to answer questions about biology and how drugs work in the body, which can be random and unpredictable.
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