Drug Target Review has published the second part of Dr Raminderpal Singh's conversation with Dr Layla Hosseini-Gerami. They explore the many forms of toxicity and how AI-powered tools like omics and cell painting are transforming early prediction in pre-clinical research.
"When asked what has enabled AI to tackle this problem now when it couldn’t 10-20 years ago, Hosseini-Gerami highlighted several key developments:
'What has really unlocked this ability to use AI and data analysis, especially when trying to understand things on a deeper level, is the advent of data, especially omics data. That explosion over the last decade or so has been amazing to see.'
She explained that earlier approaches were limited: 'Drug discovery and the machine learning side of it was initially very target focused… understanding of the molecular target or the molecular off target, often based on chemical structure data.'
The critical missing element was understanding downstream effects: 'The piece that was missing [was]: OK, we can predict that a drug is hitting a particular target or a particular off target, but what happens next? How can we understand what the consequences of that are?'"
Read more:
Part 2 of 'Fixing failed drugs - AI solutions for toxicity in drug discovery,' can be read in full at Drug Target Review.
Published: 21 July 2025 by Drug Target Review.
