Shift Bioscience has published new research in Nature Biotechnology describing an improved calibration framework for deep learning-based genetic perturbation models.
Shift will utilise the framework to support large-scale in vitro and in silico screens to identify novel inhibition targets with potential application in rejuvenation and age-related disease treatment, initially focusing on fibrosis.
Genetic perturbation models, a type of AI virtual cell, can predict how cells respond at the transcriptomic level to genetic interventions, including gene activation and inhibition. These models can support scalable in silico target screening, but previous studies have questioned their reliability, with some models being unable to outperform simple baseline approaches.
The study from Shift builds on foundational research the Company reported in November 2025. It defines a reliable benchmarking system for models that considers the biological and technical signals in a dataset, providing more meaningful insight into model performance. The framework revealed that model underperformance in some past benchmarks could stem from miscalibrated comparison metrics reducing sensitivity to genuine model performance.
Shift is set to use the study’s findings to launch large-scale in vitro and in silico screens for novel, dual-purpose inhibition targets. Following the discovery of SB-101, Shift’s first dual-purpose target, the screens will initially focus on identifying targets for rejuvenation and fibrosis treatment, a key driver of ageing and age-related disease.
Dr Brendan Swain, CSO and founder of Shift Bioscience, said, “Our findings show that by using well-calibrated metrics and the right dataset, virtual cell models can generate biologically meaningful insights. As a result, we can use them with greater confidence to identify promising new targets that are relevant to ageing and disease. We are applying this framework directly in our target identification program, focusing on targets whose inhibition can support both rejuvenation and treatment of age-related disease, giving us a clearly defined route towards clinical development.”
