Shift Bioscience publication increases confidence in AI virtual cells for novel target discovery

Genetic perturbation models, a type of AI virtual cell, are designed to predict how cells respond at a transcriptomic level to genetic interventions, including activation and inhibition of genes. These models can support scalable in silico target screening, but previous studies have questioned their reliability, with some models failing to outperform simple baseline approaches.

The study from Shift Bioscience, published in Nature Biotechnology, builds on foundational research reported by the Company 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 demonstrated that model underperformance in some past benchmarks could stem from miscalibration of the metrics used to compare them, causing reduced sensitivity to genuine model performance.

Shift will now use the findings of the study 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 focus initially on uncovering targets for both rejuvenation and the treatment of fibrosis, a key driver of ageing and age-related disease.

Source:

Shift Bioscience