Shift Bioscience Publication Increases Confidence in AI Virtual Cells for Novel Target Discovery

Shift Bioscience Publication Increases Confidence in AI Virtual Cells for Novel Target Discovery

• Nature Biotechnology publication details improved calibration framework for deep learning-based genetic perturbation models
• Peer-reviewed study will inform large-scale screen to identify inhibition targets with applications in rejuvenation and age-related disease treatment

Shift Bioscience (Shift), a biotechnology company uncovering the biology of cell rejuvenation to develop new therapies for age driven diseases, today announced the publication of new research in Nature Biotechnology describing an improved calibration framework for deep learning-based genetic perturbation models1. Shift will use 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, 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 20252. 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.

Dr Brendan Swain, CSO and Founder, 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 aging 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.”

1. https://www.nature.com/articles/s41587-026-03307-w
2. Press Release (11th November 2025): Shift Bioscience publishes improved metric calibration framework for robust genetic perturbation modeling using AI Virtual Cells

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