Target Discovery & De-risking

Target Discovery & De-risking by Turbine is an AI-powered virtual biology platform designed for biopharmaceutical organisations seeking to expand and de-risk their drug development pipelines. By simulating biological experiments computationally, Turbine enables research teams to identify novel and hidden targets, understand mechanisms of response and resistance, and define predictive biomarker hypotheses — all without the constraints of conventional experimental methods.

The platform is purpose-built for drug discovery teams that need to move beyond the limitations of CRISPR or knockdown screens, accelerate preclinical decision-making, and increase the probability of clinical success by selecting the right targets and the right patients from the outset.

Core Capabilities

• Simulated target modulation: Turbine enables the modulation of undrugged targets within models that are inaccessible to conventional CRISPR or knockdown screens, opening up a broader target landscape regardless of druggability.
• Mechanistic pathway analysis: The platform delivers mechanistic explanations of the effects of target perturbation at both the protein and pathway activity level, providing causal hypotheses that enhance the translatability of preclinical findings to clinical efficacy.
• Synthetic lethality screening: For every target candidate identified, Turbine runs drug modifier-like synthetic lethality screens to complement target identification with predictive biomarker hypotheses, supporting more informed target prioritisation.

Unique Benefits

• Faster identification of novel targets, independent of druggability or CRISPR-dependence
• Causal, biology-driven hypotheses that improve the translatability of preclinical correlations to clinical outcomes
• Capacity to run drug modifier-like screens for every novel target candidate at scale
• Biomarker-driven target identification that enhances development potential and enables rigorous target prioritisation
• Outputs include ranked targets alongside validation workplans, supporting rapid downstream experimental follow-up

Workflow and Timelines

1. A patient avatar model is trained within approximately 1 month, establishing the computational foundation for downstream simulations.
2. Over 360 million or more ideas are explored through in silico experimentation across the target landscape.
3. Within 5 months, Turbine delivers a comprehensive understanding of complex biological mechanisms, along with ranked target candidates and associated validation workplans.

Turbine has demonstrated the ability to provide multiple targets with first-in-class potential, together with the biology-driven insights required to rapidly validate that potential — as evidenced by its collaboration with Ono Pharmaceutical, in which Turbine achieved a key programme milestone. The platform is positioned as a strategic partner for organisations looking to diversify their pipelines and reduce clinical attrition through smarter, simulation-led target discovery.