Target Discovery Through Mechanism-Enabled Simulations in the Virtual Lab

Minimizing the risk of late-stage failure in drug discovery for novel targets remains a central challenge in translational research. Conventional preclinical perturbation datasets lack mechanistic insight and context specificity. Turbine’s Virtual Biology capabilities which include the Simulated Cell™, Virtual Lab platform and Lab-in-the-Loop capabilities (Fig. 1.) offer rapid scalable access to simulations with mechanistic insights. This is driven by our AI-guided modeling to predict phenotypic and transcriptomic outcomes of genetic and pharmacological perturbations in disease models representative of patient heterogeneity. This enables systematic evaluation of target–disease linkage mechanisms, target liabilities, and combinatorial strategies to enrich preclinical decision-making data packages.