Integrated Target Identification & Validation

Evotec integrates functional genomics, genetic perturbation, scalable target validation, disease-relevant models, and advanced data analysis to help identify, validate, and prioritize promising drug targets. Our complementary approaches generate causal, decision-ready evidence across the target journey, from discovery screening to high-throughput validation and deeper mechanistic studies in complex disease models.

Explore our Target Identification & Validation capabilities below, or contact our experts to discuss the right approach for your discovery program.

Advances in large-scale biological data, disease-relevant models, and modern perturbation technologies have expanded target discovery opportunities, but they have also raised the standard for target evidence. Strong decisions require more than biological association alone; target hypotheses must be tested for causality in relevant systems and supported by sufficient analytical depth.

Evotec combines the right disease model, perturbation strategy, and readout to generate interpretable, decision-ready data across the target journey, from novel target discovery to focused validation of high-priority candidates in complex in vitro models.

Evotec combines functional genomics, disease-relevant biology, scalable perturbation, and advanced data analysis within one integrated discovery ecosystem. This enables target hypotheses to be explored at the required scale and depth, from discovery through prioritization and focused validation.

• Functional genomics and scalable perturbation: CRISPR screening and complementary perturbation approaches support novel target discovery and the parallel evaluation of larger target sets.
• Disease-relevant cellular models: Primary cells, iPSC-derived systems, and complex 2D and 3D models enable target validation in biological contexts designed to improve disease relevance and translational confidence.
• Information-rich readouts: Phenotypic, imaging-based, transcriptomic, proteomic, and other high-dimensional readouts generate deeper insight into target biology and mechanism of action.
• AI-enabled data analysis and target prioritization: Bioinformatics, AI/ML, and knowledge graph approaches integrate complex datasets to support target prioritization, identify disease-relevant networks, and strengthen decision-making.
• Flexible workflows across the target journey: Integrated capabilities support discovery-scale screening, high-throughput target validation, and deeper mechanistic follow-up in complex disease models.