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Target selection and validation in drug discovery: methods and best practices

Choosing the right drug target is one of the highest-stakes decisions in early drug discovery. A target that is poorly selected or insufficiently validated will eventually fail, and the later that failure occurs, the more expensive it becomes. Target selection and validation are the processes that determine whether a therapeutic hypothesis has enough biological evidence to justify moving forward. Drug targets are biological molecules within the body that interact with the pharmaceutical compound, resulting in the modulation of a biological process. Target identification and selection represent foundational steps in drug discovery, as they determine the molecular mechanisms underlying the mode of action of the pharmaceutical compound. Understanding which biological entity a drug modulates is essential for transforming empirical observations into rational, mechanism-driven therapeutic strategies. Many of the advances in drug discovery have been driven by the technological advances that allow the identification and characterization of molecular targets. This evolution, from the initial physiological observations to the modern genomics and systems biology approaches, enables precise molecular targeting. Accurate target validation allows the rational optimization of lead compounds, increases predictive power, and underpins informed decision-making at both preclinical and clinical levels of drug development. For these reasons, robust target selection and validation are core components of effective drug discovery, enhancing selectivity while minimizing the risk of toxic side effects. Conversely, drugs targeting either poorly characterized or insufficiently validated targets often fail due to a lack of efficacy or unacceptable toxicity. Contemporary drug discovery therefore places greater emphasis on evidence-based target selection, applying genetic, biochemical, and translational data to ensure that therapeutic targeting will result in clinically meaningful benefits.1,2

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What you will find in this article

•    What target selection and validation mean in drug discovery

•    Methods for drug target identification: omics, computational, and experimental approaches

•    How to validate a drug target: functional, pharmacological, and cross-species methods

•    What target engagement is and how it is measured

•    Criteria for a good and druggable drug target

•    Common pitfalls and how to avoid them

•    Emerging technologies reshaping target validation in 2025 and beyond

What is target selection and why it matters

Target identification is the first step in the drug discovery pipeline. It is essential to select the key target(s) that are biologically relevant to a disease and assess their potential to be modulated by a drug-like molecule in preclinical studies. Making the right choice at this stage increases the probability of success in clinical trials and reduces costly attrition later in development. Target identification and validation focus on establishing that modulation of a specific biological target can meaningfully influence disease biology and produce a therapeutic effect. Tool compounds may later be used to support target validation, but compound discovery itself follows target selection. Although definitions of a “target” may differ, in biological and medical contexts it is generally defined as a biomolecule or pathway involved in disease-associated processes. Owing to the high rates of attrition, careful selection of targets is emphasized in drug development. Most drug candidates fail due to inefficacy or safety concerns, and that is as a result of poor target validation. Target selection based on well-defined mechanistic associations to the disease, using either genetic or functional evidence, adds to the causal strengths and the chances of successful clinical response.

Besides being biologically relevant, targets should be druggable and must possess structural and functional features that can be conveniently modulated by therapeutic agents. The target is safely and effectively modifiable depending on features like accessibility of binding sites, potential ligand engagement, and appropriate expression profiles. Effective drug discovery requires careful target selection that prioritizes biological relevance and druggability, as overlooking these factors is a major cause of late-stage failure. Thus, a comprehensive approach, considering disease relevance, druggability, and safety, is essential for success.2-5

Methods for drug target identification

Omics approaches

Comprehensive transcriptomic profiling and high-throughput sequencing identify dysregulated genes and regulatory networks in the disease that can be targeted by potential intervention strategies. As integrated multi-omics analyses are introduced, the integration of genomics, transcriptomics, and proteomics improves the capacity to reveal functionally relevant targets by deconstructing disease processes across biological layers and eliminating false positives found during single-layer analyses. Multi-omics integration has thus emerged as a strategy of choice to enhance biological understanding and target identification in complex diseases. A 2026 review in Nature Reviews Drug Discovery highlights AI-augmented multi-omics as an increasingly central approach to target identification, enabling the analysis of datasets at a scale and speed that conventional methods cannot match. 6,7,19

Mapping the molecular changes associated with the disease in terms of gene expression, protein levels, and post-translational modification is enabled by omics approaches including transcriptomics, proteomics, and translatomics. This mapping, in turn, allows identification of candidate targets using either a difference in abundance or a difference in interaction pattern in disease versus control conditions. These kinds of analyses have played crucial roles in ranking drug targets in diverse settings, including cardiovascular diseases and cancer, by correlating protein fluctuations with pathophysiological significance.

Computational and in silico methods

Computational approaches leverage bioinformatics, network analysis, and machine learning algorithms to shortlist targets from a biological dataset. The bioinformatics approach combines information on genetic association, gene expression, and gene function to prioritize candidate genes and proteins with a strong likelihood of contributing to a particular biological process. The network pharmacology approach focuses on building a protein-protein interaction network to isolate key proteins contributing to a biological process and acting as critical intervention targets. In silico approaches not only expedite target selection but also increase their specificity by pre-filtering targets before undergoing laboratory verification8. Other predictive approaches, including molecular docking and virtual screening, can assess potential interactions between targets and drug candidates, providing early insights into druggability and guiding compound prioritization.

Experimental screening

Experimental functional screens offer empirical data for target involvement in biological mechanisms. Novel targets associated with therapeutic responses can be found by phenotypic screens, in which the effect of compounds on cellular phenotypes or organismal phenotypes is quantified without any prior assumption of target. Genetic perturbation screens using CRISPR library-based genetic perturbation screens enable systematic loss-of-function screens across the genome to identify genes required in specific phenotypes or disease pathways. CRISPR-based screening platforms have successfully been utilized to deconvolute small molecule mechanisms as well as direct targets and have become indispensable to contemporary target discovery. RNA interference (RNAi) screening is another complementary method, which allows reversible gene knockdown to assay functional contributions of genes, though with distinct limitations concerning off-target effects and partial knockdown.9,10

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Target validation methods: evidence-based approaches

Functional studies

The functional validation procedure further substantiates the hypothesis that modulation of the candidate target influences disease-related biology. Techniques such as siRNA‑ or CRISPR‑Cas9‑mediated knockout directly perturb target genes to assess resultant phenotypic changes. These approaches help establish the causality between target activity and disease mechanisms, a necessary piece of evidence before undertaking therapeutic development.

Pharmacological modulation

Pharmacologic validation entails the use of small molecules, biologics, or other compounds to modulate the target and observe for biological responses that are consistent with those thought to be produced via the therapeutic target. This process represents the link between genetic and biochemical validation and drug discovery. Measures such as chemical proteomics can be utilized to uncover binding partners of compounds within complex bio samples.

Cross species and disease model confirmation

To address model-specific or species-specific bias, functional perturbations in cell lines, organoids, or animal models are done to ensure that observed effects are conserved and reproducible. SiRNA methods offer fast gene knockdown, whereas CRISPR offers specific and long-lasting editing, both of which are advantageous depending on experimental needs. The selection and interpretation of models is thus a critical aspect towards target validation and accurate prediction of clinical outcomes.9,11

What is target engagement in drug discovery?

Target engagement is the extent to which a drug molecule actually binds to and occupies its intended molecular target within a biological system. While target identification and validation confirm that a target is relevant to disease biology, target engagement confirms that a compound is reaching and interacting with that target under physiologically relevant conditions. A drug can be highly potent in a biochemical assay and still fail to engage its target effectively in a cellular or in vivo setting.

Measuring target engagement is a critical step throughout the drug development process, from lead identification and preclinical proof of concept through to the early clinical phases, where target engagement data informs dose selection and supports the interpretation of pharmacodynamic responses in patients. Several methods are used across these stages:

  • Cellular Thermal Shift Assay (CETSA): measures the thermal stabilization of a target protein upon ligand binding. A positive CETSA result provides direct evidence of target engagement in intact cells.
  • Activity-Based Protein Profiling (ABPP): uses chemical probes to covalently label active sites of target proteins, enabling detection of compound binding across the proteome.
  • NanoBRET and proximity-based assays: measure protein-ligand proximity in living cells using bioluminescence resonance energy transfer, providing real-time engagement data in a cellular context.
  • Pharmacodynamic biomarker assays: measure downstream pathway changes that result from target modulation, providing indirect but biologically relevant evidence of engagement.

Integrating target engagement data with functional and cellular validation results strengthens the biological rationale for a programme and reduces the risk of advancing compounds that act through unintended mechanisms.

Criteria for a good drug target

An excellent target should be safe, effective, commercially and clinically viable, and druggable. The characteristics of an ideal drug target include the following: first, the target must be closely associated with the disease, and its regulatory functions must be an important part of disease pathology; second, the target should have one or more binding sites to which other molecules can interact; third, the target must be modifiable so that a therapeutic agent can produce a desired effect; fourth, the physiological effects arising from the target should play a significant role in complex regulatory processes; and lastly, the target may have endogenous small molecules or exogenous ligands that bind to it and have known pharmacological effects.

Besides these characteristics, an effective drug target should also satisfy practical drug discovery considerations. Disease relevance is critical: targets with genetic or functional evidence linking them to the disease are more likely to yield meaningful therapeutic outcomes. Druggability remains a central consideration, as structural and functional properties, such as ligand accessibility, binding pockets, and tissue-specific expression, determine whether a compound can effectively modulate the target. Safety considerations are equally important, and differential expression between diseased and normal tissues can minimize adverse effects. Lastly, targets that show possible biomarker applications are also beneficial because they enable tracking of target interaction and therapeutic response in clinical trials. Collectively, these criteria create a framework to select the targets that have the highest probability of successful translation into effective therapies.2,5,12

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Challenges and pitfalls in target validation

Target validation in drug discovery is plagued with challenges such as off-target effects, leading to incorrect biological inference regarding the biological process targeted. This has particularly been seen in the use of RNA interference or the use of small molecules, especially if they have the potential to target multiple molecules. While the CRISPR-Cas9 systems offer accuracy, there is still the risk of unwanted modifications requiring careful screening and mitigation strategies. Then there is the problem of translational relevance. It is believed that preclinical models such as cancerous cell lines and xenograft studies in animal models inadequately represent the disease in humans to offer predictive validation from target validation studies. The problem of reproducibility also affects target validation. In addition, inherent biological variability, including genetic differences, disease heterogeneity, and context-dependent target function, can significantly affect validation outcomes and complicate interpretation. Variability in experimental conditions, the lack of controls, and the publication bias towards studies with positive outcomes all lead to increased rates of false positives. More fundamentally, the issue of underlying biological signals is further compounded by issues such as variability in annotation and batch effects in data quality. For such problems, there is a need to ensure good experimental design, replication, and the employment of orthogonal models to ensure that the biological relevance for target effects applies to human diseases.13-15

Best practices and emerging technologies

Recent technological developments in experimental and analytical methods are reshaping target validation strategies, particularly with the application of CRISPR-based genetic screens in three-dimensional models such as human organoids. These models enhance the functional annotation of targets and disease pathways, since the models provide a biologically relevant framework in contrast to conventional two-dimensional cell cultures. Further, the organoid or microphysiological models are used to advance research strategies in diseases with the advantage that they model tissue architecture and cell heterogeneity, whereas genetic editing enables the evaluation of disease-related mechanisms and mutations to be accurately assessed. Combining multi-omics techniques, genomics, transcriptomics, proteomics, and metabolomics has the potential to offer data on downstream pathways, biomarkers, or compensation mechanisms. In addition, it is now feasible to optimize tasks and reproducibility in prioritization and validation through shared data resources. Artificial intelligence tools are increasingly being applied to multi-omics datasets to identify targets that would not emerge from single-modality analyses. A 2026 review in Nature Reviews Drug Discovery describes AI-driven target identification as a rapidly maturing field, with several AI-nominated targets now in clinical trials. These strategies, ranging from CRISPR screens and multi-omics analysis to shared data in science, allow for accurate and reproducible target validation that bridges discovery and therapy.16-19

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Conclusion: from target to therapy

The translation of target identification into effective therapies requires not only the discovery of biologically relevant and druggable targets but also their systematic validation in experimental models that accurately recapitulate human disease. The stringent use of validation criteria, which include safety, disease relevance, and functional significance, considerably reduces the possibility of costly late-stage clinical failure. Simultaneously, the development of experimental platforms and cooperative data-sharing systems strengthens the evidence base regarding target progress. Therefore, the evidence-based target discovery and validation strategy is indispensable to the successful development of drugs.

At Discovery Studio, we support biotech and pharma teams with evidence-based scientific due diligence, target biology assessment, and the assay development needed to move from a validated target into a robust preclinical programme.  

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Frequently asked questions

What is target selection in drug discovery?

Target selection is the process of identifying a biological molecule, such as a protein, gene, or pathway, that plays a causal role in a disease and can be modulated by a therapeutic intervention. It is the first major decision point in drug discovery and one of the strongest predictors of clinical success.

Target engagement is the confirmation that a drug molecule is physically binding to and interacting with its intended target in a biologically relevant system. It is distinct from target validation: a target can be well-validated but a compound may still fail to engage it effectively in cells or in vivo. Methods include CETSA, NanoBRET, ABPP, and pharmacodynamic biomarker assays.

Target identification is the process of finding potential biological targets associated with a disease. Target validation is the follow-on process of generating experimental evidence that modulating that target produces a measurable, disease-relevant biological response. Identification narrows the field; validation builds the case.

Strong target validation typically requires convergent evidence from at least two independent approaches: genetic evidence (such as CRISPR knockout or RNAi), biochemical evidence (such as binding and activity data), and functional or cellular evidence (phenotypic changes following target modulation). Translational relevance, ideally supported by human genetic or patient-derived data, adds further weight.

A good drug target is closely linked to disease pathology, has accessible binding sites, can be modulated to produce a therapeutic effect, and is expressed differentially between diseased and healthy tissue. Druggability refers to the structural and biophysical properties that make the target amenable to modulation by a small molecule or biologic.

Most clinical failures attributed to poor target validation stem from one of three causes: the target was not sufficiently linked to human disease biology, the preclinical model used did not accurately represent the clinical condition, or compound engagement in vivo was assumed rather than demonstrated. All three failures can be reduced through more rigorous early validation.

CRISPR-based genetic screens in 3D organoid models, multi-omics integration, and AI-driven target prioritisation are reshaping target validation. These approaches improve biological relevance, reduce false-positive rates, and allow the analysis of datasets at a scale that was not previously feasible. AI-nominated targets are now entering clinical trials.

References

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  2. Jia, Z. C., Yang, X., Wu, Y. K., Li, M., Das, D., Chen, M. X., & Wu, J. (2024). The art of finding the right drug target: emerging methods and strategies. Pharmacological Reviews, 76(5), 896-914.

  3. Zhou, Y., Zhang, Y., Zhao, D., Yu, X., Shen, X., Zhou, Y., … & Zhu, F. (2024). TTD: Therapeutic Target Database describing target druggability information. Nucleic acids research, 52(D1), D1465-D1477.

  4. Whitepaper- Identifying Druggable Therapeutic Targets. https://www.excelra.com/whitepaper/identifying-druggable-therapeutic-targets-unveiling-promising-avenues-in-drug-discovery/

  5. Laura Elizabeth Lansdowne, 2024. Target Identification & Validation in Drug Discovery. https://www.technologynetworks.com/drug-discovery/articles/target-identification-validation-in-drug-discovery-312290.

  6. Du, P., Fan, R., Zhang, N., Wu, C., & Zhang, Y. (2024). Advances in integrated multi-omics analysis for drug-target identification. Biomolecules, 14(6), 692.

  7. van Vugt, M., Finan, C., Chopade, S., Providencia, R., Bezzina, C. R., Asselbergs, F. W., … & Schmidt, A. F. (2024). Integrating metabolomics and proteomics to identify novel drug targets for heart failure and atrial fibrillation. Genome Medicine, 16(1), 120.

  8. Puniani, S., Gupta, P., Singh, N., Nagpal, D., & Sheikh, A. M. (2025). Targeting disease: Computational approaches for drug target identification. Advances in pharmacology (San Diego, Calif.), 103, 163-184.

  9. Ling, X., & Liu, T. (2022). Innovative CRISPR screening promotes drug target identification.

  10. Hu, B., Zhong, L., Weng, Y., Peng, L., Huang, Y., Zhao, Y., & Liang, X. J. (2020). Therapeutic siRNA: state of the art. Signal transduction and targeted therapy, 5(1), 101.

  11. He, S. J., Li, J., Zhou, J. C., Yang, Z. Y., Liu, X., & Ge, Y. W. (2024). Chemical proteomics accelerates the target discovery of natural products. Biochemical Pharmacology, 230, 116609.

  12. Paananen, J., & Fortino, V. (2020). An omics perspective on drug target discovery platforms. Briefings in bioinformatics, 21(6), 1937-1953.

  13. Emmerich, C. H., Gamboa, L. M., Hofmann, M. C., Bonin-Andresen, M., Arbach, O., Schendel, P., … & Parnham, M. J. (2021). Improving target assessment in biomedical research: the GOT-IT recommendations. Nature reviews Drug discovery, 20(1), 64-81.

  14. Kalter, N., Fuster-García, C., Silva, A., Ronco-Díaz, V., Roncelli, S., Turchiano, G., … & Hendel, A. (2025). Off-target effects in CRISPR-Cas genome editing for human therapeutics: Progress and challenges. Molecular Therapy Nucleic Acids.

  15. Plenge, R. M., Scolnick, E. M., & Altshuler, D. (2013). Validating therapeutic targets through human genetics. Nature reviews Drug discovery, 12(8), 581-594.

  16. Meng, X., Reis, N., Bassik, M. C., & Pașca, S. P. (2025). CRISPR screens in human neural organoids and assembloids. Nature Protocols, 1-21.

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