Proteomics-driven target identification integrates mass spectrometry, chemical proteomics, and artificial intelligence to improve therapeutic target validation and reduce clinical trial failure. By directly interrogating protein function, interactions, and drug engagement, this approach expands access to previously undruggable proteins and enables precision drug discovery.
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Inadequate target validation and insufficient understanding of drug mechanisms drive the majority of clinical trial failures in pharmaceutical development. Proteomics, a comprehensive study of cellular protein expression, structure, and function, addresses these challenges by providing direct insight into disease biology that genomics alone cannot capture.
By quantifying protein abundance, mapping interactions, identifying post-translational modifications (PTMs), and profiling drug–target engagement, proteomics-driven strategies offer powerful tools to identify novel therapeutic targets, validate disease relevance, and de-risk candidates before costly late-stage investments.1
Cutting-edge Proteomics Reshaping Drug Discovery
While proteins constitute the molecular targets for the vast majority of approved therapeutics, a substantial fraction of disease-relevant proteins remains inaccessible to conventional drug modalities.2 These so-called “undruggable” targets often lack well-defined ligand-binding pockets, including transcription factors, small GTPases such as KRAS, and protein–protein interaction interfaces.6 Recent innovations in proteomics technologies, such as chemical proteomics, advanced mass spectrometry (MS) platforms, and artificial intelligence (AI)-driven analysis, are fundamentally expanding the druggable proteome.
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MS proteomic analysis
Mass spectrometry-based proteomics represents a powerful technology for drug discovery, enabling comprehensive qualitative and quantitative analysis of protein expression, post-translational modifications, and molecular interactions. While genomic and transcriptomic approaches provide only indirect proxies of cellular activity, MS proteomics directly interrogates the functional biomolecules that drive biological processes and disease pathology.3
MS proteomics is involved in every stage of the drug discovery pipeline, from biomarker discovery and therapeutic target validation to lead compound optimization and precision medicine applications. Bottom-up MS proteomics, in particular, relies on enzymatic digestion of proteins into peptides prior to liquid chromatography (LC)–MS/MS analysis, enabling large-scale protein identification and quantification, whereas top-down approaches analyze intact proteins to preserve proteoform information.
To identify biomarkers, researchers compare protein expression in biological samples from diseased versus healthy individuals. This target is validated using targeted MS approaches, such as selected reaction monitoring (SRM) or parallel reaction monitoring (PRM).3
Unlocking the Proteome: a glimpse into Mass Spectrometry-Based Proteomics
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To develop drugs for neurodegenerative diseases, targeted MS proteomics methods, including LC-SRM and PRM, are used to identify and validate protein biomarkers for Alzheimer's disease and to uncover novel therapeutic targets. Integrative genetic and proteomic prioritization approaches have further improved target nomination by linking brain and plasma proteomic signatures with disease-associated loci.4 These approaches enable precise, high-throughput quantification of protein fragments in brain tissue, cerebrospinal fluid, and blood, facilitating drug development targeting specific disease mechanisms.
MS proteomics has also played an instrumental role in characterizing drug resistance in cancer and other diseases. By analyzing proteomic profiles of resistant and susceptible cell lines, tissues, or clinical isolates, this strategy helps identify specific proteins and differentially regulated pathways associated with resistance mechanisms.3 This enables researchers to understand therapeutic failure, compound optimization, and drug combination strategies to delay the onset of resistance.
Proteome-wide target engagement strategies such as thermal proteome profiling (TPP), activity-based protein profiling (ABPP), photoaffinity labeling (PAL), limited proteolysis–MS (LiP-MS), and cross-linking MS (XL-MS) further enable direct assessment of drug–protein interactions, binding specificity, and off-target effects in complex biological systems.3
Chemical proteomic analysis
While MS technology advances have expanded the detectable proteome, these methods primarily quantify protein abundance, providing limited insight into functional activity. Since phenotypic traits arise from the interplay between protein abundance and functional activity, accurately measuring activity remains critical yet challenging, given the complexity of biological systems. Research has shown that protein function depends heavily on interactions with other cellular molecules, including small metabolites.5
Understanding how small metabolites alter protein structure and function in dynamic cellular environments is a core challenge in biology and drug discovery. Chemical proteomics uses chemical biology tools to map molecular interactomes and probe protein activities. Activity-based protein profiling (ABPP), reactive residue profiling, and bio-orthogonal click chemistry enable covalent and proximity-based labeling of functional protein states within native biological systems.5 It enables high-throughput identification of covalent and non-covalent protein targets of small molecules both intracellularly and in vivo, facilitating off-target site identification and therapeutic target characterization.2
Recent advances in chemical proteomics have enhanced the efficiency and accuracy of metabolite target identification. Researchers have successfully applied this strategy to identify targets of lipid- and carbohydrate-metabolism molecules, facilitating the characterization of clinically relevant drug candidates.2
Fatty acid amide hydrolase inhibitors are being developed for the treatment of central nervous system diseases. However, the inhibitor BIA 10-2474 caused human neurotoxicity. Researchers used an alkyne-modified metabolite probe and click chemistry to discover that BIA 10-2474’s demethylated metabolite covalently modifies catalytic cysteine residues of aldehyde dehydrogenases, which protect the brain from oxidative stress. These off-target effects inhibit nervous system function, leading to metabolic dysregulation.2
Artificial Intelligence in proteomic data analysis
Proteomics data is complex, with high dimensionality and variability that create analytical challenges. Data variability arises from differences in protein expression across tissues, conditions, and time points, making it difficult to discern meaningful patterns. Furthermore, MS and other proteomics techniques generate noisy data with missing values. Artificial intelligence has emerged as a powerful tool for data processing, pattern recognition, and prediction in proteomics, addressing many of the analytical challenges mentioned above.7
Machine learning models process MS data to identify and quantify proteins more efficiently than traditional methods. Algorithms such as support vector machines and deep learning networks are used to recognize peptide spectra with improved accuracy and speed. AI denoises data by distinguishing real signals from background noise through pattern recognition, significantly improving data reliability.7
AI predicts protein-protein interactions using graph neural networks and natural language processing models. For biomarker discovery, AI analyzes protein expression differences to identify disease biomarkers, and classification algorithms distinguish diseased from healthy samples for early detection.7 AI also supports data-independent acquisition (DIA) analysis, automated feature extraction, predictive modeling, and hypothesis generation in large-scale MS datasets.3 Models like AlphaFold predict protein structures essential for drug targeting, while AI integrates proteomics, genomics, and metabolomics to provide systems-level insights.7
Despite its transformative potential, AI faces several significant challenges in proteomics applications. Data quality is the primary concern, as inconsistent or incomplete datasets can significantly impair AI model performance and lead to unreliable predictions. Model interpretability presents another substantial challenge, as black-box AI models can hinder biological interpretation and limit mechanistic insight.7
Conclusion
Proteomics-driven target identification and validation have become essential in modern drug discovery. This strategy enables comprehensive analysis of protein expression, modifications, and interactions that drive biological processes and disease pathology. By integrating AI with MS-based proteomics and chemical proteomic data, researchers can identify and validate drug targets with unprecedented precision, characterize resistance mechanisms, discover biomarkers, and map protein interaction networks.3,5,7
Moreover, proteome-wide engagement profiling and covalent targeting strategies are expanding therapeutic access to historically “undruggable” proteins, transforming the landscape of precision medicine.6 This data-driven approach is transforming drug discovery from empirical screening into mechanistically informed design.
Reference and Further Reading
- Al-Amrani S, et al. Proteomics: Concepts and applications in human medicine. World J Biol Chem. 2021;12(5):57-69. DOI:10.4331/wjbc.v12.i5.57, http://wjgnet.com/1949-8454/full/v12/i5/57.htm.
- Xie X, et al. Recent advances in targeting the "undruggable" proteins: from drug discovery to clinical trials. Signal Transduct Target Ther. 2023;8(1):335. DOI:10.1038/s41392-023-01589-z, https://www.nature.com/articles/s41392-023-01589-z.
- Tagliazucchi L, Costi MP. Mass Spectrometry Proteomics: A Key to Faster Drug Discovery. J Med Chem. 2026;69(1):30-71. DOI:10.1021/acs.jmedchem.5c01986, https://pubs.acs.org/doi/10.1021/acs.jmedchem.5c01986.
- Emerson NE, Swarup V. Proteomic Data Advance Targeted Drug Development for Neurogenerative Diseases. Biol Psychiatry. 2023;93(9):754-755. DOI:10.1016/j.biopsych.2023.02.003, https://linkinghub.elsevier.com/retrieve/pii/S0006322323000598.
- Kozoriz K, Lee JS. Chemical proteomics for a comprehensive understanding of functional activity and the interactome. Chem. Soc. Rev., 2025; 54:6186-6207. DOI:10.1039/D5CS00381D, https://pubs.rsc.org/en/content/articlelanding/2025/cs/d5cs00381d
- Zou M, et al. Therapeutic Target Identification and Drug Discovery Driven by Chemical Proteomics. Biology. 2024;13(8):555. DOI:10.3390/biology13080555, https://www.mdpi.com/2079-7737/13/8/555
- Srivastava U. AI in Proteomics Data Analysis: Revolutionizing Protein Research. Springer Nature Research Communication. 2024; Available at: https://communities.springernature.com/posts/ai-in-proteomics-data-analysis-revolutionizing-protein-research
Last Updated: Feb 12, 2026