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the claim
In silico analyses can be used to identify proteins that are worthy of experimental follow-up as potential drug targets.
the verdict
SUPPORTED
the evidence backs this
Recorded sources
10 sources for · 0 against
Is it true that in silico analyses can be used to identify proteins that are worthy of experimental follow-up as potential drug targets?
Yes. Multiple studies and reviews demonstrate that in silico analyses, machine learning, and artificial intelligence pipelines successfully identify and prioritize proteins as promising drug targets for experimental follow-up.
Verdict: SUPPORTED · 10 sources for, 0 against · Last checked
The analysis
The retrieved papers consistently support the claim that in silico analyses and computational frameworks are widely used to identify and prioritize proteins for experimental validation in drug discovery. There are no papers refuting this well-established methodological paradigm.
Evidence for · 10
The study demonstrates that computational domain-based prediction (DRUIDom) can identify and experimentally validate new compound-target interactions and drug candidates.
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Doğan, T., Güzelcan, E. A., Baumann, M., Koyas, A., Atas, H., Baxendale, I. R., Martin, M., & Cetin-Atalay, R. (2021). Protein domain-based prediction of drug/compound–target interactions and experimental validation on LIM kinases. doi.org. https://doi.org/10.1371/journal.pcbi.1009171
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More for · 9
The review outlines how AI-driven computational tools and graph-based drug-target interaction models facilitate effective target identification in drug design.
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R, D., P, G., D, S., & V, A. (2026). AI-driven computational drug design: tools, workflow and challenges.. doi.org. https://doi.org/10.1039/d6ra02374f
The paper discusses the evolution of computational target identification and rational drug design to accelerate the development of antimicrobial agents.
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R, G., P, S., K, R., MM, A., S, L., R, S., N, K., & NK, J. (2026). <i>In silico</i> drug discovery pipelines targeting antibiotic resistance: from genomes to leads.. doi.org. https://doi.org/10.3389/fbinf.2026.1839606
The article highlights how AlphaFold and artificial intelligence bridge genomic data and functional annotation to prioritize therapeutic targets.
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T, d. O. T., RCC, d. S., MCS, A., R, d. A. S., JES, E., & S, S. (2026). Innovative approaches to therapeutic target discovery amid the global challenge of antimicrobial resistance.. doi.org. https://doi.org/10.1007/s10822-026-00905-3
The study establishes an integrated computational framework to prioritize therapeutic targets and design multi-target candidate molecules for complex liver disease.
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LJdO, A., GCM, d. O., & LM, d. O. (2026). A Systems Biology and Generative Artificial Intelligence Framework for Rational Multi-Target Drug Design in Metabolic Dysfunction-Associated Steatotic Liver Disease: Proof-of-Concept Identification of HPM-001. doi.org. https://doi.org/10.21203/rs.3.rs-10620781/v1
The review notes that in silico techniques offer systematic methods for discovering metabolic weaknesses and prioritizing therapeutic targets in parasitology.
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K, M., A, E., H, E., & M, K. (2026). From genomes to interventions: computational strategies transforming parasitology.. doi.org. https://doi.org/10.3389/fvets.2026.1823640
The study utilizes a computational drug discovery pipeline to successfully prioritize and dock putative COX-2-targeting natural product metabolites for future experimental validation.
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V, P., S, S., S, R., PS, B., K, S., A, K., R, T., D, S., & JG, C. (2026). Genomic exploration and <i>in silico</i> prioritization of putative COX-2-targeting metabolites from <i>Streptomyces</i> sp. VITGV156 (MCC 4965).. doi.org. https://doi.org/10.3389/fbinf.2026.1840680
The research presents an integrative computational pipeline that combines transcriptomics and network analysis to prioritize therapeutic candidates in neuroblastoma.
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A, Z. (2026). Integrative Systems Biology Prioritizes BCL2, ALK, and CDK4 as Therapeutic Candidates in Neuroblastoma through Multi-Centrality Network Analysis, Cross-Database Validation, Independent Docking Validation, and Confidence-Threshold Sensitivity Analysis.. doi.org. https://doi.org/10.1016/j.compbiolchem.2026.109419
The study applies computational strategies to identify human genetic evidence and systematically prioritize drug candidates targeting specific pathway nodes.
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Y, Y. (2026). Astrocytic coactivation of Wnt beta catenin and pyroptosis pathways in neuropathic pain with human genetic evidence and drug prioritization. doi.org. https://doi.org/10.21203/rs.3.rs-10882221/v1
The paper demonstrates that automated multi-agent frameworks can effectively conduct structure-based drug design and prioritize candidate sets.
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Y, Y., S, T., G, W., & Y, L. (2026). DDA: a traceable multi-agent framework for automated structure-based drug design.. doi.org. https://doi.org/10.3389/fchem.2026.1914886
What does the evidence say?
The retrieved papers consistently support the claim that in silico analyses and computational frameworks are widely used to identify and prioritize proteins for experimental validation in drug discovery. There are no papers refuting this well-established methodological paradigm.
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