Target Identification and Validation Service
In the early stages of drug development, target identification and validation are crucial. Given the challenges of identifying biological targets for compounds through traditional biological experiments, CD ComputaBio employs cutting-edge computational technologies to predict and screen potential targets. This provides strong support for drug development and helps pharmaceutical companies and research institutions efficiently advance the drug discovery process.
Introduction to Target Identification and Validation
Traditional experiment-based target identification and validation are time-consuming, labor-intensive, and resource-intensive. Therefore, computational methods have become a powerful alternative for achieving efficient target screening. Depending on the availability of protein structure and the chemical structure of the compound of interest, methods such as pharmacophore screening and reverse docking are used to predict novel biological targets for small molecules. Simultaneously, artificial intelligence is rapidly developing in the field of target discovery. Machine learning, as a crucial component, not only predicts biological targets for existing drugs or compounds but also discovers novel therapeutic targets for new diseases.
Fig.1 Three exploratory strategies for target identification. (Pun F W, et al., 2020)
Computational Tools for Target Identification and Validation
| Tool |
Description |
References |
| TargetRNA3 |
A tool for predicting targets of small regulatory RNAs in prokaryotes. |
Tjaden (2023) |
| TarIKGC |
A tool for target prioritization that leverages semantics enhanced knowledge graph (KG) completion. |
Shen et al. (2025) |
| MFCADTI |
A novel method to improve the predictive capability for drug-target interactions (DTIs) by integrating multi-source feature through cross-attention mechanisms. |
Quan et al. (2025) |
| MAI-TargetFisher |
A target identification method integrating structure-based target identification & ligand-based target identification. |
Li et al. (2025) |
Our Services
CD ComputaBio is a leader in target identification and validation services, leveraging its deep expertise in cutting-edge technologies like computational biology and artificial intelligence. We are committed to providing clients with efficient and cost-effective target identification solutions to accelerate drug discovery pipelines.
Disease-specific Target Identification and Validation
Integrating multi-omics data (genomic, transcriptomic, proteomic, etc.) and employing bioinformatics and machine learning, this approach identifies key targets associated with specific diseases, assesses their druggability, and performs computational validation to accelerate novel drug development.
Drug Target Identification and Validation
Utilizing methods such as molecular docking, deep learning, and chemical similarity analysis, this approach predicts potential on-targets and off-targets of known drugs, provides visualization of binding modes, and offers in-depth analysis of their mechanisms of action.
Methods for Target Identification and Validation
- Data-driven Target Identification and Validation
Utilizing artificial intelligence technologies to efficiently identify and screen the most promising drug targets from vast biological data, deeply mining key targets closely associated with the development of specific diseases, or accurately predicting the potential molecular targets of drugs.
- Ligand-based Target Identification and Validation
Relying on known active ligand information and analyzing its structural and pharmacophore characteristics to deduce the key attributes required for pharmacological activity provides crucial support for target identification. This method is suitable for cases with known active compounds, discovering new potential targets by comparing pharmacophores from multiple ligands.
- Structure-based Target Identification and Validation
Depending on the three-dimensional structural data of proteins, with the accumulation of databases like the Protein Data Bank (PDB), it plays a dominant role in computational methods, providing key support for target identification. Through methods like reverse docking, potential active targets are screened.
- Reverse Pharmacophore Modeling
- Binding Cavity Comparison
CD ComputaBio's target identification services, relying on computational methods, provide reliable, efficient, and cost-effective solutions for the pharmaceutical and biotechnology industries in the early critical stages of drug discovery. If you are interested in our target identification services, please feel free to contact us.
References:
- Pun, F W.; et al. AI-powered therapeutic target discovery[J]. Trends in pharmacological sciences. 2023, 44(9): 561-572.
- Tjaden, B. TargetRNA3: predicting prokaryotic RNA regulatory targets with machine learning[J]. Genome Biology. 2023, 24(1): 276.
- Shen, X.; et al. TarIKGC: A Target Identification Tool Using Semantics-Enhanced Knowledge Graph Completion with Application to CDK2 Inhibitor Discovery[J]. Journal of Medicinal Chemistry. 2025.
- Quan, N.; et al. MFCADTI: improving drug-target interaction prediction by integrating multiple feature through cross attention mechanism[J]. BMC bioinformatics. 2025, 26(1): 57.
- Li, S.; et al. MAI-TargetFisher: A proteome-wide drug target prediction method synergetically enhanced by artificial intelligence and physical modeling[J]. Acta Pharmacologica Sinica. 2025: 1-14.
* For Research Use Only.
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