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In the fields of life science research and drug development, accurately identifying peptide drug targets is a crucial step that determines the progress of research. CD ComputaBio utilizes multi-omics data fusion technologies, including bioinformatics analysis, network pharmacology analysis, and deep learning algorithms to provide data-driven peptide target identification services.
Data-driven methods are straddling ligand-based and structure-based approaches in that they can leverage compound or target-related information to discover new drug targets. This method emerged with the development of technologies such as artificial intelligence (AI), genomics, and proteomics. In peptide target identification, data-driven methods rely on advanced algorithms like machine learning and deep learning to deeply mine and analyze complex biological information, revealing the interactions between peptides and targets. This innovative approach greatly enhances the efficiency, accuracy, and comprehensiveness of target identification, providing support for drug development and other related fields.
Fig. 1 Workflow of artificial intelligence (AI)-driven target identification. (PUN F W, et al., 2023)
CD ComputaBio breaks through the limitations of traditional experiments, which are time-consuming and costly. By leveraging multi-omics data, such as genomics, transcriptomics, and proteomics, we achieve efficient and comprehensive target screening and provide clients with data-driven peptide target identification services.
Data Collection
Comprehensively gather high-quality biological data from authoritative public databases and experimental data provided by clients, including gene expression profiles, protein interaction networks, metabolic pathway data, and more.
Data Preprocessing
Bioinformatics software and statistical methods are used to perform operations such as normalization, standardization, and batch effect correction on the data to ensure data quality and availability.
Target Identification
Utilize bioinformatics and machine learning algorithms to analyze large-scale biological data and predict potential targets for peptides.
Result Delivery
A detailed experimental report is delivered, including a list of potential targets, relevant analysis data, functional annotations, and disease-related information.
Machine Learning
By analyzing complex information such as gene expression and omics data, it is possible to learn the potential association patterns between peptides and targets, guiding the selection of molecular pairings and predicting the key proteins responsible for specific biological effects.
Network Analysis
By integrating multi-source heterogeneous data and constructing complex biological networks, the pathology of diseases can be dissected into multiple subnetworks for analysis, thereby revealing the key receptors that can be targeted by known active peptides.
Text Mining
Utilizing machine learning for information extraction in medical and disease databases, large volumes of textual data can be analyzed to identify potential information related to peptide targets, including disease-associated proteins and drug mechanisms of action.
CD ComputaBio provides data-driven peptide target identification services, helping you precisely locate key targets in complex data to accelerate scientific innovation. Whether you are facing challenges in selecting disease targets or need to optimize your drug development strategy, we will provide customized solutions. Contact us to explore more possibilities in life sciences.
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CD ComputaBio offers computation-driven peptide design services to research institutions, pharmaceutical, and biotechnology companies.
