Drug Target Identification

The Target Identification Data Package provides comprehensive coverage of 16,558 **drugs** and 19,535 **targets** spanning 743 **therapeutic categories**. This data also includes ADMET properties to support predictive modelling.

This package is ideal in your efforts to:

- **Generate hypotheses** by exploring the connections between drugs, targets, metabolism, chemical structures, adverse effects, and more.

- **Evaluate research** trends by therapeutic area, target category, functional groups, and more.

- **Augment your analysis** of novel targets with detailed information about previously studied targets, including structural similarity, functional groups, metabolic reactions, indications, adverse effects, and drug-drug interactions.

---

DrugBank is the intelligence operating system (OS) built for drug discovery and development. Our mission is to ensure biomedical data and commercial insight is connected and used to its fullest potential, empowering biopharma to bring life-changing therapies to patients faster. Using both AI and expert curation, we’ve crafted the most trusted, comprehensive drug knowledgebase to draw fast, defensible, and contextualized insights. We offer AI and machine learning-ready datasets for download that are packaged to serve your unique needs.

The following data modules are included in this package:

- **Drugs**: This module provides a comprehensive list of all approved and investigational drugs. Each drug includes detailed molecular descriptions and relevant nomenclature and identifiers. The dataset includes coverage of small molecule drugs and biologics.

- **Conditions**: A complete hierarchy of condition terms describing diseases, symptoms, and other medical conditions.

- **Categories**: The categories module describes pharmacological and therapeutic categories for drugs. It also includes ATC categories, a commonly used hierarchy of anatomical and pharmacological groups.

- **Pathways**: Metabolic, disease, and biological pathways that drugs are involved in, as identified by the Small Molecule Pathway Database (SMPDB).

- **Metabolic Reactions**: A sequential representation of the metabolic reactions that drugs are involved in. Depending on available information, this may include metabolizing enzymes, reaction type, substrates, products, pharmacological activity of metabolites, and a structural representation of the biochemical reactions.

- **Drug Protein Relationships and Drug Targets**: Describes the relationship between drugs and targets, enzymes, carriers, or transporters. Annotation includes the pharmacological action, and the type of interaction (antagonist, agonist, substrate, inhibitor, or inducer).

- **Adverse Effects**:<br/>Each structured adverse effect represents a known adverse effect of a given drug, when used in a specific situation. Some adverse effects are specific to a certain route of administration, or patients of a certain age group.

- **Drug Interactions**: The Drug-Drug Interactions data module describes drugs that, when administered concomitantly with the drug of interest, will affect its activity or result in adverse effects. These interactions may be synergistic or antagonistic depending on the physiological effects and mechanism of action of each drug. Each interaction description includes a summary description and detailed description of the mechanism of the interaction, as well as management information, a severity rating, and type of evidence.

- **Drug Indications**: Describes the known uses of a drug, including prescription-only, off-label, and over-the-counter indications. This dataset covers many types of indications including the treatment, prevention, and management of conditions.

- **References**: This module provides comprehensive citations substantiating the facts mentioned within DrugBank.

---<br/>The workflow to request this data is as follows:

- Click the Get button to try the data
- Click the “Request” button to request the complete package
- DrugBank will get in touch with you to describe how the data will be shared

搜集汇总

背景与挑战

背景概述

该数据集提供16,558种药物与19,535个靶点的全面关联数据，覆盖743个治疗类别，并包含ADMET属性及多个数据模块，支持药物发现和开发的研究与分析。