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Drug Discovery: Overview

Drug Discovery: Overview

Drug discovery is a multifaceted process involving extensive screening, testing, and optimization of lead compounds to identify potential new drugs for therapeutic use. It combines several approaches, including screening large numbers of natural products, chemical modification of known active molecules, identification of new drug targets, and rational design based on biological mechanisms and drug-receptor structure. These approaches are carried out in both academic research laboratories and...

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Updated: Jun 9, 2026

Mass Spectrometry-Guided Genome Mining as a Tool to Uncover Novel Natural Products

Mass Spectrometry-Guided Genome Mining as a Tool to Uncover Novel Natural Products

Published on: March 12, 2020

Progress in Natural Products Target Discovery Technology.

Qiyuan Pan¹, Xiao Yuan¹, Jinmei Jin¹

¹Shanghai Frontiers Science Center of TCM Chemical Biology Institute of Interdisciplinary Integrative Medicine Research and Shuguang Hospital Shanghai University of Traditional Chinese Medicine Shanghai China.

|June 8, 2026

Summary

This summary is machine-generated.

Natural products offer drug discovery potential but identifying their targets is challenging. This review details experimental and computational methods, proposing an integrated workflow to validate targets and accelerate research.

Keywords:

artificial intelligence drug discovery molecular probes natural products target identification

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Area of Science:

Drug discovery and development
Natural products chemistry
Systems biology

Background:

Natural products are a rich source of drug candidates due to their biological activities.
Target identification is a significant hurdle in natural product drug discovery, complicated by chemical complexity and biological system heterogeneity.

Purpose of the Study:

To systematically review current technologies for natural product target discovery.
To propose an integrated experimental-computational workflow for robust target identification and validation.

Main Methods:

Review of experimental strategies (e.g., chemical proteomics) and computational methods (e.g., AI-driven approaches).
Analysis of case studies to illustrate target identification and mode of action elucidation.
Proposal of a reusable integrated workflow combining experimental and computational techniques.

Main Results:

Current approaches for natural product target discovery are summarized, highlighting their principles and applications.
An integrated workflow is proposed to address bottlenecks in advancing candidate hits to validated targets.
Common challenges and potential solutions in target discovery are discussed.

Conclusions:

The review provides a framework to improve the robustness of natural product target identification.
The proposed workflow aims to reduce false positives and fragmented evidence.
Facilitating translational research by enhancing mechanism-oriented studies is a key outcome.