Cheminformatics in Natural Product-based Drug Discovery

Area of Science:

• Medicinal Chemistry
• Computational Chemistry
• Pharmacology

Background:

• Natural products are a vital source of drug leads.
• Advancements in cheminformatics offer powerful tools for natural product research.
• Efficiently navigating the complexity of natural product chemical space is crucial for drug discovery.

Purpose of the Study:

• To provide a comprehensive survey of cheminformatics methods applicable to natural product drug discovery.
• To discuss the scope and limitations of current in silico approaches.
• To highlight key areas including data curation, dereplication, virtual screening, and de novo design.

Main Methods:

• Review of existing literature on cheminformatics applications in natural product research.
• Discussion of various computational approaches: rule-based, similarity-based, shape-based, pharmacophore-based, network-based, docking, and machine learning.
• Analysis of data resources for chemical, biological, and structural information on natural products.

Main Results:

• Cheminformatics tools enable efficient data curation and dereplication of natural products.
• In silico methods facilitate the analysis, visualization, and comparison of natural product chemical space.
• Computational approaches aid in predicting bioactivities, ADME/Tox profiles, and potential assay interference.

Conclusions:

• Cheminformatics significantly enhances the efficiency and success rate of natural product-based drug discovery.
• The discussed methods offer valuable strategies for exploring, evaluating, and designing novel natural product-inspired therapeutics.
• Further development and application of these computational tools are essential for unlocking the full potential of natural products in medicine.