In Silico Studies on Natural Products and Derivatives Against Different Types of Cancer

Area of Science:

• Computational chemistry and drug discovery
• Oncology and pharmaceutical research

Background:

• Cancer is a leading global cause of death, with late diagnosis contributing to increased mortality.
• Effective cancer treatment relies on early detection and the development of novel therapeutic agents.
• The World Health Organization (WHO) identifies cancer as the second leading cause of death worldwide.

Purpose of the Study:

• To review scientific literature on in silico analyses for proposing new antineoplastic agents.
• To explore computational techniques in drug discovery for glioblastoma, breast, colon, prostate, and lung cancer.
• To highlight the role of molecular docking and dynamics in identifying drug-receptor interactions.

Main Methods:

• Bibliographical review of research articles focusing on computational techniques in drug discovery.
• Analysis of studies detailing in silico methods, molecular docking simulations, and molecular dynamics.
• Presentation of 3D chemical structures and interactions between molecules and Protein Data Bank (PDB) receptors.

Main Results:

• Identified computational techniques contributing to the development of new or existing anticancer drugs.
• Highlighted key findings, methodologies, and conclusions from selected scientific studies.
• Presented molecular structures and interactions demonstrating significant computational responses.

Conclusions:

• In silico analyses are valuable tools for proposing novel anticancer agents.
• Computational methods can accelerate drug discovery and advance pharmaceutical research.
• This review aims to support future research in developing new antitumor drugs and enhancing scientific knowledge.