Molecular and structure-based drug design: From theory to practice

Area of Science:

• Biochemistry
• Computational Chemistry
• Pharmacology

Background:

• Structure-based drug design (SBDD) and molecular docking are pivotal in modern drug discovery.
• High-resolution structural data from techniques like NMR, cryo-EM, and X-ray crystallography are essential for SBDD.

Purpose of the Study:

• To review the principles and methodologies of SBDD and molecular docking.
• To highlight advancements, including machine learning integration, in molecular docking.
• To discuss the challenges and future directions in these fields.

Main Methods:

• Utilizing high-resolution structural data of biological targets for SBDD.
• Employing molecular docking simulations to predict ligand-receptor interactions.
• Incorporating machine learning to enhance docking accuracy and scoring functions.

Main Results:

• SBDD enables the design of drugs with improved selectivity and efficacy.
• Molecular docking predicts ligand binding orientation and interaction strength.
• Advancements address limitations in prediction accuracy, protein flexibility, and solvation effects.

Conclusions:

• SBDD and molecular docking are indispensable tools in identifying and optimizing therapeutic agents.
• Ongoing innovation and interdisciplinary collaboration are crucial for advancing molecular docking in drug discovery.
• These computational approaches significantly contribute to the development of novel therapies.