Navigating structure-based drug discovery with emerging innovations in physics- and knowledge-based approaches.

Area of Science:

• Computational chemistry and pharmacology
• Drug discovery and development

Background:

• Structure-based drug design (SBDD) is a cornerstone of modern pharmaceutical research.
• Computational methods, including physics-based and knowledge-based approaches, are integral to SBDD.
• Significant advancements have been made, yet practical application faces hurdles.

Purpose of the Study:

• To critically review recent advancements in computational methods for SBDD.
• To analyze the strengths and limitations of current physics-based and knowledge-based techniques.
• To explore synergistic strategies for future drug design.

Main Methods:

• Review of recent literature on computational drug design.
• Analysis of physics-based simulation techniques.
• Evaluation of knowledge-based data mining and machine learning approaches.

Main Results:

• SBDD methods show rapid evolution, integrating computational approaches throughout drug discovery.
• Key challenges persist, including accuracy, generalizability, computational cost, and ensuring chemical synthesizability.
• Synergies between physics-based and knowledge-based methods offer promising avenues for improvement.

Conclusions:

• Continued development of computational SBDD is crucial for efficient drug discovery.
• Addressing current limitations through integrated approaches will enhance practical applicability.
• Future research should focus on combining diverse computational strategies for robust drug design.