Quantum Chemical Approaches in Structure-Based Virtual Screening and Lead Optimization.

Area of Science:

• Computational chemistry
• Drug discovery
• Quantum mechanics applications

Background:

• Computational chemistry is vital for drug lead discovery.
• Advancements in computing power and methodology have increased interest in quantum mechanics (QM) methods.
• QM calculations on biomacromolecules aim to improve accuracy in describing protein-ligand interactions and predicting binding affinities.

Purpose of the Study:

• To review recent applications of explicit QM-based methods in drug discovery.
• To highlight QM's role in small-molecule docking, scoring, and binding free-energy calculations for protein-ligand systems.

Main Methods:

• Review of recent literature on explicit quantum mechanics (QM) based methods.
• Application of QM in small-molecule docking and scoring.
• Calculation of binding free-energy in protein-ligand systems using QM.

Main Results:

• QM methods offer a more comprehensive energy description than molecular mechanics force fields, including electronic polarization, metal coordination, and covalent binding.
• QM methods are systematically improvable and offer greater transferability.
• Recent applications show promise for QM in enhancing the accuracy of protein-ligand interaction predictions.

Conclusions:

• Explicit QM-based methods are increasingly explored for drug lead discovery.
• Despite current challenges in routine industrial application, QM methods are poised to become more significant in the drug discovery pipeline.
• QM provides a more accurate and transferable approach to modeling molecular interactions crucial for drug design.