Conformer Generation for Structure-Based Drug Design: How Many and How Good?

Area of Science:

• Computational Chemistry
• Medicinal Chemistry
• Drug Discovery

Background:

• Conformer generation is crucial for structure-based drug design, impacting rigid-body and flexible ligand methods.
• The quality and characteristics of conformational ensembles directly influence the success of drug discovery algorithms.
• Existing knowledge on optimal ensemble parameters is often empirical; quantification is needed.

Purpose of the Study:

• To empirically determine general principles for conformational ensemble size, diversity, and quality in structure-based drug design.
• To quantify the impact of different conformer generation and post-processing strategies on drug discovery task performance.
• To compare state-of-the-art deep learning methods with classical approaches for conformer generation.

Main Methods:

• Investigated a state-of-the-art generative deep learning approach and a classical geometry-based approach.
• Evaluated the effect of energy minimization as a post-processing step.
• Analyzed the influence of ensemble size and diversity (RMSD filtering) on performance.

Main Results:

• Quantified the impact of ensemble size, diversity, and quality on structure-based drug discovery tasks.
• Demonstrated how choices in conformer generation and post-processing affect the ability to recapitulate bioactive conformations.
• Showcased the influence on pharmacophore screening and molecular docking performance.

Conclusions:

• Established quantifiable principles for optimizing conformational ensembles in drug design.
• Highlighted the importance of ensemble characteristics for accurate structure-based drug discovery.
• Provided insights into selecting appropriate methods for conformer generation and post-processing.