Knowledge-based libraries for predicting the geometric preferences of druglike molecules

Area of Science:

• Computational chemistry
• Structural bioinformatics
• Drug discovery

Background:

• Predicting molecular geometry is crucial for drug discovery and development.
• Existing methods may struggle with novel or underrepresented molecular structures.
• The Cambridge Structural Database (CSD) is a valuable resource for molecular geometry data.

Purpose of the Study:

• To develop an automated method for generating libraries that predict the geometric preferences of druglike molecules.
• To ensure comprehensive coverage of molecular dimensions, including bond lengths, valence angles, rotamers, and ring structures.
• To validate the accuracy of these predictive libraries against newly published molecular structures.

Main Methods:

• Automated generation of molecular libraries based on distributions from CSD crystal structures.
• Cascade searching of libraries to find relevant distributions for poorly represented features.
• Inclusion of atom chirality in geometry distributions for rotamers and rings.
• Validation using druglike molecules published after the latest CSD data used in library generation.

Main Results:

• The generated libraries are comprehensive for bond lengths, valence angles, and rotamers.
• Templates are produced for a large majority of unfused and fused rings.
• The method accounts for atom chirality in rotamer and ring geometry.
• Validation confirmed the high prediction accuracy of the libraries for novel structures.

Conclusions:

• Automated library generation provides a robust method for predicting druglike molecule geometry.
• The libraries offer accurate templates for diverse molecular features, enhancing drug design.
• This approach improves the prediction of molecular dimensions, especially for novel chemical entities.