BCL::Conf

BCL::Conf generates conformational ensembles of small molecules using a knowledge-based conformation sampling algorithm and a rotamer library derived from the Crystallography Open Database (COD) to improve binding-conformer recovery.

Key Features:

• Knowledge-based sampling: Employs a knowledge-based conformation sampling algorithm for small-molecule conformer generation.
• COD-derived rotamer library: Leverages a rotamer library derived from the Crystallography Open Database (COD), which is freely accessible.
• BioChemical Library integration: Functions as a component of the BioChemical Library (BCL) cheminformatics toolkit.
• Predecessor rotamer source: Previously used a fragment rotamer library sourced from the Cambridge Structural Database (CSD).
• Benchmark validation: Demonstrates enhanced recovery of native conformers on the Platinum dataset (2859 high-quality protein-ligand complexes).
• Comparative benchmarking: Evaluated against Conformator, Balloon, RDKit DG, ETKDG, Confab, Frog2, MultiConf-DOCK, CSD conformer generator, ConfGenX-OPSL3 force field, Omega, excalc, iCon, and MOE.
• Binding-conformer recovery: Significantly outperforms the CSD conformer generation algorithm in binding conformer recovery across various ensemble sizes.
• Performance: Maintains fast rates of conformer generation.

Scientific Applications:

• Computational chemistry: Generation of conformational ensembles for molecular modeling and analysis.
• Drug discovery: Recovery of binding conformers for structure-based drug design and ligand–protein interaction studies.
• Molecular modeling: Creation of diverse conformer libraries for downstream modeling and docking workflows.

Methodology:

Uses a knowledge-based conformation sampling algorithm that leverages a COD-derived rotamer library (previously a CSD fragment rotamer library) and was validated on the Platinum dataset (2859 high-quality protein-ligand complexes) and benchmarked against multiple conformer generators listed above.