BCL::Mol2D-a robust atom environment descriptor for QSAR modeling and lead optimization.

Vu O, Mendenhall J, Altarawy D, Meiler J
J Comput Aided Mol Des. 2019 33 (5): 477-486

PMID: 30955193 · PMCID: PMC6824857 · DOI:10.1007/s10822-019-00199-8

Comparing fragment based molecular fingerprints of drug-like molecules is one of the most robust and frequently used approaches in computer-assisted drug discovery. Molprint2D, a popular atom environment (AE) descriptor, yielded the best enrichment of active compounds across a diverse set of targets in a recent large-scale study. We present here BCL::Mol2D descriptors that outperformed Molprint2D on nine PubChem datasets spanning a wide range of protein classes. Because BCL::Mol2D records the number of AEs from a universal AE library, a novel aspect of BCL::Mol2D over the Molprint2D is its reversibility. This property enables decomposition of prediction from machine learning models to particular molecular substructures. Artificial neural networks with dropout, when trained on BCL::Mol2D descriptors outperform those trained on Molprint2D descriptors by up to 26% in logAUC metric. When combined with the Reduced Short Range descriptor set, our previously published set of descriptors optimized for QSARs, BCL::Mol2D yields a modest improvement. Finally, we demonstrate how the reversibility of BCL::Mol2D enables visualization of a 'pharmacophore map' that could guide lead optimization for serine/threonine kinase 33 inhibitors.

MeSH Terms (7)

Algorithms Drug Design Drug Discovery Humans Ligands Quantitative Structure-Activity Relationship Small Molecule Libraries

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