Paper Info
Title
BCL::Mol2D-a robust atom environment descriptor for QSAR modeling and lead optimization.
Abstract
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.
Year
DOI
Venue
2019
10.1007/s10822-019-00199-8
Journal of Computer-Aided Molecular Design
Keywords
Field
DocType
QSAR, Molecular descriptor, Sensitivity analysis, Cheminformatics, Pharmacophore mapping
Molecular descriptor,Pharmacophore,Quantitative structure–activity relationship,Drug discovery,Pattern recognition,Computational chemistry,Chemistry,PubChem,Artificial intelligence,Artificial neural network,Cheminformatics
Journal
Volume
Issue
ISSN
33
5
0920-654X
Citations 
PageRank 
References 
0
0.34
0
Authors
4
Name
Order
Citations
PageRank
Oanh Vu120.75
Jeffrey L. Mendenhall282.52
Doaa Altarawy310.69
J Meiler44211.15