Analysis and Comparison of Vector Space and Metric Space Representations in QSAR Modeling.

ABSTRACT: The performance of quantitative structure-activity relationship (QSAR) models largely depends on the relevance of the selected molecular representation used as input data matrices. This work presents a thorough comparative analysis of two main categories of molecular representations (vector space and metric space) for fitting robust machine learning models in QSAR problems. For the assessment of these methods, seven different molecular representations that included RDKit descriptors, five different fingerprints types (MACCS, PubChem, FP2-based, Atom Pair, and ECFP4), and a graph matching approach (non-contiguous atom matching structure similarity; NAMS) in both vector space and metric space, were subjected to state-of-art machine learning methods that included different dimensionality redu

SUBMITTER: Kausar S 

PROVIDER: S-EPMC6539555 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature