TY - JOUR

T1 - Development of quantitative structure-activity relationship models for vapor pressure estimation using computed molecular descriptors

AU - Basak, Subhash C

AU - Mills, Denise

PY - 2005/11/19

Y1 - 2005/11/19

N2 - Vapor pressure is an important property which is an indicator of chemical volatility, along with transport, partitioning, fate and distribution of environmental pollutants. Various models have been developed for the prediction of vapor pressure of chemicals using physicochemical and calculated structural properties. We have used different classes of graph theoretic indices, e.g., topostructural indices, topochemical indices, geometrical (3D) indices and, quantum chemical descriptors, for the development of predictive models for vapor based on a structurally diverse set of 469 chemicals. Initially, a set of 379 molecular descriptors was calculated using the software POLLY, Triplet, Sybyl, MOPAC, and Molconn-Z. Comparatively, three linear regression methodologies were used to develop hierarchical QSAR (HiQSAR) models, namely ridge regression (RR), principal components regression (PCR), and partial least squares (PLS) regression. The results indicate that, in general, RR outperforms PCR and PLS, and that the easily calculated topological descriptors are sufficient for the prediction of vapor pressure based on this large, diverse set of chemicals.

AB - Vapor pressure is an important property which is an indicator of chemical volatility, along with transport, partitioning, fate and distribution of environmental pollutants. Various models have been developed for the prediction of vapor pressure of chemicals using physicochemical and calculated structural properties. We have used different classes of graph theoretic indices, e.g., topostructural indices, topochemical indices, geometrical (3D) indices and, quantum chemical descriptors, for the development of predictive models for vapor based on a structurally diverse set of 469 chemicals. Initially, a set of 379 molecular descriptors was calculated using the software POLLY, Triplet, Sybyl, MOPAC, and Molconn-Z. Comparatively, three linear regression methodologies were used to develop hierarchical QSAR (HiQSAR) models, namely ridge regression (RR), principal components regression (PCR), and partial least squares (PLS) regression. The results indicate that, in general, RR outperforms PCR and PLS, and that the easily calculated topological descriptors are sufficient for the prediction of vapor pressure based on this large, diverse set of chemicals.

KW - Hierarchical QSAR

KW - Partial least squares regression

KW - Principal components regression

KW - Ridge regression

KW - Topological indices

KW - Vapor pressure

UR - http://www.scopus.com/inward/record.url?scp=28044459869&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=28044459869&partnerID=8YFLogxK

M3 - Article

AN - SCOPUS:28044459869

SN - 1424-6376

VL - 2005

SP - 308

EP - 320

JO - Arkivoc

JF - Arkivoc

IS - 10

ER -