Predicting biological activity: Computational approach using novel distance based molecular descriptors

• Published in: Computers in biology and medicine 2012-10, Vol.42 (10), p.1026-1041
• Main Authors: Dutt, R, Madan, A.K
• Format: Article
• Language: English
• Subjects: Biological activity; Computational Biology - methods; CRF-1 antagonists; Databases, Factual; Decision tree; Decision Trees; Drug Discovery - methods; Humans; Internal Medicine; Models, Biological; Models, Molecular; Molecular descriptors; Molecular structure; Moving average analysis; Other; Pyrazines; Pyrazines - chemistry; Pyrazines - metabolism; Quantitative Structure-Activity Relationship; Receptors, Corticotropin-Releasing Hormone - antagonists & inhibitors; Receptors, Corticotropin-Releasing Hormone - metabolism; Reproducibility of Results; Reverse engineering; Software; Studies; Superpendentic eccentric distance sum indices; Topological descriptors
• ISSN: 0010-4825; 1879-0534
• DOI: 10.1016/j.compbiomed.2012.08.006

Abstract Four novel distance based molecular descriptors termed as superpendentic eccentric distance sum indices 1–4 (denoted by: ∫ P − 1 E D S , ∫ P − 2 E D S , ∫ P − 3 E D S and ∫ P − 4 E D S ) as well as their topochemical counterparts (denoted by: ∫ c P − 1 E D S , ∫ c P − 2 E D S , ∫ c P − 3 E D S and ∫ c P − 4 E D S ) have been conceptualized and developed in the present study. The sensitivity towards branching, discriminating power, and degeneracy of the proposed novel descriptors were investigated. Utility of these indices was investigated for development of models through decision tree and moving average analysis for the prediction of human corticotropin releasing factor-1 receptor binding affinity of substituted pyrazines. A wide variety of 46 2D and 3D molecular descriptors including proposed indices was employed for development of models through decision tree and moving average analysis. The calculation of most of these descriptors for each compound of the dataset was performed using online E-Dragon software (version 1.0). An in-house computer programme was also employed to calculate additional topological descriptors which did not figure in E-Dragon software . The decision tree classified and correctly predicted the input data with an impressive accuracy of 92% in the training set and 71% during cross-validation. A total of three descriptors, identified by decision tree, were subsequently utilized for development of suitable models using moving average analysis. These models predicted human corticotropin releasing factor-1 receptor binding affinity with an accuracy of ≥85%. The statistical significance of models was assessed through sensitivity, specificity and Matthew's correlation coefficient. High discriminating power, high sensitivity towards branching amalgamated with negligible degeneracy offer proposed descriptors a vast potential for use in the quantitative structure–activity/property/toxicity relationships so as to facilitate drug design.