In silico ADME modelling 2: computational models to predict human serum albumin binding affinity using ant colony systems

Bioorganic & Medicinal Chemistry
Sitarama B GunturiAkash Khandelwal

Abstract

Modelling of in vitro human serum albumin (HSA) binding data of 94 diverse drugs and drug-like compounds is performed to develop global predictive models that are applicable to the whole medicinal chemistry space. For this aim, ant colony systems, a stochastic method along with multiple linear regression (MLR), is employed to exhaustively search and select multivariate linear equations, from a pool of 327 molecular descriptors. This methodology helped us to derive optimal quantitative structure-property relationship (QSPR) models based on five and six descriptors with excellent predictive power. The best five-descriptor model is based on Kier and Hall valence connectivity index--Order 5 (path), Auto-correlation descriptor (Broto-Moreau) weighted by atomic masses--Order 4, Auto-correlation descriptor (Broto-Moreau) weighted by atomic polarizabilities--Order 5, AlogP98, SklogS (calculated buffer water solubility) [R=0.8942, Q=0.86790, F=62.24 and SE=0.2626]; the best six-variable model is based on Kier and Hall valence connectivity index of Order 3 (cluster), Auto-correlation descriptor (Broto-Moreau) weighted by atomic masses--Order 4, Auto-correlation descriptor (Broto-Moreau) weighted by atomic polarizabilities--Order 5, Atomi...Continue Reading

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Citations

Sep 12, 2008·Journal of Computer-aided Molecular Design·Rajarshi Guha
Aug 23, 2011·Journal of Chemical Information and Modeling·Felix HammannJörg Huwyler
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Jul 17, 2007·Chemical Biology & Drug Design·Omar Deeb, Bahram Hemmateenejad

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