title: In silico ADME modelling 2: computational models to predict human serum albumin binding affinity using ant colony systems.
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, Atomic-Level-Based AI topological descriptors--AIdsCH, AlogP98, SklogS (calculated buffer water solubility) [R=0.9128, Q=0.89220, F=64.09 and SE=0.2411]. From the analysis of the physical meaning of the selected descriptors, it is inferred that the binding affinity of small organic compounds to human serum albumin is principally dependent on the following fundamental properties: (1) hydrophobic interactions, (2) solubility, (3) size and (4) shape. Finally, as the models reported herein are based on computed properties, they appear to be a valuable tool in virtual screening, where selection and prioritisation of candidates is required.
authors.affiliation: Life Sciences R&D Division, Advanced Technology Centre, Tata Consultancy Services Limited, # 1, Software Units Layout, Madhapur, Hyderabad 500 081, India.
authors.forename: Sitarama B
authors.initials: SB
authors.lastname: Gunturi
authors.forename: Ramamurthi
authors.initials: R
authors.lastname: Narayanan
authors.forename: Akash
authors.initials: A
authors.lastname: Khandelwal
mesh_terms: D000465:Algorithms; D001665:Binding Sites; D001667:Binding, Competitive; D019295:Computational Biology; D003195:Computer Communication Networks; D003198:Computer Simulation; D016208:Databases, Factual; D004353:Drug Evaluation, Preclinical; D006801:Humans; D066298:In Vitro Techniques; D016014:Linear Models; D004364:Pharmaceutical Preparations / Q000737:chemistry* / Q000378:metabolism; D021281:Quantitative Structure-Activity Relationship; D012044:Regression Analysis; D015203:Reproducibility of Results; D012709:Serum Albumin / Q000737:chemistry* / Q000378:metabolism; D012984:Software; D013269:Stochastic Processes
chemical_list: D004364:Pharmaceutical Preparations; D012709:Serum Albumin