Sitarama Gunturi - Academia.edu (original) (raw)

Uploads

Papers by Sitarama Gunturi

Research paper thumbnail of A Classifier for the SNP-Based Inference of Ancestry

Journal of forensic …, 2003

Human identity testing relies on the segregation of polymorphic alleles into unique combinations ... more Human identity testing relies on the segregation of polymorphic alleles into unique combinations in individual human beings. Because a balance of dispersive and systematic forces has shaped the genetic structure of modern-day humanity, most human poly-morphisms are ...

Research paper thumbnail of Prediction of skin sensitization potential using D-optimal design and GA-kNN classification methods

Sar and Qsar in Environmental Research, 2010

Modelling of skin sensitization data of 255 diverse compounds and 450 calculated descriptors was ... more Modelling of skin sensitization data of 255 diverse compounds and 450 calculated descriptors was performed to develop global predictive classification models that are applicable to whole chemical space. With this aim, we employed two automated procedures, (a) D-optimal design to select optimal members of the training and test sets and (b) k-Nearest Neighbour classification (kNN) method along with Genetic Algorithms (GA-kNN Classification) to select significant and independent descriptors in order to build the models. This methodology helped us to derive multiple models, M1-M5, that are stable and robust. The best among them, model M1 (CCR(train) = 84.3%, CCR(test) = 87.2% and CCR(ext) = 80.4%), is based on six neighbours and nine descriptors and further suggests that: (a) it is stable and robust and performs better than the reported models in literature, and (b) the combination of D-optimal design and GA-kNN classification approach is a very promising approach. Consensus prediction based on the models M1-M5 improved the CCR of training, test and external validation datasets by 3.8%, 4.45% and 3.85%, respectively, over M1. From the analysis of the physical meaning of the selected descriptors, it is inferred that the skin sensitization potential of small organic compounds can be accurately predicted using calculated descriptors that code for the following fundamental properties: (i) lipophilicity, (ii) atomic polarizability, (iii) shape, (iii) electrostatic interactions, and (iv) chemical reactivity.

Research paper thumbnail of In silico ADME modelling: prediction models for blood–brain barrier permeation using a systematic variable selection method

Bioorganic & Medicinal Chemistry, 2005

Quantitative Structure-Property Relationship models (QSPR) based on in vivo blood-brain permeatio... more Quantitative Structure-Property Relationship models (QSPR) based on in vivo blood-brain permeation data (logBB) of 88 diverse compounds, 324 descriptors and a systematic variable selection method, namely ÔVariable Selection and Modeling method based on the prediction (VSMP)Õ, are reported. Of all the models developed using VSMP, the best three-descriptors model is based on Atomic type E-state index (SsssN), AlogP98 and Van der WaalÕs surface area (r = 0.8425, q = 0.8239, F = 68.49 and SE = 0.4165); the best four-descriptors model is based on Kappa shape index of order 1, Atomic type E-state index (SsssN), Atomic level based AI topological descriptor (AIssssC) and AlogP98 (r = 0.8638, q = 0.8472, F = 60.982 and SE = 0.3919). The performance of the models on three test sets taken from the literature is illustrated and compared with the results from other reported computational approaches. Test set III constitutes 91 compounds from the literature with known qualitative BBB indication and is used for virtual screening studies. The success rate of the reported models is 82% in the case of BBB+ compounds and a similar success rate is observed with BBBÀ compounds. Finally, as the models reported herein are based on computed properties, they appear as a valuable tool in virtual screening, where selection and prioritization of candidates is required.

Research paper thumbnail of In silico ADME modelling 2: Computational models to predict human serum albumin binding affinity using ant colony systems

Bioorganic & Medicinal Chemistry, 2006

Modelling of in vitro human serum albumin (HSA) binding data of 94 diverse drugs and drug-like co... more 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 structureproperty 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.

Research paper thumbnail of A Classifier for the SNP-Based Inference of Ancestry

Journal of forensic …, 2003

Human identity testing relies on the segregation of polymorphic alleles into unique combinations ... more Human identity testing relies on the segregation of polymorphic alleles into unique combinations in individual human beings. Because a balance of dispersive and systematic forces has shaped the genetic structure of modern-day humanity, most human poly-morphisms are ...

Research paper thumbnail of Prediction of skin sensitization potential using D-optimal design and GA-kNN classification methods

Sar and Qsar in Environmental Research, 2010

Modelling of skin sensitization data of 255 diverse compounds and 450 calculated descriptors was ... more Modelling of skin sensitization data of 255 diverse compounds and 450 calculated descriptors was performed to develop global predictive classification models that are applicable to whole chemical space. With this aim, we employed two automated procedures, (a) D-optimal design to select optimal members of the training and test sets and (b) k-Nearest Neighbour classification (kNN) method along with Genetic Algorithms (GA-kNN Classification) to select significant and independent descriptors in order to build the models. This methodology helped us to derive multiple models, M1-M5, that are stable and robust. The best among them, model M1 (CCR(train) = 84.3%, CCR(test) = 87.2% and CCR(ext) = 80.4%), is based on six neighbours and nine descriptors and further suggests that: (a) it is stable and robust and performs better than the reported models in literature, and (b) the combination of D-optimal design and GA-kNN classification approach is a very promising approach. Consensus prediction based on the models M1-M5 improved the CCR of training, test and external validation datasets by 3.8%, 4.45% and 3.85%, respectively, over M1. From the analysis of the physical meaning of the selected descriptors, it is inferred that the skin sensitization potential of small organic compounds can be accurately predicted using calculated descriptors that code for the following fundamental properties: (i) lipophilicity, (ii) atomic polarizability, (iii) shape, (iii) electrostatic interactions, and (iv) chemical reactivity.

Research paper thumbnail of In silico ADME modelling: prediction models for blood–brain barrier permeation using a systematic variable selection method

Bioorganic & Medicinal Chemistry, 2005

Quantitative Structure-Property Relationship models (QSPR) based on in vivo blood-brain permeatio... more Quantitative Structure-Property Relationship models (QSPR) based on in vivo blood-brain permeation data (logBB) of 88 diverse compounds, 324 descriptors and a systematic variable selection method, namely ÔVariable Selection and Modeling method based on the prediction (VSMP)Õ, are reported. Of all the models developed using VSMP, the best three-descriptors model is based on Atomic type E-state index (SsssN), AlogP98 and Van der WaalÕs surface area (r = 0.8425, q = 0.8239, F = 68.49 and SE = 0.4165); the best four-descriptors model is based on Kappa shape index of order 1, Atomic type E-state index (SsssN), Atomic level based AI topological descriptor (AIssssC) and AlogP98 (r = 0.8638, q = 0.8472, F = 60.982 and SE = 0.3919). The performance of the models on three test sets taken from the literature is illustrated and compared with the results from other reported computational approaches. Test set III constitutes 91 compounds from the literature with known qualitative BBB indication and is used for virtual screening studies. The success rate of the reported models is 82% in the case of BBB+ compounds and a similar success rate is observed with BBBÀ compounds. Finally, as the models reported herein are based on computed properties, they appear as a valuable tool in virtual screening, where selection and prioritization of candidates is required.

Research paper thumbnail of In silico ADME modelling 2: Computational models to predict human serum albumin binding affinity using ant colony systems

Bioorganic & Medicinal Chemistry, 2006

Modelling of in vitro human serum albumin (HSA) binding data of 94 diverse drugs and drug-like co... more 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 structureproperty 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.