Suman Chakravarti - Academia.edu (original) (raw)
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Papers by Suman Chakravarti
Predictive Toxicology, 2005
Pharmaceutical Research, 2014
Molecular Informatics, 2013
Journal of Chemical Information and Modeling, 2012
Fragment based expert system models of toxicological end points are primarily comprised of a set ... more Fragment based expert system models of toxicological end points are primarily comprised of a set of substructures that are statistically related to the toxic property in question. These special substructures are often referred to as toxicity alerts, toxicophores, or biophores. They are the main building blocks/classifying units of the model, and it is important to define the chemical structural space within which the alerts are expected to produce reliable predictions. Furthermore, defining an appropriate applicability domain is required as part of the OECD guidelines for the validation of quantitative structure-activity relationships (QSARs). In this respect, this paper describes a method to construct applicability domains for individual toxicity alerts that are part of the CASE Ultra expert system models. Defining applicability domain for individual alerts was necessary because each CASE Ultra model is comprised of multiple alerts, and different alerts of a model usually represent different toxicity mechanisms and cover different structural space; the use of an applicability domain for the overall model is often not adequate. The domain for each alert was constructed using a set of fragments that were found to be statistically related to the end point in question as opposed to using overall structural similarity or physicochemical properties. Use of the applicability domains in reducing false positive predictions is demonstrated. It is now possible to obtain ROC (receiver operating characteristic) profiles of CASE Ultra models by applying domain adherence cutoffs on the alerts identified in test chemicals. This helps in optimizing the performance of a model based on their true positive-false positive prediction trade-offs and reduce drastic effects on the predictive performance caused by the active/inactive ratio of the model's training set. None of the major currently available commercial expert systems for toxicity prediction offer the possibility to explore a model's full range of sensitivity-specificity spectrum, and therefore, the methodology developed in this study can be of benefit in improving the predictive ability of the alert based expert systems.
Chemosphere, 2003
A structurally and functionally diverse and cross-validated quantitative structure-activity knowl... more A structurally and functionally diverse and cross-validated quantitative structure-activity knowledge database generated by the MultiCASE expert system was used to screen 2526 high production volume chemicals (HPVCs) for their estrogen receptor binding activity. 73 HPVCs were found to contain structural features or biophores that have been documented as having the ability to bind to the estrogen receptor. Potential chemicals were ranked according to their quantitatively predicted ER binding potential and the details of the biophores found in them are discussed.
Chemosphere, 2003
The MultiCASE expert system was used to construct a quantitative structure-activity relationship ... more The MultiCASE expert system was used to construct a quantitative structure-activity relationship model to screen chemicals with estrogen receptor (ER) binding potential. Structures and ER binding data of 313 chemicals were used as inputs to train the expert system. The training data set covers inactive, weak as well as very powerful ER binders and represents a variety of chemical compounds. Substructural features associated with ER binding activity (biophores) and features that prevent receptor binding (biophobes) were identified. Although a single phenolic hydroxyl group was found to be the most important biophore responsible for the estrogenic activity of most of the chemicals, MultiCASE also identified other biophores and structural features that modulate the activity of the chemicals. Furthermore, the findings supported our previous hypothesis that a 6 A A distant descriptor may describe a ligand-binding site on an ER. Quantitative structure-activity relationship models for the chemicals associated with each biophore were constructed as part of the expert system and can be used to predict the activity of new chemicals. The model was cross validated via 10 Â 10%-off tests, giving an average concordance of 84.04%.
Journal of Chemical Information and Modeling, 2004
Computers in chemistry V 0380 ESP: A Method to Predict Toxicity and Pharmacological Properties of... more Computers in chemistry V 0380 ESP: A Method to Predict Toxicity and Pharmacological Properties of Chemicals Using Multiple MCASE Databases. -(KLOPMAN*, G.; CHAKRAVARTI, S. K.; ZHU, H.; IVANOV, J. M.; SAIAKHOV, R. D.; J. Chem. Inf. Comput. Sci. 44 (2004) 2, 704-715; Dep. Chem., Case West. Reserve Univ., Cleveland, OH 44106, USA; Eng.) -Lindner 25-237
Current Computer - Aided Drug Design, 2005
A new improved group contribution model that predicts the n-octanol/water partition coefficient (... more A new improved group contribution model that predicts the n-octanol/water partition coefficient (logP) is described. A combined parameter set that contains 153 basic parameters, 41 extended parameter and 14 molecular surface/property descriptors was generated from a training database of 8320 chemicals. The model achieved significant improvement after modifying the traditional group contribution equation by using a three dimensional steric hindrance modulator. The predictive ability of this model was accessed by calculating the logP values of a test set of 1667 ordinary organic chemicals and a set of 137 drug-like chemicals that were not included in the training database.
Bioorganic & Medicinal Chemistry, 2008
The primary functions of cancer chemotherapeutic agents are not only to inhibit the growth or kil... more The primary functions of cancer chemotherapeutic agents are not only to inhibit the growth or kill the cancer cells, but to do so without eliciting unreasonable cytotoxic effects on the healthy cells and to withstand the ability of the cancer cells to develop resistance against it. This has unfortunately been proven so far to be a very difficult objective. In this perspective, the ability of small molecules (anti-tumor agents) to 'see' different cell types differently can be a key attribute. Thus the term 'differential cytotoxicity' is normally used to describe the drug's specificity. In the present paper, we have quantified differential cytotoxicity from a study of the chemicals tested in the National Cancer Institute's Developmental Therapeutics Program. The MULTICASE (Multiple Computer Automated Structure Evaluation) methodology was used to discover statistically significant structural fragments (biophores) related to the differential cytotoxicity of the compounds. We found that even small structural features often become important in this regard which is evident from the biophores that were found in structurally diverse chemicals. By utilizing the difference between the raw and normalized differential cytotoxicity indices, we found that the a,b-unsaturated carbonyl group (O@CAC@CH 2 ) is the major biophore associated with compounds with essentially parallel concentration profiles in the cell lines in question. These compounds have high non-normalized differential cytotoxicity but considerably low normalized differential cytotoxocity. The models developed were cross validated for their predictive ability.
Current Computer - Aided Drug Design, 2009
Sar and Qsar in Environmental Research, 2003
ABSTRACT Computational screening is suggested as a way to set priorities for further testing of h... more ABSTRACT Computational screening is suggested as a way to set priorities for further testing of high production volume (HPV) chemicals for mutagenicity and other toxic endpoints. Results are presented for batch screening of 2484 HPV chemicals to predict their mutagenicity in Salmonella typhimurium (Ames test). The chemicals were tested against 15 databases for Salmonella strains TA100, TA1535, TA1537, TA97 and TA98, both with metabolic activation (using rat liver and hamster liver S9 mix test) and without metabolic activation. Of the 2484 chemicals, 1868 are predicted to be completely nonmutagenic in all of the 15 data modules and 39 chemicals were found to contain structural fragments outside the knowledge of the expert system and therefore suggested for further evaluation. The remaining 616 chemicals were found to contain different biophores (structural alerts) believed to be linked to mutagenicity. The chemicals were ranked indescending order according to their predicted mutagenic potential and the first 100 chemicals with highest mutagenicity scores are presented. The screening result offers hope that rapid and inexpensive computational methods can aid in prioritizing the testing of HPV chemicals, save time and animals and help to avoid needless expense.
Cheminform, 2004
Computers in chemistry V 0380 ESP: A Method to Predict Toxicity and Pharmacological Properties of... more Computers in chemistry V 0380 ESP: A Method to Predict Toxicity and Pharmacological Properties of Chemicals Using Multiple MCASE Databases. -(KLOPMAN*, G.; CHAKRAVARTI, S. K.; ZHU, H.; IVANOV, J. M.; SAIAKHOV, R. D.; J. Chem. Inf. Comput. Sci. 44 (2004) 2, 704-715; Dep. Chem., Case West. Reserve Univ., Cleveland, OH 44106, USA; Eng.) -Lindner 25-237
Predictive Toxicology, 2005
Pharmaceutical Research, 2014
Molecular Informatics, 2013
Journal of Chemical Information and Modeling, 2012
Fragment based expert system models of toxicological end points are primarily comprised of a set ... more Fragment based expert system models of toxicological end points are primarily comprised of a set of substructures that are statistically related to the toxic property in question. These special substructures are often referred to as toxicity alerts, toxicophores, or biophores. They are the main building blocks/classifying units of the model, and it is important to define the chemical structural space within which the alerts are expected to produce reliable predictions. Furthermore, defining an appropriate applicability domain is required as part of the OECD guidelines for the validation of quantitative structure-activity relationships (QSARs). In this respect, this paper describes a method to construct applicability domains for individual toxicity alerts that are part of the CASE Ultra expert system models. Defining applicability domain for individual alerts was necessary because each CASE Ultra model is comprised of multiple alerts, and different alerts of a model usually represent different toxicity mechanisms and cover different structural space; the use of an applicability domain for the overall model is often not adequate. The domain for each alert was constructed using a set of fragments that were found to be statistically related to the end point in question as opposed to using overall structural similarity or physicochemical properties. Use of the applicability domains in reducing false positive predictions is demonstrated. It is now possible to obtain ROC (receiver operating characteristic) profiles of CASE Ultra models by applying domain adherence cutoffs on the alerts identified in test chemicals. This helps in optimizing the performance of a model based on their true positive-false positive prediction trade-offs and reduce drastic effects on the predictive performance caused by the active/inactive ratio of the model's training set. None of the major currently available commercial expert systems for toxicity prediction offer the possibility to explore a model's full range of sensitivity-specificity spectrum, and therefore, the methodology developed in this study can be of benefit in improving the predictive ability of the alert based expert systems.
Chemosphere, 2003
A structurally and functionally diverse and cross-validated quantitative structure-activity knowl... more A structurally and functionally diverse and cross-validated quantitative structure-activity knowledge database generated by the MultiCASE expert system was used to screen 2526 high production volume chemicals (HPVCs) for their estrogen receptor binding activity. 73 HPVCs were found to contain structural features or biophores that have been documented as having the ability to bind to the estrogen receptor. Potential chemicals were ranked according to their quantitatively predicted ER binding potential and the details of the biophores found in them are discussed.
Chemosphere, 2003
The MultiCASE expert system was used to construct a quantitative structure-activity relationship ... more The MultiCASE expert system was used to construct a quantitative structure-activity relationship model to screen chemicals with estrogen receptor (ER) binding potential. Structures and ER binding data of 313 chemicals were used as inputs to train the expert system. The training data set covers inactive, weak as well as very powerful ER binders and represents a variety of chemical compounds. Substructural features associated with ER binding activity (biophores) and features that prevent receptor binding (biophobes) were identified. Although a single phenolic hydroxyl group was found to be the most important biophore responsible for the estrogenic activity of most of the chemicals, MultiCASE also identified other biophores and structural features that modulate the activity of the chemicals. Furthermore, the findings supported our previous hypothesis that a 6 A A distant descriptor may describe a ligand-binding site on an ER. Quantitative structure-activity relationship models for the chemicals associated with each biophore were constructed as part of the expert system and can be used to predict the activity of new chemicals. The model was cross validated via 10 Â 10%-off tests, giving an average concordance of 84.04%.
Journal of Chemical Information and Modeling, 2004
Computers in chemistry V 0380 ESP: A Method to Predict Toxicity and Pharmacological Properties of... more Computers in chemistry V 0380 ESP: A Method to Predict Toxicity and Pharmacological Properties of Chemicals Using Multiple MCASE Databases. -(KLOPMAN*, G.; CHAKRAVARTI, S. K.; ZHU, H.; IVANOV, J. M.; SAIAKHOV, R. D.; J. Chem. Inf. Comput. Sci. 44 (2004) 2, 704-715; Dep. Chem., Case West. Reserve Univ., Cleveland, OH 44106, USA; Eng.) -Lindner 25-237
Current Computer - Aided Drug Design, 2005
A new improved group contribution model that predicts the n-octanol/water partition coefficient (... more A new improved group contribution model that predicts the n-octanol/water partition coefficient (logP) is described. A combined parameter set that contains 153 basic parameters, 41 extended parameter and 14 molecular surface/property descriptors was generated from a training database of 8320 chemicals. The model achieved significant improvement after modifying the traditional group contribution equation by using a three dimensional steric hindrance modulator. The predictive ability of this model was accessed by calculating the logP values of a test set of 1667 ordinary organic chemicals and a set of 137 drug-like chemicals that were not included in the training database.
Bioorganic & Medicinal Chemistry, 2008
The primary functions of cancer chemotherapeutic agents are not only to inhibit the growth or kil... more The primary functions of cancer chemotherapeutic agents are not only to inhibit the growth or kill the cancer cells, but to do so without eliciting unreasonable cytotoxic effects on the healthy cells and to withstand the ability of the cancer cells to develop resistance against it. This has unfortunately been proven so far to be a very difficult objective. In this perspective, the ability of small molecules (anti-tumor agents) to 'see' different cell types differently can be a key attribute. Thus the term 'differential cytotoxicity' is normally used to describe the drug's specificity. In the present paper, we have quantified differential cytotoxicity from a study of the chemicals tested in the National Cancer Institute's Developmental Therapeutics Program. The MULTICASE (Multiple Computer Automated Structure Evaluation) methodology was used to discover statistically significant structural fragments (biophores) related to the differential cytotoxicity of the compounds. We found that even small structural features often become important in this regard which is evident from the biophores that were found in structurally diverse chemicals. By utilizing the difference between the raw and normalized differential cytotoxicity indices, we found that the a,b-unsaturated carbonyl group (O@CAC@CH 2 ) is the major biophore associated with compounds with essentially parallel concentration profiles in the cell lines in question. These compounds have high non-normalized differential cytotoxicity but considerably low normalized differential cytotoxocity. The models developed were cross validated for their predictive ability.
Current Computer - Aided Drug Design, 2009
Sar and Qsar in Environmental Research, 2003
ABSTRACT Computational screening is suggested as a way to set priorities for further testing of h... more ABSTRACT Computational screening is suggested as a way to set priorities for further testing of high production volume (HPV) chemicals for mutagenicity and other toxic endpoints. Results are presented for batch screening of 2484 HPV chemicals to predict their mutagenicity in Salmonella typhimurium (Ames test). The chemicals were tested against 15 databases for Salmonella strains TA100, TA1535, TA1537, TA97 and TA98, both with metabolic activation (using rat liver and hamster liver S9 mix test) and without metabolic activation. Of the 2484 chemicals, 1868 are predicted to be completely nonmutagenic in all of the 15 data modules and 39 chemicals were found to contain structural fragments outside the knowledge of the expert system and therefore suggested for further evaluation. The remaining 616 chemicals were found to contain different biophores (structural alerts) believed to be linked to mutagenicity. The chemicals were ranked indescending order according to their predicted mutagenic potential and the first 100 chemicals with highest mutagenicity scores are presented. The screening result offers hope that rapid and inexpensive computational methods can aid in prioritizing the testing of HPV chemicals, save time and animals and help to avoid needless expense.
Cheminform, 2004
Computers in chemistry V 0380 ESP: A Method to Predict Toxicity and Pharmacological Properties of... more Computers in chemistry V 0380 ESP: A Method to Predict Toxicity and Pharmacological Properties of Chemicals Using Multiple MCASE Databases. -(KLOPMAN*, G.; CHAKRAVARTI, S. K.; ZHU, H.; IVANOV, J. M.; SAIAKHOV, R. D.; J. Chem. Inf. Comput. Sci. 44 (2004) 2, 704-715; Dep. Chem., Case West. Reserve Univ., Cleveland, OH 44106, USA; Eng.) -Lindner 25-237