Sar and Qsar in Environmental Research, Jul 1, 2012
Animals and humans are exposed to a wide array of xenobiotics and have developed complex enzymati... more Animals and humans are exposed to a wide array of xenobiotics and have developed complex enzymatic mechanisms to detoxify these chemicals. Detoxification pathways involve a number of biotransformations, such as oxidation, reduction, hydrolysis and conjugation reactions. The intermediate substances created during the detoxification process can be extremely toxic compared with the original toxins, hence metabolism should be accounted for when hazard effects of chemicals are assessed. Alternatively, metabolic transformations could detoxify chemicals that are toxic as parents. The aim of the present paper is to describe specificity of eukaryotic metabolism and its simulation and incorporation in models for predicting skin sensitization, mutagenicity, chromosomal aberration, micronuclei formation and estrogen receptor binding affinity implemented in the TIMES software platform. The current progress in model refinement, data used to parameterize models, logic of simulating metabolism, applicability domain and interpretation of predictions are discussed. Examples illustrating the model predictions are also provided.
A multi-dimensional formulation of the COmmon REactivity PAttern (COREPA) modeling approach has b... more A multi-dimensional formulation of the COmmon REactivity PAttern (COREPA) modeling approach has been used to investigate chemical binding to the human estrogen receptor (hER). A training set of 645 chemicals included 497 steroid and environmental chemicals (database of the Chemical Evaluation and Research Institute, Japan - CERI) and 148 chemicals to further explore hER-structure interactions (selected J. Katzenellenbogen references). Upgrades of modeling approaches were introduced for multivariate COREPA analysis, optimal conformational generation and description of the local hydrophobicity of chemicals. Analysis of reactivity patterns based on the distance between nucleophilic sites resulted in identification of distinct interaction types: a steroid-like A-B type described by frontier orbital energies and distance between nucleophilic sites with specific charge requirements; an A-C type where local hydrophobic effects are combined with electronic interactions to modulate binding; and mixed A-B-C (AD) type. Chemicals were grouped by type, then COREPA models were developed for within specific relative binding affinity ranges of >10%, 10 > RBA > or = 0.1%, and 0.1 > RBA > 0.0%. The derived models for each interaction type and affinity range combined specific prefiltering requirements (interatomic distances) and a COREPA classification node using no more than 2 discriminating parameters. The interaction types are becoming less distinct in the lowest activity range for each chemicals of each type; here, the modeling was performed within chemical classes (phenols, phthalates, etc.). The ultimate model was organized as a battery of local models associated to interaction type and mechanism.
The multiparameter formulation of the COmmon REactivity PAttern (COREPA) approach has been used t... more The multiparameter formulation of the COmmon REactivity PAttern (COREPA) approach has been used to describe the structural requirements for eliciting rat androgen receptor (AR) binding affinity, accounting for molecular flexibility. Chemical affinity for AR binding was related to the distances between nucleophilic sites and structural features describing electronic and hydrophobic interactions between the receptor and ligands. Categorical models were derived for each binding affinity range in terms of specific distances, local (maximal donor delocalizability associated with the oxygen atom of the A ring), global nucleophilicity (partial positive surface areas and energy of the highest occupied molecular orbital) and hydrophobicity (log Kow) of the molecules. An integral screening tool for predicting binding affinity to AR was constructed as a battery of models, each associated with different activity bins. The quality of the screening battery of models was assessed using a high value (0.9) of the Pearson contingency coefficient. The predictability of the model was assessed by testing the model performance on external validation sets. A recently developed technique for selection of potential androgenically active chemicals was used to test the performance of the model in its applicability domain. Some of the selected chemicals were tested for AR transcriptional activation. The experimental results confirmed the theoretical predictions.
Our previous work has investigated the utility of mutagenicity data in the development and applic... more Our previous work has investigated the utility of mutagenicity data in the development and application of Integrated Testing Strategies (ITS) for skin sensitization by focusing on the chemical mechanisms at play and substantiating these with experimental data where available. The hybrid expert system TIMES (Tissue Metabolism Simulator) was applied in the identification of the chemical mechanisms since it encodes a comprehensive set of established structure-activity relationships for both skin sensitization and mutagenicity. Based on the evaluation, the experimental determination of mutagenicity was thought to be potentially helpful in the evaluation of skin sensitization potential. This study has evaluated the dataset reported by Wolfreys and Basketter (Cutan. Ocul. Toxicol. 23 (2004), pp. 197-205). Upon an update of the experimental data, the original reported concordance of 68% was found to increase to 88%. There were several compounds that were 'outliers' in the two experimental evaluations which are discussed from a mechanistic basis. The discrepancies were found to be mainly associated with the differences between skin and liver metabolism. Mutagenicity information can play a significant role in evaluating sensitization potential as part of an ITS though careful attention needs to be made to ensure that any information is interpreted in the appropriate context.
2021 Big Data, Knowledge and Control Systems Engineering (BdKCSE), 2021
Choosing the best practice and associated components which will guarantee successful selection an... more Choosing the best practice and associated components which will guarantee successful selection and further career success of job applicants is a complex process. There are many components that can contribute in order to support the ultimate “right” decision of the recruiter. In the current paper, the role of emotional intelligence, company attractiveness, and the concept of “work-life balance” expressed by the job seeker is investigated. A generalized net model based on mentioned factors in order to support the selection of job applicants by human resources units is proposed. Modelling the evaluation process of job candidates can be used to find out the most appropriate candidates for achieving the goals of the organization.
Rapid and correct identification of endocrine-disrupting chemicals (EDCs) is an important issue i... more Rapid and correct identification of endocrine-disrupting chemicals (EDCs) is an important issue in environmental and human health risk assessment. Nowadays it is proved that structural diverse estrogen receptor agonists may induce cancer or endocrine function disruption. Because of the high cost and time-consuming nature of experimental tests, in silico methods are valuable alternative tools for identification of EDCs. However, the larger part of the available models could be applied successfully for prediction the binding potential toward the receptor without supporting information for possible functional activity (agonistic/antagonistic) effect. In this study, a large chemical dataset containing experimental data for estrogen binding and agonistic activity have been evaluated by application of profiling scheme for EDCs incorporated in non-commercial computational software tool.
During the development of pharmaceuticals, the mutagenic potential of a chemical is considered fo... more During the development of pharmaceuticals, the mutagenic potential of a chemical is considered for not only active pharmaceutical ingredients, but also for product-related substances such as metabolites and impurities. As indicated in ICH M7 draft guidance, in silico predictive tools including Quantitative Structure-Activity Relationships (QSARs) and expert analysis may be used as a computational assessment for bacterial mutagenicity for the qualification of impurities in pharmaceuticals. In the present study the implemented profiling schemes for mutagenicity prediction in non-commercial computational tool have been used for prediction of a set of so called hard to predict mutagenic compounds. The obtained results suggest that the system allows reliable mutagenic predictions when taking into account structural alerts with transparent reactivity mechanism along with adequate metabolism simulation. INTRODUCTION The genotoxic potential of a chemical is a highly important safety liabili...
2019 29th Annual Conference of the European Association for Education in Electrical and Information Engineering (EAEEIE), 2019
The current paper emphasizes on explaining how Virtual Reality (VR) technology works and why it i... more The current paper emphasizes on explaining how Virtual Reality (VR) technology works and why it is important to be implemented for education purposes. The main goal of VR in education is to make studying process more involving and effective. The essential distinction of VR from other computer technologies for displaying information is that there is a “feedback” corresponding to the user's actions, i.e., the virtual environment is changing appropriately to his/her reactions. In the paper, a comparison between the different implementations of VR is made and the key elements of a VR system are listed. Also, a case study is presented in the last part of the paper. It consists of a description of the process of choosing which VR technology is best used in different education scenarios and shows a specific example of how VR technology is implemented in the teaching process at “Prof. Dr. Assen Zlatarov” University - Burgas and thus making the education process of students, and especially those from medical and technical science departments, more complete and engaging.
2019 29th Annual Conference of the European Association for Education in Electrical and Information Engineering (EAEEIE), 2019
The use of digital technologies influences practically almost all aspects of our daily life. Comp... more The use of digital technologies influences practically almost all aspects of our daily life. Computer technologies have seen dramatic improvements, and laid the foundation for worldwide virtual reality (VR) development. The main goal of virtual reality is simulation of realistic human world. One of the key features of virtual reality is real-time interactivity, which means that the computer or the system has to be able to detect a user's input and modify the virtual world instantaneously. There are lots of educational difficulties related to understanding of complex objects like chemical compounds or parts of human body. A way to overcome them is the use of VR as appropriate technique which can improve the learning process to became more efficient. The aim of the paper is to present an approach for constructing molecules with different structural complexity. In addition, the development of VR models for human anatomy parts is also considered.
Efforts to develop new predictive methods to assess the likelihood of a xenobiotics being a hepat... more Efforts to develop new predictive methods to assess the likelihood of a xenobiotics being a hepatotoxicants have been challenging due to the increasing concerns of safety chemical assessments. A huge variety of chemicals exist in the environment without information for their ability to exert hepatotoxic effect. Based on the assumption that damages on DNA may lead to hepatotoxic outcome it is assumed that available models for identification of mutagens can be used for prediction of the effect. In the current study this assumption is investigated by in silico application of DNA reactivity profilers over dataset of chemicals with known hepatotoxic effect. The results suggest that the approach can be used for identification of hepatotoxins with special concern about the influence of their metabolism. The overall predictions show high statistical performance - 86% correct predictions for hepatotoxins. Regarding non hepatotoxins it was found that the role of metabolic detoxification shoul...
The primary testing strategy to identify chemical (hepato)carcinogens largely relies on the 2-yea... more The primary testing strategy to identify chemical (hepato)carcinogens largely relies on the 2-year rodent bioassay, which is time-consuming and labor-intensive. There is an increasing effort to develop alternative approaches to prioritize the chemicals for, supplement, or even replace the cancer bioassay. In silico approaches based on quantitative structure-activity relationships (QSAR) are rapid and inexpensive and thus have been investigated for such purposes. Since the correlation between carcinogenicity and Ames mutagenicty test results was found to be significant enough it is expected that models based on Ames data could be used successfully for identification of chemical carcinogens. In the current study the implemented profiler for DNA binding prediction in non-commercial software tool was used to predict the hepatocarcinogenic effect of 55 representative chemicals. The obtained results show that 73% of the hepatocarcinogens can be successfully identified as genotoxic carcino...
Risk assessment is a process that characterizes the magnitude of risk that chemicals pose to huma... more Risk assessment is a process that characterizes the magnitude of risk that chemicals pose to human and environmental health. Recent findings have suggested that certain chemicals have properties that make them harmful to human health or the environment which may require their restriction or even prohibition. One of the endpoints of major regulatory concern is mutagenicity defined as ability of chemicals to cause damages on DNA. Considering the large number of the registered industrial chemicals and taking in mind the cost and the time needed for testing it is evident that they could not be experimentally evaluated for possible mutagenic effect. However, the task could be accomplished by application of alternative in silico methods such quantitative structure–activity relationships (QSARs). In the present study the mutagenic potential of the high production volume HPV OECD chemicals (4843) have been computationally evaluated by DNA profiling schemes incorporated in the computer platf...
Recent advances in Farnesoid X Receptor (FXR) biology demonstrate that FXR may represent a valuab... more Recent advances in Farnesoid X Receptor (FXR) biology demonstrate that FXR may represent a valuable target for the identification of novel drugs to treat variety of biological disorders. However, for therapeutic purposes the advanced development of selective FXR modulators requires preliminary in silico evaluation of potential ligand candidates. In the current study the capabilities for structure-activity modeling incorporated in the non commercial computational tool have been employed for investigation the activating effect of various non-steroidal FXR ligands. A total of 97 molecules, representing three chemical classes – n-benzylamine, Diarylethene and Cycloalkyl amide have been analyzed. The ultimate models associated to each chemical class provide knowledge about molecular descriptors that may influence the activation of FXR.
Exposures to environmental concentrations of endocrine disrupting compounds are now a known threa... more Exposures to environmental concentrations of endocrine disrupting compounds are now a known threat to both human and ecological health. In the current study capabilities for structure-activity modeling incorporated in the platform QSAR Toolbox were employed for investigation the binding effect of set of chemicals toward glucocorticoid receptor. A total of 39 steroidal ligands were split in categories, representing strong, moderate and weak binders. As a result of comparative analysis a mechanistic reasonable molecular descriptors were found to be useful for prediction of strong and moderate receptor binders. It was found that the important feature related to strong binders is their surface which is assessed by specific range of van der Waals surface area. Regarding moderate binders it was found that the interaction can be assessed by using more specific descriptor van der Waals partial negative surface area. The obtained results suggest that identified descriptors and their specific...
Sar and Qsar in Environmental Research, Jul 1, 2012
Animals and humans are exposed to a wide array of xenobiotics and have developed complex enzymati... more Animals and humans are exposed to a wide array of xenobiotics and have developed complex enzymatic mechanisms to detoxify these chemicals. Detoxification pathways involve a number of biotransformations, such as oxidation, reduction, hydrolysis and conjugation reactions. The intermediate substances created during the detoxification process can be extremely toxic compared with the original toxins, hence metabolism should be accounted for when hazard effects of chemicals are assessed. Alternatively, metabolic transformations could detoxify chemicals that are toxic as parents. The aim of the present paper is to describe specificity of eukaryotic metabolism and its simulation and incorporation in models for predicting skin sensitization, mutagenicity, chromosomal aberration, micronuclei formation and estrogen receptor binding affinity implemented in the TIMES software platform. The current progress in model refinement, data used to parameterize models, logic of simulating metabolism, applicability domain and interpretation of predictions are discussed. Examples illustrating the model predictions are also provided.
A multi-dimensional formulation of the COmmon REactivity PAttern (COREPA) modeling approach has b... more A multi-dimensional formulation of the COmmon REactivity PAttern (COREPA) modeling approach has been used to investigate chemical binding to the human estrogen receptor (hER). A training set of 645 chemicals included 497 steroid and environmental chemicals (database of the Chemical Evaluation and Research Institute, Japan - CERI) and 148 chemicals to further explore hER-structure interactions (selected J. Katzenellenbogen references). Upgrades of modeling approaches were introduced for multivariate COREPA analysis, optimal conformational generation and description of the local hydrophobicity of chemicals. Analysis of reactivity patterns based on the distance between nucleophilic sites resulted in identification of distinct interaction types: a steroid-like A-B type described by frontier orbital energies and distance between nucleophilic sites with specific charge requirements; an A-C type where local hydrophobic effects are combined with electronic interactions to modulate binding; and mixed A-B-C (AD) type. Chemicals were grouped by type, then COREPA models were developed for within specific relative binding affinity ranges of >10%, 10 > RBA > or = 0.1%, and 0.1 > RBA > 0.0%. The derived models for each interaction type and affinity range combined specific prefiltering requirements (interatomic distances) and a COREPA classification node using no more than 2 discriminating parameters. The interaction types are becoming less distinct in the lowest activity range for each chemicals of each type; here, the modeling was performed within chemical classes (phenols, phthalates, etc.). The ultimate model was organized as a battery of local models associated to interaction type and mechanism.
The multiparameter formulation of the COmmon REactivity PAttern (COREPA) approach has been used t... more The multiparameter formulation of the COmmon REactivity PAttern (COREPA) approach has been used to describe the structural requirements for eliciting rat androgen receptor (AR) binding affinity, accounting for molecular flexibility. Chemical affinity for AR binding was related to the distances between nucleophilic sites and structural features describing electronic and hydrophobic interactions between the receptor and ligands. Categorical models were derived for each binding affinity range in terms of specific distances, local (maximal donor delocalizability associated with the oxygen atom of the A ring), global nucleophilicity (partial positive surface areas and energy of the highest occupied molecular orbital) and hydrophobicity (log Kow) of the molecules. An integral screening tool for predicting binding affinity to AR was constructed as a battery of models, each associated with different activity bins. The quality of the screening battery of models was assessed using a high value (0.9) of the Pearson contingency coefficient. The predictability of the model was assessed by testing the model performance on external validation sets. A recently developed technique for selection of potential androgenically active chemicals was used to test the performance of the model in its applicability domain. Some of the selected chemicals were tested for AR transcriptional activation. The experimental results confirmed the theoretical predictions.
Our previous work has investigated the utility of mutagenicity data in the development and applic... more Our previous work has investigated the utility of mutagenicity data in the development and application of Integrated Testing Strategies (ITS) for skin sensitization by focusing on the chemical mechanisms at play and substantiating these with experimental data where available. The hybrid expert system TIMES (Tissue Metabolism Simulator) was applied in the identification of the chemical mechanisms since it encodes a comprehensive set of established structure-activity relationships for both skin sensitization and mutagenicity. Based on the evaluation, the experimental determination of mutagenicity was thought to be potentially helpful in the evaluation of skin sensitization potential. This study has evaluated the dataset reported by Wolfreys and Basketter (Cutan. Ocul. Toxicol. 23 (2004), pp. 197-205). Upon an update of the experimental data, the original reported concordance of 68% was found to increase to 88%. There were several compounds that were 'outliers' in the two experimental evaluations which are discussed from a mechanistic basis. The discrepancies were found to be mainly associated with the differences between skin and liver metabolism. Mutagenicity information can play a significant role in evaluating sensitization potential as part of an ITS though careful attention needs to be made to ensure that any information is interpreted in the appropriate context.
2021 Big Data, Knowledge and Control Systems Engineering (BdKCSE), 2021
Choosing the best practice and associated components which will guarantee successful selection an... more Choosing the best practice and associated components which will guarantee successful selection and further career success of job applicants is a complex process. There are many components that can contribute in order to support the ultimate “right” decision of the recruiter. In the current paper, the role of emotional intelligence, company attractiveness, and the concept of “work-life balance” expressed by the job seeker is investigated. A generalized net model based on mentioned factors in order to support the selection of job applicants by human resources units is proposed. Modelling the evaluation process of job candidates can be used to find out the most appropriate candidates for achieving the goals of the organization.
Rapid and correct identification of endocrine-disrupting chemicals (EDCs) is an important issue i... more Rapid and correct identification of endocrine-disrupting chemicals (EDCs) is an important issue in environmental and human health risk assessment. Nowadays it is proved that structural diverse estrogen receptor agonists may induce cancer or endocrine function disruption. Because of the high cost and time-consuming nature of experimental tests, in silico methods are valuable alternative tools for identification of EDCs. However, the larger part of the available models could be applied successfully for prediction the binding potential toward the receptor without supporting information for possible functional activity (agonistic/antagonistic) effect. In this study, a large chemical dataset containing experimental data for estrogen binding and agonistic activity have been evaluated by application of profiling scheme for EDCs incorporated in non-commercial computational software tool.
During the development of pharmaceuticals, the mutagenic potential of a chemical is considered fo... more During the development of pharmaceuticals, the mutagenic potential of a chemical is considered for not only active pharmaceutical ingredients, but also for product-related substances such as metabolites and impurities. As indicated in ICH M7 draft guidance, in silico predictive tools including Quantitative Structure-Activity Relationships (QSARs) and expert analysis may be used as a computational assessment for bacterial mutagenicity for the qualification of impurities in pharmaceuticals. In the present study the implemented profiling schemes for mutagenicity prediction in non-commercial computational tool have been used for prediction of a set of so called hard to predict mutagenic compounds. The obtained results suggest that the system allows reliable mutagenic predictions when taking into account structural alerts with transparent reactivity mechanism along with adequate metabolism simulation. INTRODUCTION The genotoxic potential of a chemical is a highly important safety liabili...
2019 29th Annual Conference of the European Association for Education in Electrical and Information Engineering (EAEEIE), 2019
The current paper emphasizes on explaining how Virtual Reality (VR) technology works and why it i... more The current paper emphasizes on explaining how Virtual Reality (VR) technology works and why it is important to be implemented for education purposes. The main goal of VR in education is to make studying process more involving and effective. The essential distinction of VR from other computer technologies for displaying information is that there is a “feedback” corresponding to the user's actions, i.e., the virtual environment is changing appropriately to his/her reactions. In the paper, a comparison between the different implementations of VR is made and the key elements of a VR system are listed. Also, a case study is presented in the last part of the paper. It consists of a description of the process of choosing which VR technology is best used in different education scenarios and shows a specific example of how VR technology is implemented in the teaching process at “Prof. Dr. Assen Zlatarov” University - Burgas and thus making the education process of students, and especially those from medical and technical science departments, more complete and engaging.
2019 29th Annual Conference of the European Association for Education in Electrical and Information Engineering (EAEEIE), 2019
The use of digital technologies influences practically almost all aspects of our daily life. Comp... more The use of digital technologies influences practically almost all aspects of our daily life. Computer technologies have seen dramatic improvements, and laid the foundation for worldwide virtual reality (VR) development. The main goal of virtual reality is simulation of realistic human world. One of the key features of virtual reality is real-time interactivity, which means that the computer or the system has to be able to detect a user's input and modify the virtual world instantaneously. There are lots of educational difficulties related to understanding of complex objects like chemical compounds or parts of human body. A way to overcome them is the use of VR as appropriate technique which can improve the learning process to became more efficient. The aim of the paper is to present an approach for constructing molecules with different structural complexity. In addition, the development of VR models for human anatomy parts is also considered.
Efforts to develop new predictive methods to assess the likelihood of a xenobiotics being a hepat... more Efforts to develop new predictive methods to assess the likelihood of a xenobiotics being a hepatotoxicants have been challenging due to the increasing concerns of safety chemical assessments. A huge variety of chemicals exist in the environment without information for their ability to exert hepatotoxic effect. Based on the assumption that damages on DNA may lead to hepatotoxic outcome it is assumed that available models for identification of mutagens can be used for prediction of the effect. In the current study this assumption is investigated by in silico application of DNA reactivity profilers over dataset of chemicals with known hepatotoxic effect. The results suggest that the approach can be used for identification of hepatotoxins with special concern about the influence of their metabolism. The overall predictions show high statistical performance - 86% correct predictions for hepatotoxins. Regarding non hepatotoxins it was found that the role of metabolic detoxification shoul...
The primary testing strategy to identify chemical (hepato)carcinogens largely relies on the 2-yea... more The primary testing strategy to identify chemical (hepato)carcinogens largely relies on the 2-year rodent bioassay, which is time-consuming and labor-intensive. There is an increasing effort to develop alternative approaches to prioritize the chemicals for, supplement, or even replace the cancer bioassay. In silico approaches based on quantitative structure-activity relationships (QSAR) are rapid and inexpensive and thus have been investigated for such purposes. Since the correlation between carcinogenicity and Ames mutagenicty test results was found to be significant enough it is expected that models based on Ames data could be used successfully for identification of chemical carcinogens. In the current study the implemented profiler for DNA binding prediction in non-commercial software tool was used to predict the hepatocarcinogenic effect of 55 representative chemicals. The obtained results show that 73% of the hepatocarcinogens can be successfully identified as genotoxic carcino...
Risk assessment is a process that characterizes the magnitude of risk that chemicals pose to huma... more Risk assessment is a process that characterizes the magnitude of risk that chemicals pose to human and environmental health. Recent findings have suggested that certain chemicals have properties that make them harmful to human health or the environment which may require their restriction or even prohibition. One of the endpoints of major regulatory concern is mutagenicity defined as ability of chemicals to cause damages on DNA. Considering the large number of the registered industrial chemicals and taking in mind the cost and the time needed for testing it is evident that they could not be experimentally evaluated for possible mutagenic effect. However, the task could be accomplished by application of alternative in silico methods such quantitative structure–activity relationships (QSARs). In the present study the mutagenic potential of the high production volume HPV OECD chemicals (4843) have been computationally evaluated by DNA profiling schemes incorporated in the computer platf...
Recent advances in Farnesoid X Receptor (FXR) biology demonstrate that FXR may represent a valuab... more Recent advances in Farnesoid X Receptor (FXR) biology demonstrate that FXR may represent a valuable target for the identification of novel drugs to treat variety of biological disorders. However, for therapeutic purposes the advanced development of selective FXR modulators requires preliminary in silico evaluation of potential ligand candidates. In the current study the capabilities for structure-activity modeling incorporated in the non commercial computational tool have been employed for investigation the activating effect of various non-steroidal FXR ligands. A total of 97 molecules, representing three chemical classes – n-benzylamine, Diarylethene and Cycloalkyl amide have been analyzed. The ultimate models associated to each chemical class provide knowledge about molecular descriptors that may influence the activation of FXR.
Exposures to environmental concentrations of endocrine disrupting compounds are now a known threa... more Exposures to environmental concentrations of endocrine disrupting compounds are now a known threat to both human and ecological health. In the current study capabilities for structure-activity modeling incorporated in the platform QSAR Toolbox were employed for investigation the binding effect of set of chemicals toward glucocorticoid receptor. A total of 39 steroidal ligands were split in categories, representing strong, moderate and weak binders. As a result of comparative analysis a mechanistic reasonable molecular descriptors were found to be useful for prediction of strong and moderate receptor binders. It was found that the important feature related to strong binders is their surface which is assessed by specific range of van der Waals surface area. Regarding moderate binders it was found that the interaction can be assessed by using more specific descriptor van der Waals partial negative surface area. The obtained results suggest that identified descriptors and their specific...
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