title: Modeling the binding affinity of structurally diverse industrial chemicals to carbon using the artificial intelligence approaches.
abstract: Binding affinity of chemical to carbon is an important characteristic as it finds vast industrial applications. Experimental determination of the adsorption capacity of diverse chemicals onto carbon is both time and resource intensive, and development of computational approaches has widely been advocated. In this study, artificial intelligence (AI)-based ten different qualitative and quantitative structure-property relationship (QSPR) models (MLPN, RBFN, PNN/GRNN, CCN, SVM, GEP, GMDH, SDT, DTF, DTB) were established for the prediction of the adsorption capacity of structurally diverse chemicals to activated carbon following the OECD guidelines. Structural diversity of the chemicals and nonlinear dependence in the data were evaluated using the Tanimoto similarity index and Brock-Dechert-Scheinkman statistics. The generalization and prediction abilities of the constructed models were established through rigorous internal and external validation procedures performed employing a wide series of statistical checks. In complete dataset, the qualitative models rendered classification accuracies between 97.04 and 99.93%, while the quantitative models yielded correlation (R(2)) values of 0.877-0.977 between the measured and the predicted endpoint values. The quantitative prediction accuracies for the higher molecular weight (MW) compounds (class 4) were relatively better than those for the low MW compounds. Both in the qualitative and quantitative models, the Polarizability was the most influential descriptor. Structural alerts responsible for the extreme adsorption behavior of the compounds were identified. Higher number of carbon and presence of higher halogens in a molecule rendered higher binding affinity. Proposed QSPR models performed well and outperformed the previous reports. A relatively better performance of the ensemble learning models (DTF, DTB) may be attributed to the strengths of the bagging and boosting algorithms which enhance the predictive accuracies. The proposed AI models can be useful tools in screening the chemicals for their binding affinities toward carbon for their safe management.
authors.affiliation: Environmental Chemistry Division, CSIR-Indian Institute of Toxicology Research, Post Box 80, Mahatma Gandhi Marg, Lucknow, 226 001, India.
authors.forename: Shikha
authors.initials: S
authors.lastname: Gupta
authors.affiliation: KanbanSystems Pvt. Ltd., Laxmi Nagar, Delhi, 110092, India.
authors.forename: Nikita
authors.initials: N
authors.lastname: Basant
authors.affiliation: Environmental Chemistry Division, CSIR-Indian Institute of Toxicology Research, Post Box 80, Mahatma Gandhi Marg, Lucknow, 226 001, India.
authors.forename: Premanjali
authors.initials: P
authors.lastname: Rai
authors.affiliation: Environmental Chemistry Division, CSIR-Indian Institute of Toxicology Research, Post Box 80, Mahatma Gandhi Marg, Lucknow, 226 001, India. kpsingh_52@yahoo.com.
authors.forename: Kunwar P
authors.initials: KP
authors.lastname: Singh
mesh_terms: D000327:Adsorption; D000465:Algorithms; D001185:Artificial Intelligence; D002244:Carbon / Q000737:chemistry*; D002606:Charcoal / Q000737:chemistry*; D015386:Hazardous Substances / Q000737:chemistry*; D007220:Industrial Waste; D008962:Models, Theoretical; D021281:Quantitative Structure-Activity Relationship
chemical_list: D015386:Hazardous Substances; D007220:Industrial Waste; D002606:Charcoal; D002244:Carbon
keywords: Adsorption; Artificial intelligence; Binding affinity; Carbon; Industrial chemicals; QSPR models