Design, virtual screening and in silico QSPR modeling for the development of new thiosemicarbazone-based complexes

This modeling is performed on an experimental data set of complexes, where the metal ions of these complexes include transition ion metals and lanthanide ion metals. We use these models to develop a series of new thiosemicarbazone and their complexes; simultaneously, the complexes are worked out the stability constants from the novel models.
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Design, virtual screening and in silico QSPR modeling for the development of new thiosemicarbazone-based complexesCite this paper: Vietnam J. Chem., 2023, 61(S1), 8-16 Research articleDOI: 10.1002/vjch.202200203 Design, virtual screening and in silico QSPR modeling for the development of new thiosemicarbazone-based complexes Nguyen Minh Quang1, Huynh Ngoc Chau1, Tran Thai Hoa2*, Vu Thi Bao Ngoc4, Pham Van Tat3*1 Faculty of Chemical Engineering, Industrial University of Ho Chi Minh City, 12 Nguyen Van Bao, Go Vap, Ho Chi Minh City 70000, Viet Nam 2 Faculty of Chemistry, Hue University of Sciences, Hue University, 77 Nguyen Hue, Hue City 49100, Viet Nam 3 Department of Sciences and Journal Management, Hoa Sen University, 8 Nguyen Van Trang, Dist. 1, Ho Chi Minh City 70000, Viet Nam4 Faculty of Chemistry and Environment, University of Dalat, 01 Phu Dong Thien Vuong, Da Lat City 66000, Viet Nam Submitted November 16, 2022; Revised January 16, 2023; Accepted February 14, 2023Abstract Eighteen new thiosemicarbazone ligands and 30 new ligand-based complexes were developed from quantitativestructure-property relationships (QSPR) methods. Stability constants (log12) of complexes were calculated on QSPRmodels that were built by methods of multivariate linear regression (MLR) and artificial neural network (ANN). Sixdescriptors, including dipole, 5C, 4N, fw, xc3, and ka1 were discovered in the best QSPRMLR model with the goodstatistical criteria: R2train = 0.892, Q2CV = 0.845, and SE = 0.900. Besides, the ANN model with architecture I(6)-HL(3)-O(1) was built from the descriptors of the MLR model with excellent results as R2train = 0.958, Q2CV = 0.966, and Q2test =0.980. Also, the models were externally validated on the other experimental dataset. Consequently, the resulting QSPRmodels could be applied to develop new complexes for chemically related fields. Keywords. Machine learning, MLR, QSPR, stability constants log12, thiosemicarbazone.1. INTRODUCTION thiosemicarbazones leads to a variety of practical applications. Some have therapeutic activity used asRecently, a new theoretical method is emerging as a antivirals and antibiotics;[2] some have been used aspotential method for developing new derivatives. The anticancer drugs.[2] Besides, thiosemicarbazone hasmethod is based on the relationship between a certain also been used as a potential reagent in the formationquantitative value that characterizes the properties of of complexes,[3] which is understandable becausethe research object and its structure, and the method their structure contains nitrogen and sulfur donors, sois called the quantitative structure-properties the ability to form complexes is very easy. Moreover,relationship (QSPR). This method came from a these complexes also have properties and activitiesclassic study, namely the quantitative structure- similar to those of thiosemicarbazone mentionedactivity relationship (QSAR), activity was replaced above.[2,3] This creates excitement in the research andby the property.[1] This complex method covers steps new development of these derivatives. The easysuch as data mining, filtration, molecular or quantum complexation is a prerequisite for application inmechanism computation for optimizing structures, analytical chemistry by using the UV-Vis technique.and the construction of statistical and mathematically It is a cheap, quick, easy operation, and highlyrigorous regression equations.[1] Up to now, many effective technique. In fact, many works have beenworks have successfully used this method to develop published about thiosemicarbazones used as a reagentnew derivatives. in analytical techniques.[3] Meanwhile, thiosemicarbazone derivatives are Up to now, there are a few QSPR studies on thealso known as organosulfur and organonitrogen M:L complex of a thiosemicarbazone ligand (L) withcompounds and the structural peculiarity of a metal ion (M) in an aqueous solution.[4,5]8 Wiley Online Library © 2023 Vietnam Academy of Science and Technology, Hanoi & Wiley-VCH GmbH ...