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

Abstract

Eighteen new thiosemicarbazone ligands and 30 new ligand‐based complexes were developed from quantitative structure‐property relationships (QSPR) methods. Stability constants (logβ12) of complexes were calculated on QSPR models that were built by methods of multivariate linear regression (MLR) and artificial neural network (ANN). Six descriptors, including dipole, 5C, 4N, fw, xc3, and ka1 were discovered in the best QSPRMLR model with the good statistical 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 QSPR models could be applied to develop new complexes for chemically related fields.

MDs-NP: a property prediction model construction procedure for naphtha based on molecular dynamics simulation

“…QSPR is a method for building machine learning models to mine the relationships between the properties and structure of a molecule. As a new field in the natural science, researches on QSPR include predicting the physicochemical behaviors and properties (boiling point [13], toxicity [14], heat capacity [15], sublimation enthalpy [16]) of a molecule based on structural information, identifying potential candidates with specified properties or functionalities [17]. Generally, a QSPR model uses numerical molecular structural characteristics as input, achieving efficient as well as accurate calculations and predictions of molecular properties based on various machine learning algorithms [17][18][19].…”