## 1 Introduction
This process includes many steps that include: the synthesis of tens of thousands of compounds, followed by biological evaluation, greenhouse evaluation,  
Figure 1: The main groups of natural coumarins. field experiments, toxicology, and environmental evaluation, approval of substances, and manufacture and commercialization of new plant protection products [42]. Computer-aided molecular design (CAMD) is a rational approach used for screening, optimization, and design of highly potent agents for plant protection. CAMD is a promising technique used in drug design and pesticide discovery. Due to advances in biochemistry and structural biology [43; 44; 45], it has become a promising rational approach in agrochemistry. During the past decade, many reports described the roles of in-silico approaches in novel molecule development. In silico techniques like quantitative structure-activity relationships (QSAR), pharmacophore, docking, and virtual screening play crucial roles in the design of \"better\" molecules that may later be synthesized and biologically assayed. QSAR techniques provide insight into the relationships between chemical structure and biological activity. They present an alternative pathway for the design and development of new molecules with improved activity. Using this relationship, the QSAR model predicts novel compounds' activity [46]. The QSAR approach aims to form a quantitative relationship between biological activity (or toxicity) and the structure of each chemical. The general purpose of the QSAR study is to predict the biological activity and physicochemical properties and rationalize the mechanisms of action within a series of chemicals [45]. Also, QSAR has the potential to estimate the risks of chemicals for the environment and human health, reducing time, monetary cost, and necessary animal testing [47]. In a mathematical sense, activity (toxicity) is a function of physiochemical and/or structural properties:  
Activity (toxicity) = \(f\) (physiochemical properties and/or structural properties). Information on chemical structure is encoded by molecular descriptors, numerical values obtained by various mathematical algorithms. In QSAR, activity data are correlated with molecular descriptors using a statistical approach. Various regression- and classification-based methods are used to derive a mathematical correlation between structural and activity/toxicity information. Regression-based methods are multiple linear regression (MLR) and partial least squares (PLS), Neural Networks (NN), and Support Vector Machine (SVM). Classification-based approaches are linear discriminant analysis (LDA) and cluster analysis [48; 49]. Recently, regression methods were replaced by various mathematical methods that improved QSAR studies' performance, such as Gene Expression Programming (GEP), Project Pursuit Regression (PPR), and Local Lazy Regression (LLR) [50]. The 3D-QSAR analysis permits correlations between a series of diverse molecular structures and their biological functions at a particular target. Introducing three-dimensional parameters allows the identification of the pharmacophoric arrangement of molecular features in space. This method takes into account the numerous field properties of each molecule, such as steric, lipophilic, and electrostatic interactions [51]. CoMFA is a widely used 3D QSAR method. [52]. The CoMFA model focuses on ligand properties favorable and unfavorable for receptor-ligand interactions. Molecular descriptors in CoMFA models are energies calculated by placing aligned ligands on the energy grid. Resultant energies correspond to ligand electrostatic and steric properties. CoMFA models are generated by correlating descriptors in 3D space with a biological response by PLS and validated by cross-validation. The CoMSIA method incorporates 3D information for the ligands by searching for conformation or spatial orientation of molecules capable of being modified into better specific ligands [53].