## 3 QSAR Today
### Data Partitioning: Set Aside a Portion of the Data for External Validation  
A QSAR model is valuable if it can predict the activity of new molecules. To assess the quality of a model, scientists typically divide the data into training and test datasets. The training set is used to build the QSAR model, while the test set evaluates its predictive accuracy. Generally, around 15-30% of the data are set aside for external prediction, though this proportion depends on the homogeneity and size of the dataset. Various methods are used for data splitting. These methods are based either on analyzing the similarity of compounds (X response) using techniques like PCA [39; 40; 115] or cluster analysis or on activity sampling (Y response) through methods such as random selection, Statistical Molecular Design (SMDs) [38; 76], k-means clustering [116; 117], Kennard-Stone selection [118], and [PERSON]'s self-organizing map selection [119; 120]. Feature Selection and Model Building: Use Chemometric Methods to Select Relevant Features and Develop a QSAR Model  
Chemometrics is a discipline that applies mathematical and statistical methods to analyze chemical data. Since the relationships between the structural properties of chemical compounds and their biological activities are often non-linear, modeling can be applied to both linear and non-linear properties. Chemometric methods are generally grouped into regression and classification techniques based on the type of predictions they make. Regression methods provide quantitative predictions of the Y variable(s), while classification methods categorize compounds, for example, as active or non-active. A commonly used regression method is Multiple Linear Regression (MLR) [121], where the dependent variable (Y), such as biological activity or physicochemical properties, is predicted utilizing more than one independent variable (X), typically molecular descriptors (Equation (4)). \[\mathrm{y}=\mathrm{a}+\mathrm{b}_{1}\mathrm{x}_{1}+\mathrm{b}_{2}\mathrm{x}_{ 2}+\ldots \tag{4}\]  
where y is the dependent variable--biological activity or physicochemical property, x\({}_{i}\) is an independent variable accounting for molecular descriptors, and b\({}_{i}\) is the corresponding regression coefficient. When working with descriptors, the next critical step is determining which of these parameters are relevant to the biological activity and should be included in the regression equation. Two essential points must be considered when using Multiple Linear Regression (MLR) [121] for analysis: (1) compound-to-descriptor ratio--the ratio of compounds to descriptors should be at least 5:1--and (2) multicollinearity--high correlations among descriptors can cause spurious solutions. This issue can be addressed by performing feature selection to reduce the number of descriptors or by applying multivariate techniques such as Partial Least Squares (PLS) [122; 123] or Principal Component Regression (PCR) [39; 124]. Classification or clustering involves grouping similar data points to maximize similarity within groups while ensuring dissimilarity between groups. Commonly used methods include hierarchical clustering and k-means clustering. Hierarchical clustering uses distances between objects, typically measured by Euclidean distances, to form clusters based on their dissimilarities [125]. K-means clustering is a non-hierarchical method that partitions data points into clusters based on k-centroids, determined by the mean values of the objects in each cluster [116; 117]. Other widely used classification techniques include linear discriminant analysis [126; 127] and logistic regression [128]. Machine learning (ML) comprises advanced mathematical and statistical techniques for analyzing complex and large-scale datasets, including big data. ML strategies are broadly categorized into unsupervised learning and supervised learning (Table 1) [129]. In the unsupervised learning, the system identifies patterns or structures within data without labeled outputs, simplifying data interpretation.