## 3 QSAR Today  
Today, we continue to apply the same principles that were used in the development of the first QSAR model. However, significant advancements in technology now allow for the generation of many new descriptors for chemical compounds and improved methods for data acquisition. QSAR models today can be broadly divided into two categories: \"global\" QSAR and \"local\" QSAR. Global QSAR models are built from datasets containing hundreds of thousands of chemical compounds, covering extensive regions of the chemical space. These models are typically available only for highly standardized cases, where consistent data are gathered across various compound types. Their primary aim is to provide predictions for virtually all types of compounds. These models are widely used in drug profiling, a standard practice in the early stages of drug discovery, where compounds are tested to identify unsuitable properties for early elimination. However, the high costs involved make testing every compound across all assays impractical. When sufficient data are available, QSAR models can replace some wet-laboratory tests. The pharmaceutical industry has made significant progress in this area, with models built on data from over 100,000 compounds that can perform as effectively as wet-laboratory tests. By leveraging these models, in silico profiling of all compounds can be conducted even before synthesis, enhancing efficiency in drug discovery. Examples include models predicting drug permeability in the Caco-2 cell system [56], mutagenicity in the Ames test [57], drug-plasma protein binding [58], logP [59], pKa [60], and water solubility of chemical compounds [61]. The development of global QSAR models heavily relies on experimental data that is consistently collected under the same laboratory conditions, using the same assay, and often by the same person or through automated processes. This could be seen as a disadvantage, as it may result in the generation of inaccurate models. In contrast, local QSAR models are developed using smaller datasets, sometimes comprising fewer than 100 compounds, and in some cases, only 10. In lead optimization projects, these models are frequently developed in an iterative process, where data from newly synthesized compounds are incorporated into the existing dataset [62]. Local QSAR studies often focus on closely related compounds, typically varying only in the substituents attached to a shared scaffold. These models can provide valuable insights to direct further synthesis aimed at improving the target activities of the compounds. One disadvantage of these models is that their applicability domain (see Section 3.9) is often restricted to a specific class of compounds with similar properties and close structural relationships. Additionally, there is a third type of QSAR model developed for classification purposes. Examples include models that broadly classify chemical compounds as active or inactive against specific target groups (e.g., kinase-active or -inactive [63], toxic or non-toxic [64], water-soluble or insoluble [65]. QSAR can be defined as a method for building computational or mathematical models that attempt to find a statistically significant correlation between chemical structure and biological activity using chemometric techniques [66; 67; 68]. In drug design, structural information is represented by molecular descriptors, while biological activity (BA) is expressed as a function of these descriptors (Equation (3)). \[\text{BA}=\text{f(molecular descriptors)} \tag{3}\]  
The QSAR model development process typically follows an iterative approach with the following steps:Selection of Chemical Compounds: identify relevant compounds for analysis. * 2 Assessment of Biological Activity: evaluate the biological activity of the selected compounds. * 3 Descriptors Calculation: calculate molecular descriptors. * 4 Data Matrix Setup: preprocess data into a uniform matrix. * 5 Data Partitioning: set aside a portion of the data for external validation.