## 4 Lead Identification and Optimization  
Drug design and discovery is a complex and time-intensive process, with most drug candidates failing during pre-clinical or clinical trials. QSAR approaches help accelerate this process by identifying potential hits from large compound libraries. These hits can then be acquired and tested experimentally for biological activity. Molecules with confirmed activity undergo further optimization to design promising drug candidates, reducing the need for extensive synthesis and experimental testing, thereby saving both time and costs. Figure 3 illustrates the concept behind rational drug design [168]. Certain regions of the chemical space are well-characterized based on existing experimental data. QSAR models built on these data can predict the activity of new compounds with potentially improved properties. Statistical Molecular Design (SMD) [38,76] organizes experiments within an information-gathering framework, enabling optimal predictions in specific regions of the chemical space. The goal is to create an accurate, valid model that effectively covers targeted chemical areas. Additionally, SMD can be used to relate the chemical features of compounds to their biological activity (BA) through regression modeling [38]. A regression equation similar to Equation (4) is constructed, where x\({}_{1}\) represents a chemical compound expressed by k predictor variables. These variables capture chemical features, including higher-order or cross-product terms that account for the non-liner nature of BA. The regression coefficient, b, corresponds to each k variable, while a represents the residual BA for each compound. The objective is to minimize the error in the regression coefficients b of the QSAR model and to enhance the likelihood of deriving a robust and predictive model [169]. When predicted compounds fall outside previously explored regions, the process is termed lead discovery, involving the identification of entirely new chemical entities with novel properties. If the predicted compounds lie within previously explored areas, the process is referred to as lead optimization, focusing on refining the molecular structure for improved performance. Additional experiments can be conducted to link distinct regions of the chemical space, enabling QSAR models to be updated with new data. This process expands the model's applicability, unifies previously separate regions, and enhances prediction accuracy for unexplored areas. The cycle continues as more regions are modeled and explored. In drug development, three key stages of molecules are recognized: hit, lead, and drug [170]. A hit is a molecule that contains a pharmacophore but has low affinity for its target receptor (~50 mmol/L). Through molecular design and optimization, the hit evolves into a lead, which exhibits improved activity and selectivity. Further optimization of the lead structure transforms it into a drug, characterized by optimal potency, selectivity, and pharmacokinetic properties. In this context, the drug must comply with [PERSON]'s Rule of Five [171], a set of guidelines for evaluating drug-likeness based on molecular properties such as molecular weight, lipophilicity, hydrogen bond donors, and acceptors. This rule helps ensure good absorption, distribution, metabolism, and excretion (ADME) profiles for potential drug candidates. The distinguishing features of these three molecular types are summarized in Table 3.  
QSAR is one of the LBDD methods used for lead identification and optimization. Table 4 presents additional techniques categorized based on whether the ligand structure or the target structure is known. Figure 3: The iterative process of rational drug design is depicted. **1**. The chemical space is represented by libraries of compounds (colored ovals) with known activities against a specific target, which are utilized for QSAR modeling and predictions. **2**. Newly predicted compounds with promising activity are depicted as red stars.