A (Comprehensive) Review of the Application of Quantitative Structure-Activity Relationship (QSAR) in the Prediction of New Compounds with Anti-Breast Cancer Activity  
[PERSON] and [PERSON]  
One of the most important goals in the pharmaceutical industry is to identify new chemical entities (NCEs) with the potential to become approved drugs. An established technology adopted by the pharmaceutical industry is the so-called wet-lab high-throughput screening (HTS), which helps achieve this goal. On the other hand, the high cost and low hit rate associated with high-throughput screening (HTS) have driven the development of computational alternatives, enabling cheaper and faster in silico screening. The concept of using computers to perform \"virtual\" screening before experimental testing is now referred to as \"high-throughput virtual screening\" (HTVS) [10; 11]. The completion of the Human Genome Project has unveiled a vast array of attractive druggable targets. Druggable targets are proteins capable of binding drug-like compounds with a certain binding affinity. In this context, \"druggability\" refers to the ability of a target protein to be therapeutically modulated by medicines. The \"druggable genome\" encompasses all genes encoding such proteins [12; 13]. Meanwhile, advances in structural biology techniques, including X-ray crystallography, nuclear magnetic resonance spectroscopy, and cryogenic electron microscopy (cryo-EM), have paved the way for the development of structure-based virtual screening (SBVS) [14; 15; 16; 17; 18]. When the exact structure of the biological target of interest is unknown, information from ligands can be extracted and leveraged along with previously obtained experimental data. This concept forms the basis of Quantitative Structure-Activity Relationship (QSAR) modeling, which is a key component of ligand-based drug design (LBDD) methods. LBDD, also known as pharmacophore-based design, enables the development of new active compounds by using the critical features of one or more molecules with the same mechanism of action, in other words, molecules that interact with the same target macromolecule [19]. The primary goal is to analyze input data to identify the pharmacophore responsible for the pharmacological activity. This identification is based on structural similarity, typically achieved using the alignment approach [20; 21]. The identified pharmacophore can be used to design new active molecules through various methods, including the design of structural analogs, 3D database searching, de novo design, and manual design. There are three major categories of ligand-based drug design (LBDD): QSAR, pharmacophore modeling, and similarity searching [22; 23]. Similarity searching is based on the similarity-property principle, which states that similar molecules are likely to exhibit similar biological properties and activities. Similarities can be quantified using molecular fingerprints, 3D shape descriptors, or physicochemical properties [22]. It is utilized to identify compounds with chemical or physicochemical properties similar to those of known active compounds [24; 25]. Additionally, it can be applied to expand structure-activity relationships within a given series and to prioritize compounds for synthesis or biological evaluation. Pharmacophore modeling identifies the spatial arrangement of key electronic and steric features of ligands, such as hydrogen bond donors/acceptors, aliphatic hydrophobic groups, aromatic rings, ionizable groups, and others, which are responsible for their activity, specifically their interactions with the targeted protein [26]. It is applied in virtual screening to identify novel hits, facilitate de novo design and support various applications such as lead optimization and multitarget drug design. QSAR is an effective method for analyzing and harnessing the relationship between chemical structures and their biological activities.