This review discusses molecular docking and structure-based drug design (SBDD) strategies in drug discovery and medicinal chemistry. It highlights the importance of integrating computational and experimental methods to identify and develop novel compounds. Molecular docking explores ligand conformations within binding sites of macromolecular targets and estimates binding free energy. With the availability of various docking algorithms, understanding their advantages and limitations is crucial for effective strategies and results. The review examines current docking strategies, exploring advances and the role of integrating structure- and ligand-based methods. Structure-based drug design (SBDD) uses three-dimensional structural information from biological targets. Molecular docking, structure-based virtual screening (SBVS), and molecular dynamics (MD) are frequently used SBDD strategies due to their applications in analyzing molecular recognition events. Ligand-based drug design (LBDD) uses bioactive small-molecule libraries, while ligand-based virtual screening (LBVS), similarity searching, QSAR modeling, and pharmacophore generation are useful LBDD methods. SBDD and LBDD approaches are valuable in drug discovery, both in academia and industry, due to their versatility and synergy. The integration of these approaches has been successfully used in investigations of structural, chemical, and biological data. Molecular docking is a key method in SBDD, predicting ligand conformations within binding sites. It has evolved since the 1980s and is essential in drug discovery. Docking algorithms predict binding energetics and provide rankings of compounds based on binding affinity. Conformational search involves exploring a large conformational space and accurately predicting interaction energy. Systematic and stochastic methods are used in docking programs to explore conformational space. Scoring functions estimate binding energetics by evaluating physical-chemical phenomena. Scoring functions are categorized into force-field-based, empirical, and knowledge-based. Each has its strengths and limitations, and combining different scoring methods can improve accuracy. Covalent bonds in molecular docking are challenging, as current methods struggle to accurately model them. Quantum mechanical methods are more suitable for covalent bond formation. Molecular dynamics (MD) simulations are used to handle flexibility in binding sites, allowing the study of conformational changes. MD simulations use Newton’s equations of motion to examine ligand-receptor complex trajectories. Structural water poses challenges in docking and SBDD, as it can be displaced by ligands or considered part of the target structure. Strategies include explicitly including structural water in docking experiments or using free energy perturbation calculations to identify strongly-bound water molecules. Protein-protein interaction (PPI) inhibitors are attractive targets in drug discovery. Challenges include identifying and characterizing binding sites. Computational methods like Q-SiteFinder and ANCHOR are used to detect binding sites and evaluate their drugability. Virtual screening (VS) is a computational method for selecting promising compounds from chemical databases. It includes ligand-based (LBVS) and structure-based (SBVS) approaches. LBVS uses molecular descriptors, while SBThis review discusses molecular docking and structure-based drug design (SBDD) strategies in drug discovery and medicinal chemistry. It highlights the importance of integrating computational and experimental methods to identify and develop novel compounds. Molecular docking explores ligand conformations within binding sites of macromolecular targets and estimates binding free energy. With the availability of various docking algorithms, understanding their advantages and limitations is crucial for effective strategies and results. The review examines current docking strategies, exploring advances and the role of integrating structure- and ligand-based methods. Structure-based drug design (SBDD) uses three-dimensional structural information from biological targets. Molecular docking, structure-based virtual screening (SBVS), and molecular dynamics (MD) are frequently used SBDD strategies due to their applications in analyzing molecular recognition events. Ligand-based drug design (LBDD) uses bioactive small-molecule libraries, while ligand-based virtual screening (LBVS), similarity searching, QSAR modeling, and pharmacophore generation are useful LBDD methods. SBDD and LBDD approaches are valuable in drug discovery, both in academia and industry, due to their versatility and synergy. The integration of these approaches has been successfully used in investigations of structural, chemical, and biological data. Molecular docking is a key method in SBDD, predicting ligand conformations within binding sites. It has evolved since the 1980s and is essential in drug discovery. Docking algorithms predict binding energetics and provide rankings of compounds based on binding affinity. Conformational search involves exploring a large conformational space and accurately predicting interaction energy. Systematic and stochastic methods are used in docking programs to explore conformational space. Scoring functions estimate binding energetics by evaluating physical-chemical phenomena. Scoring functions are categorized into force-field-based, empirical, and knowledge-based. Each has its strengths and limitations, and combining different scoring methods can improve accuracy. Covalent bonds in molecular docking are challenging, as current methods struggle to accurately model them. Quantum mechanical methods are more suitable for covalent bond formation. Molecular dynamics (MD) simulations are used to handle flexibility in binding sites, allowing the study of conformational changes. MD simulations use Newton’s equations of motion to examine ligand-receptor complex trajectories. Structural water poses challenges in docking and SBDD, as it can be displaced by ligands or considered part of the target structure. Strategies include explicitly including structural water in docking experiments or using free energy perturbation calculations to identify strongly-bound water molecules. Protein-protein interaction (PPI) inhibitors are attractive targets in drug discovery. Challenges include identifying and characterizing binding sites. Computational methods like Q-SiteFinder and ANCHOR are used to detect binding sites and evaluate their drugability. Virtual screening (VS) is a computational method for selecting promising compounds from chemical databases. It includes ligand-based (LBVS) and structure-based (SBVS) approaches. LBVS uses molecular descriptors, while SB