Structure-based drug design (SBDD) is a common approach widely used in drug research and development (R&D). Utilizing a large number of available three-dimensional structures of therapeutic targets, SBDD helps researchers accelerate the discovery speed of potent drug-like molecules and reduce the cost and time dedicated to R&D, thanks to computational techniques such as molecular docking, pharmacophore modeling, and molecular dynamics simulations. In order to ensure the success of drug design, it is obligatory to validate SBDD procedures according to many validation aspects. In this chapter, three main factors involved in a validation process are discussed, including data sets, scoring functions (SFs), and metrics for validation. First, target validation is elaborated on different aspects. SFs are then highlighted in detail with two major approaches: classical and machine learning SFs. Last, metrics used for validating molecular docking, pharmacophore modeling, molecular dynamics simulations, homology modeling, and de novo drug design are described. Validation of algorithms is also emphasized in this chapter. Future research and outlook for validation aspects are also discussed.