In the pre-era of synthetic antibodies, pharmaceutical companies depended on finding novel drugs from medicinal plants and other traditional resources; while at present, technological advances in biology, computer, and robotics give researchers the ability to rewrite and edit DNA to synthesize extensive sets of drug candidates; these novel and improved candidates serve the basis for creating another library of drug candidates and so on until we find the right biomolecule for the disease of interest. All these technologies combined to synthesize therapeutic antibodies for many types of cancer, autoimmune diseases, and infectious diseases that can address diseases much more readily to very rapidly get therapeutics into patients so that we can potentially impact disease. The antibody mechanism is recognized, binds to disease cells, and pinpoints the immune system to attack those cells effectively. Now a day, they depend on a computational approach to guide and accelerate the process of antibodies engineering by a combination of a selection system and the use of high-throughput data acquisition and analysis to build and construct populations of next-generation antibodies that are thermo-stable, non-immunogenic as possible, and to be administered to many humans as possible. In this review, I will discuss the latest in silico methods for antibody engineering.