Recent Trends and Success Stories of Computational Techniques in Drug Discovery

Computer-aided drug design (CADD) is a critical component in modern drug discovery. CADD has emerged as a pivotal tool due to its ability to streamline the drug development process, reducing both the time and cost associated with traditional methods. CADD offers a sophisticated blend of computational power and biological insights, enabling researchers to navigate the complex molecular landscape of drug design efficiently. By facilitating the identification and optimization of potential drug candidates, CADD accelerates the drug discovery timeline and enhances the precision and effectiveness of therapeutic compounds. The recent surge in computational capabilities, coupled with advanced algorithms and machine learning techniques, has further elevated the importance of CADD. This chapter explores these advancements, illustrating how they have streamlined traditional methodologies and fostered innovation in drug discovery. The practical implications of these computational techniques are exemplified through detailed case studies of several recently approved drugs, such as lecanemab-irmb (for Alzheimer’s disease) and Elacestrant (to treat breast cancer). These examples underscore the efficacy of CADD in real-world scenarios, highlighting its contribution to the rapid development and approval of novel therapeutic agents.