Drug Design and Discovery: Theory, Applications, Open Issues and Challenges

Cheminformatics has the major research factors that lead to the importance of similarity measurements for drugs and increase the chemical compound on databases search. The previous method is used for predicting the design of drugs and is comparatively efficient and less weak. Drug design is the process for finding new medications depended on the collected knowledge about a biological target. This chapter introduces several Metaheuristics Algorithms (MA), and Machine Learning (ML) techniques are used to design and discover new drug compounds in Cheminformatics. The balance of exploration and exploitation processes of MA’s are convenient for selecting the significant features as preprocessing to ML step for accomplishing high classification accuracy to the active chemical compound. In consequence, the results of the drug designed aim an optimal compound and enhanced the chemical descriptor.