An Overview of In Silico Drug Discovery with Emphasis on Cancer

Abstract

Each and every living individual is getting infected by other living or nonliving entities like virus, bacteria, poison, etc. from the very beginning of life formation. Infections have either huge agonistic or antagonistic effects on the infected individuals. Majority of the infections produce reactive oxygen species (ROS). Some effective remedy is needed to reduce the unfavorable change and get relief from the debilitating effects of ROS. Most of the cancer tumor cells are known to be rich in ROS. In the current era, the pharmaceutical industries are facing major challenges to the drug development process because it is crucial to the exploration of novel drugs. The drug development process requires a substantial amount of time, money, and several clinical and preclinical trial phases before launching a new drug in the market. Nowadays, the time and cost of the drug discovery process have been reduced dramatically because of the introduction of various computational methods and multidisciplinary approaches in this field. Computer-aided drug design (CADD) is one of the most promising methods used in the drug development process to minimize the cost and time and maximize the chances to bring in new drug candidates in a shorter time period. In this context, structure-based virtual screening (SBVS) and structure-based drug discovery (SBDD) are becoming essential in silico techniques that are extensively used for drug-designing purposes in the treatment of various types of cancers. Among the VS approaches, quantitative structure-activity relationship (QSAR) and quantitative structure-property relationship (QSPR) are two of the good prediction tools used in drug development purposes. Interestingly, a couple of drugs (like Gefitinib, Erlotinib, Sorafenib, Lapatinib, Abiraterone, and Crizotinib) to treat cancers were brought into the market, and all these drugs were obtained by computational techniques. Therefore, we tried to focus on the fundamental aspects of computational drug designing.KeywordsDrug designCADDSBDDSBVSQSARQSPRPharmacophores