In normal pharmacological effect screening protocols, natural substances that were thoroughly diluted and without their active components separated are employed. Over the last two decades, strong active isomeric compounds have been identified and isolated. The notion of multi-target treatment was novel in mid-2000s, but it will be one of the most significant advancements in drug development by 2021. Instead, then relying on organically generated mixtures, researchers are looking at target-based drug development based on precisely specified fragments for effective organic anticancer medicines. This study emphasizes the breakdown of structures utilizing computer aid or fragments, as well as a process for applying natural anticancer medications. The use of computer-assisted drug development (CADD) is becoming more frequent. The major areas of this study were the development of computer-aided pharmaceuticals and anticancer agents. The discovery of effective all-natural cancer treatments will be accelerated. Multitarget drug development methodologies have enabled the development of cancer medicines with less negative side effects. Cutting-edge analytical and bioinformatics approaches, particularly machine learning, will be employed to uncover natural anticancer therapies.
Keywords: Cancer, Machine learning, Artificial intelligence