Computational Tools for Drug Discovery of Anticancer Therapy

The use of computersComputer in cancerCancer diagnosisDiagnosis is increasing continually powered by new modalities of imaging and advanced image processing methods. The use of bioinformatics, chemoinformatics, mathematical models and artificial intelligenceArtificial Intelligence is some of the critical fields but not limited to employing computersComputer for drug design. Medical imagining remains to be notable factor in the management of cancerCancer in patient which is currently at the rise with the clinicians due to an increase in cancerCancer incidences. This increase availability of data with the medical practitioners has the potential for using computersComputer or algorithmsAlgorithm for cancerCancer diagnosisDiagnosis for elevation of patient outcomes, reduction in toxicity and lowering the clinical burdens. CancerCancer data used for diagnosisDiagnosis and treatment will always be on the rise due to the advanced treatment options such as adaptive radiotherapy (RT) planning and image mining of quantitative nature. Machine learningMachine Learning particularly is used to build statistical models based on the past data sets collected having the self-learning knowledge from collected data sets for making future predictions. It has been shown that a trained deep neural network is able to detect malignant cancerCancer lesionsLesions form skin lesionsLesions photographs giving a rivalry to well-trained dermatologists. MammographicMammography lesionsLesions can also be detected by trained deep neural networks producing rivals to certified radiologist which shows the immense accuracy and profitable use of computerComputer-aided methods in cancerCancer diagnosisDiagnosis. Designing new anti-cancerCancer therapeutic or helping in the process of developing such therapies to decrease the failure rate and approval time is the most exciting potential of CAD in cancerCancer such as autoencoders. Precise clinical and preclinical, which lead increased likelihood for clinical success, may be achieved by using computational methods to predict correct mechanism of action. Effective combinations of drugs, which remain to be a combinatorial, can also be solved using AI-based methods as the number of anti-cancerCancer drugs is increasing day by day. An increase in the growth of complex experimental data sets has lead researchers today to welcome sophisticated algorithmic and computational tools to analyse the results and study basic biological questions including oncologyOncology. Recently, some of the prominent applications of CAD in cancerCancer diagnosisDiagnosis include tumour detection through medical image analysis, histopathological characterization and quantificationsQuantification, clinical diagnosisDiagnosis through computersComputer, selection of treatments, planning treatment and using multimodal clinical data, anti-cancerCancer drug development and surveillance of cancerCancer in populations. These steps collectively help to achieve precision oncologyOncology which may increase the efficiency of early screening and improve treatment levels. In this chapter, computerComputer methods employed in most of the critical fields of oncologyOncology such as radiation oncologyOncology, diagnosisDiagnosis and detection of cancerCancer and optimization of cancerCancer treatment are discussed.