Application of Computational Biology and Artificial Intelligence Technologies in Cancer Precision Drug Discovery.

Abeer Mohammed Al-Subaie,Nagasundaram Nagarajan,Nguyen Quoc Khanh Le,Balu Kamaraj,Hui-Yuan Yeh,Edward K. Y. Yapp

doi:10.1155/2019/8427042

Abstract

Artificial intelligence (AI) proves to have enormous potential in many areas of healthcare including research and chemical discoveries. Using large amounts of aggregated data, the AI can discover and learn further transforming these data into “usable” knowledge. Being well aware of this, the world's leading pharmaceutical companies have already begun to use artificial intelligence to improve their research regarding new drugs. The goal is to exploit modern computational biology and machine learning systems to predict the molecular behaviour and the likelihood of getting a useful drug, thus saving time and money on unnecessary tests. Clinical studies, electronic medical records, high-resolution medical images, and genomic profiles can be used as resources to aid drug development. Pharmaceutical and medical researchers have extensive data sets that can be analyzed by strong AI systems. This review focused on how computational biology and artificial intelligence technologies can be implemented by integrating the knowledge of cancer drugs, drug resistance, next-generation sequencing, genetic variants, and structural biology in the cancer precision drug discovery.

Highlights

Personalized or precision cancer therapy involves the identification of anticancer medicine for individual tumor molecular profiles, clinical features, and associated microenvironment of cancer patients [1, 2]
The genetic mechanism behind human disease can be understood by next-generation sequencing technology approaches such as whole exome sequencing (WES) [63, 64]. rough WES sequencing technology, the genetic variants in the human genome can be detected
Understanding the underlying mechanisms of the patient’s responses to cancer drugs and the unravelling of their genetic code would lead to the identification of new precision therapies that may improve the patient’s overall health and quality of life

Summary

Introduction

Personalized or precision cancer therapy involves the identification of anticancer medicine for individual tumor molecular profiles, clinical features, and associated microenvironment of cancer patients [1, 2]. The initial response to the chemotherapy is remarkable. Millions of cases regarding adverse drug resistance in cancer treatments are reported every year, which translates to a possibility of thousands of avoidable deaths. Such a dire situation calls for the designing of potential drugs. It is a time-consuming and complex process since each cancer patient

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