Introduction: An Overview of AI in Oncology Drug Discovery and Development

Abstract

Artificial intelligence (AI) has been termed the machine for the fourth industrial revolution. One of the main challenges in drug discovery and development is the time and costs required to sustain the drug development pipeline. It is estimated to cost over 2.6 billion USD and take over a decade to develop cancer therapeutics. This is primarily due to the high numbers of candidate drugs failing at late drug development stages. Many sizable pharmaceutical and biotech companies have made considerable investments in AI. This is primarily due to recent advancements in AI, which have displayed the possibility of rapid low-cost drug discovery and development. This overview provides a general introduction to AI in drug discovery and development. This chapter will describe the conventional oncology drug discovery pipeline and its associated challenges. Fundamental AI concepts are also introduced, alongside historical and modern advancements within AI and drug discovery and development. Lastly, the future potential and challenges of AI in oncology are discussed.

Application of Artificial Intelligence in Discovery and Development of Anticancer and Antidiabetic Therapeutic Agents

“…This stage involves the quest for an improvement up-and-comer, which is an attainable medication such as little substance or natural treatment. The medication applicant will go through preclinical testing and, if effective, clinical testing [ 22 ].…”

“…Random forests, autoencoders, and convolutional neural networks are just a few examples of machine learning models. Individual models will be discussed in each of the following chapters as needed [ 22 ].…”

Experimental Models of Hepatocellular Carcinoma—A Preclinical Perspective

“…In particular, AI algorithms hold tremendous potential within the field of oncology, paving the pathway that leads to the identification of genetic mutations at an early stage, and they allow the diagnosis and the efficient evaluation of cancer prognosis, progression, treatment, and susceptibility to recurrence [174,177]. Furthermore, AI methods represent a rapid and low-cost alternative to the current oncology drug discovery, which is, in essence, expensive and time-consuming as a consequence of the multitude of candidates failing during the late development stages [180].…”

İlaç Tasariminda Yapay Zekâ Uygulamalari

“…Problem bir aralık içindeki sayısal bir değeri öngörmeyi içerdiğinde regresyon modelleri kullanılmaktadır. Rastgele Orman, Otomatik Kodlayıcılar (AutoEncoder (AE)) ve Evrişimli Sinir Ağları (CNN) gibi çeşitli farklı türde makine öğrenimi uygulamaları bulunmaktadır [16].…”