Cancer affects one in three to four people globally, with over 20 million new cases and 10 million deaths annually, projected to rise to 35 million cases by 2050. Developing effective cancer treatments is crucial, but the drug discovery process is a highly complex and expensive endeavor, with success rates sitting well below 10% for oncologic therapies. More recently, there has been a growing interest in Artificial intelligence (AI) due to its potential to significantly enhance the success rates by processing large data sets, identifying patterns, and making autonomous decisions. The primary aim of this literature review is to examine the potential that state-of-the-art AI tech-nologies have to enhance and complement well-established research methods used in cancer drug development, such as QSAR, interactions prediction, and ADMET prediction, among others. The basic technical aspects of computational technologies are clarified, and key terms commonly asso-ciated with AI are defined. Current applications and case studies from academia and industry are presented to highlight AI’s potential to accelerate progress in cancer drug research. Challenges and disadvantages of AI are also acknowledged, and it is discussed that future research should focus on overcoming its limitations to maximize its impact in cancer treatment.

By Sorin-Ștefan Bobolea, Miruna-Ioana Hinoveanu, Andreea Dimitriu, Miruna-Andrada Brașoveanu, Cristian-Nicolae Iliescu, Cristina-Elena Dinu-Pîrvu, Mihaela Violeta Ghica, Valentina Anuța, Lăcrămioara Popa, Răzvan Mihai Prisada