Revolutionizing cancer drug discovery deep learning neural networks for accelerated development
Rupali Vyas, Rajendra Kumar Pandey, Huma Qamar Khan · 2024
Developing new cancer treatments is essential for improving patient outcomes. However, the traditional process is lengthy, meticulous, and resource-intensive. It involves a multi-step journey, from pinpointing a specific target molecule in cancer cells to rigorous clinical trials. Deep learning (DL), a branch of artificial intelligence (AI), has the ability to greatly speed up and improve the process of discovering new drugs, making it highly impactful in this industry. This chapter examines the incorporation of DL in many phases of drug development, such as target prediction, lead optimization, and the prediction of pharmacokinetics and toxicity. This integration aims to decrease the duration, expenses, and dependence on animal experimentation. DL, utilizing models like convolutional neural networks (CNNs) and generative adversarial networks (GANs), enhances the speed of identifying potential drug candidates and enhances the accuracy of predicting therapeutic effectiveness and safety. Furthermore, this chapter examines the ethical implications and the necessity for datasets of exceptional quality in order to comprehend the intricacies and potential constraints of implementing DL in pharmaceutical research. The chapter seeks to offer a thorough comprehension for both researchers and general readers on the influence of DL technologies in transforming drug development, resulting in expedited and more effective delivery of novel therapies.