DeepPharma: Revolutionizing Drug Discovery through Kernelled Drug Prediction Based on Similarity Matrix

Cancer poses a formidable and diverse challenge, as patients with the same type of cancer exhibit varying responses to targeted therapy. The need to develop new drugs and tailor treatment plans for individual patients becomes imperative. However, the high costs and extended duration of clinical trials present significant barriers to the advancement of cancer treatments. To overcome these hurdles, there is an urgent demand for a predictive methodology that can anticipate medication responses and facilitate personalized treatment strategies. To address this requirement, we propose an innovative approach called “Kernelled Drug Prediction Based on Similarity Matrix.” This study focuses on utilizing this method to accurately predict drug responses and customize therapies for specific individuals. To assess the robustness of our proposed method, we employ a 10-fold cross-validation technique. In this process, the dataset is divided into ten equal parts, with one part used for testing and the remaining nine parts for training during each iteration. For validation, we utilize the Cancer Cell Line Encyclopedia (CCLE) dataset, which includes activity area as drug response parameters, with a dimensionality (k) set as 10 for the CCLE dataset. Our study aims to offer a practical and efficient solution to predict drug responses and pave the way for personalized cancer treatment strategies. Through this research, we contribute to the advancement of cancer treatment by providing a more streamlined and accurate means of predicting drug responses, potentially leading to improved patient outcomes and optimized therapeutic interventions.