Innovations in Anticancer Drug Prediction

This chapter investigates the application of supervised learning and deep learning methodologies in predicting the efficacy of anticancer drugs using IC50 values. It details the use of variational autoencoders and reinforcement learning to navigate the challenges of drug discovery, focusing on the integration of cancer cell line data and chemical representations. Furthermore, the discussion emphasizes the advantages of SMILES notation and the innovative approaches to explore vast chemical spaces for developing effective cancer treatments.