Drug discovery and development are long and arduous processes; recent figures point to 10 years and $2 billion USD to take a new chemical agent from discovery through to market. Moreover, though an approved blockbuster drug can be lucrative for the controlling pharmaceutical company, new therapeutic agents suffer from a 90% attrition during development, making the chances of success in the drug development process relatively low. Machine learning (ML) has re-emerged in the last several years as a powerful set of tools for unlocking value from large datasets. ML has shown great promise in improving efficiencies across numerous industries with high quality, vast, datasets. In an age of increasing access to highly curated rich sources of biological data, ML shows promise in reversing some of the negative trends shown in drug discovery and development. In this first part of our analysis of the application of ML to the drug discovery and development process, we discuss recent advances in the use of computational techniques in drug target discovery and lead molecule optimisation. We focus our analysis on oncology, though make reference to the wider field of human health and disease.
Part of the book: Artificial Intelligence in Oncology Drug Discovery and Development