Use of Artificial Intelligence and Machine Learning for Discovery of Drugs for Neglected Tropical Diseases - Winkler, David A. (Frontiers in Chemistry, 2021)
Neglected tropical diseases continue to create high levels of morbidity and mortality in a sizeable fraction of the world’s population despite ongoing research into new treatments. Some of the most important technological developments that have accelerated drug discovery for diseases of affluent countries have not flowed down to neglected tropical disease drug discovery. Pharmaceutical development business models, cost of developing new drug treatments and subsequent costs to patients, and accessibility of technologies to scientists in many of the affected countries are some of the reasons for this low uptake and slow development relative to that for common drugs in developed countries. Computational methods are starting to make significant inroads into discovery of drugs for neglected tropical diseases because of the increasing availability of large databases that can be used to train ML models, increasing accuracy of these methods, low entry cost for researchers, and widespread availability of public domain machine learning codes. Here we summarize the application of artificial intelligence, largely the subset of methods called machine learning, to modelling and prediction of biological activities, and discovery of new drugs for neglected tropical diseases. We also discuss the pathways for development of machine learning methods in the short to medium term, foreshadow the use of other artificial intelligence methods for drug discovery, and discuss current roadblocks to and likely impacts of synergistic new technological developments, such as phenotypic assays and high throughput screening, on the impact of ML methods for neglected tropical diseases in the future.