Algorithms and health sciences: a winning combination
The development of machine learning and AI and their application to more and more uses raises important questions about the future of many industries.
Companies in the pharmaceutical industry work on the discovery of a molecule for many years and after lengthy clinical trials and waiting for regulatory approval, enjoy a few years of sales before the molecule becomes available as a generic drug. Generally, pharmaceutical companies test many molecules and end up developing only a few, those that prove to have the clearest applications, advantages in terms of the target market, etc., which means that the composition of this pipeline is highly strategic, so as to maintain a competitive portfolio of molecules in the sales phase.
The use of machine learning algorithms in medicine is not new, but as the entry barriers to their design, training and production are falling, we are seeing more and more examples of their use. Using algorithms, for example, to include a greater number of variables in diagnosing heart attacks has increased their reliability and speed and, therefore, the ability to treat them.
Using a machine learning algorithm to design molecules for treating complex pathologies can make many more molecules available in the initial stages of the pipeline, thus offering the company greater scope.
