Applications of Machine Learning in drug discovery and development

In the medical and pharmaceutical areas, discovering different drugs is too important. This process is long, complex, and depends on numerous factors. To avoid that, Machine Learning (ML) techniques are applied. These techniques are the practice of using algorithms to parse data, learn from it and then make a determination or a prediction about the future state of any new data sets. This practice consists of at least 80% data processing and cleaning and 20% algorithm application. Data types can include images, textual information, biometrics and other information from wearables, assay information, and high-dimensional omics data. In relation with the types of techniques that are used to apply ML can separate into two different. The first one is supervised learning, which is used to develop training models to predict future values of data categories or continuous variables. The second one is unsupervised learning, whereas this one is used for exploratory purposes to develop models that enable clustering of the data in a way that is not specified by the user. ML presents new opportunities for early target identification and validation that will help us to discover drugs sooner. Apart from being used for that, ML can predict biomarkers, which will be a way to better understand the mechanism of action of a drug and for instance to identify the right drug for the right patients. Moreover, it is used to analysis of digital pathology data in clinical trials. It can also predict disease-specific drug effects. To sum up, nowadays ML approaches are beginning to be commonly used in the various steps of the discovery and development of drugs by pharmaceutical companies