Applying ML to molecular data

The discovery of a drug requires a trial-and-error method that involves scanning large libraries of small molecules and proteins using high-performance computing environments. ML can speed up the process by predicting a variety of properties of the molecules and proteins, such as their toxicity and binding affinity. This reduces the search space, thereby allowing scientists to speed up the process. In addition, drug manufacturers are looking at ways to customize drugs to an individual’s biomarkers (also known as precision medicine). ML can speed up these processes by correlating molecular properties to clinical outcomes, which helps in detecting biomarkers from a variety of datasets such as biomedical images, protein sequences, and clinical information. Let us look at a few applications of ML on molecular data.

Molecular reaction prediction

One of the most common applications of ML in drug discovery is the prediction of how two molecules...