Applying Machine Learning to Molecular Data

Molecular data contains information about the chemical structures of different molecules. Extrapolating this data for analysis helps us determine important properties of a chemical at a molecular level. These properties are key in the discovery of new therapies and drugs. For example, small molecules with specific atomic structures can combine with other molecules to form compounds. These compounds may then become compounds of interest or candidate compounds if they are beneficial in treating diseases. This is done by looking at the interaction of the compound with a protein or gene involved in a disease and understanding how this interaction affects the underlying protein or gene. If the interaction helps to modulate the function or behavior of the gene or the protein, we can say that we have found a biological target for our compound. This process, also known as target discovery, is a key step in discovering new drugs. In some cases, instead...