Therefore, a descriptor that has a low correlation with the properties of the molecules to predict will give a model with a poor predictive performance.
In the case of molecules, the data used to train the machine learning algorithm are the descriptors and the properties of the molecules. The more correlated are the input part of the data with the output part of the data given to a machine learning algorithm, the better the predictive model will be. The descriptor is correlated with the properties of the molecules that are to be predicted. When selecting a descriptor to encode molecules, it is important to ensure that the three following criteria are met: What makes a descriptor appropriate for a machine learning problem? What are the different types of descriptors?.What makes a descriptor appropriate for a machine learning problem?.The purpose of this article is to give an overview of descriptors that can be used for molecules and to address three main questions: Source: DScribe: Library of descriptors for machine learning in materials science. Typical workflow to make machine learning predict a property from a molecule. Usually, a descriptor is a number, a vector or a matrix, but other data formats such as character strings are possible. The molecular structures are transformed into descriptors before being used to train the machine learning model, as depicted in the illustration below. In machine learning, when it comes to predicting the properties of molecules, it is necessary to convert the molecular structures into values that can be used by machine learning algorithms. Using machine learning to predict properties of molecules or to design molecules with desired properties plays an important role in the field of chemistry, toxicology and materials science.
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