APPLYING VECTOR REPRESENTATIONS OF CHEMICAL FORMULAS TO MODELING PHARMACEUTICAL CHARACTERISTICS
In this work we propose the use of vector representations of chemical notation (specifically, SMILES) for preprocessing data for pharmaceutical characteristic prediction models. The pharmaceutical characteristics used in the work consist of ligandprotein bindings, as revealed by the assay dataset Tox21. Words2Vec, a preprocessing method from Natural Language Processing, is implemented for this task. A comparison of two different types of SMILES notation, kekule and canonical, is performed.