Approximating the Atomic Composition of Drug-like Molecules from Quantum Derived Properties: Inverse Design

This paper further explores an inverse design approach to molecular design consisting of using machine learning methods to approximate the atomic composition of molecules. In this generative approach the input is given by a set of desired properties of the molecule and the output is an approximation of the atomic composition in terms of its constituent chemical elements. This could serve as the starting region for further search in the huge space determined by the set of possible chemical compounds. The quantum mechanic's dataset QM9 is used in the study, composed of 133885 small organic molecules and 19 electronic properties. Different multi-target regression approaches were considered for predicting the atomic composition from the properties, including feature engineering techniques in an auto-machine learning framework. It was found that the data consist of an outer region pre-dominantly composed of scattered outliers, and an inner, core region that concentrates clustered inliner objects. The spatial structure exhibits a relationship with molecular weight. High-quality models were found that predict the atomic composition of the molecules from their electronic properties, as well as from a subset of only 52.6% size. Feature selection worked better than feature generation. The results validate the generative approach to inverse molecular design.