Inverse‐QSPR for de novo Design: A Review

Abstract

The use of computer tools to solve chemistry‐related problems has given rise to a large and increasing number of publications these last decades. This new field of science is now well recognized and labelled Chemoinformatics. Among all chemoinformatics techniques, the use of statistical based approaches for property predictions has been the subject of numerous research reflecting both new developments and many cases of applications. The so obtained predictive models relating a property to molecular features – descriptors – are gathered under the acronym QSPR, for Quantitative Structure Property Relationships. Apart from the obvious use of such models to predict property values for new compounds, their use to virtually synthesize new molecules – de novo design – is currently a high‐interest subject. Inverse‐QSPR (i‐QSPR) methods have hence been developed to accelerate the discovery of new materials that meet a set of specifications. In the proposed manuscript, we review existing i‐QSPR methodologies published in the open literature in a way to highlight developments, applications, improvements and limitations of each.

MolFinder: an evolutionary algorithm for the global optimization of molecular properties and the extensive exploration of chemical space using SMILES

“…Together with the VAE approach, RL–VAE models that improve the fitness of molecules produced by VAE via RL have been suggested [ 17 , 18 ]. More comprehensive reviews of various ML-based molecular generation and optimization methods are given in detail in recent papers [ 4 , 19 – 21 ].…”

On the second maximum Wiener polarity index of chemical trees of a fixed order

“…Graph theory has been (and is being) applied successfully in chemistry—see, for example, the recent papers [1–3]. The multidisciplinary field where graph theory and chemistry meet is known as the “chemical graph theory.” The present paper is concerned with the part of the chemical graph theory that involves topological indices [4,5].…”

SUSSOL—Using Artificial Intelligence for Greener Solvent Selection and Substitution

“…Such an approach proves useful in the screening and development of green solvents with respect to unconventional and novel compounds. Interestingly, the methodology can be inversed (iQSPR) to generate new structures based on a set of molecular properties [40,41]. Deep neural network supported QSPR modeling exploits the concept of automatic feature extraction.…”