Exhaustive Structure Generation for Inverse‐QSPR/QSAR

Abstract

Chemical structure generation based on quantitative structure property relationship (QSPR) or quantitative structure activity relationship (QSAR) models is one of the central themes in the field of computer-aided molecular design. The objective of structure generation is to find promising molecules, which according to statistical models, are considered to have desired properties. In this paper, a new method is proposed for the exhaustive generation of chemical structures based on inverse-QSPR/QSAR. In this method, QSPR/QSAR models are constructed by multiple linear regression method, and then the conditional distribution of explanatory variables given the desired properties is estimated by inverse analysis of the models using the framework of a linear Gaussian model. Finally, chemical structures are exhaustively generated by a sophisticated algorithm that is based on a canonical construction path method. The usefulness of the proposed method is demonstrated using a dataset of the boiling points of acyclic hydrocarbons containing up to 12 carbon atoms. The QSPR model was constructed with 600 hydrocarbons and their boiling points. Using the proposed method, chemical structures which had boiling points of 100, 150, or 200 °C were exhaustively generated.

Bayesian molecular design with a chemical language model

“…The objective is to minimize the difference between given desired properties and those attained by the designed molecules. Some previous studies tackled this issue with genetic algorithms (GAs) [2, 4–7, 10–13] and molecular graph enumeration [8, 9, 14]. Graph enumeration is generally less effective due to the combinatorial complexity of the design space.…”

Atom Coloring for Chemical Interpretation and De Novo Design for Molecular Design

“…In this case, an inverse QSAR approach is attractive to design practical chemical structures. Inverse QSAR is a relatively new concept that chemical structures having high biological activities are computationally generated using a structure generator [11,12]. EA-Inventor (Evolutionary Algorithm-Inventor) was used as structure generator in our study.…”

“…The selected 15 descriptors have the ambiguous physicochemical meanings and their chemical interpretations are far to be ease. In this situation, an inverse QSAR becomes a powerful approach [11,12]. …”

“…Structure generator intended for de novo design generates any chemical structures that are expected to have desired biological activities. This study has been known as inverse QSAR [11,12]. In our approach, EA-Inventor (Evolutional Algorithm-Inventor) was used as structure generator.…”

Finding Chemical Structures Corresponding to a Set of Coordinates in Chemical Descriptor Space

“…[17] The group of Akutsu proved that it is strongly NP-hard even using planar graphs with bounded degrees, supposing descriptors are a feature vector defined as the frequencies of labeled paths. [18] We proposed methodologies for inverse QSPR/QSAR analysis, [19,20] where probability density and a structure generator play pivotal roles. In our approach, the posterior density of x given a specific y value is derived [21] by Bayes' theorem, which means taking applicability domains (ADs) [22,23] of the regression model into account.…”