A constructive approach for discovering new drug leads: Using a kernel methodology for the inverse-QSAR problem

Abstract

Background The inverse-QSAR problem seeks to find a new molecular descriptor from which one can recover the structure of a molecule that possess a desired activity or property. Surprisingly, there are very few papers providing solutions to this problem. It is a difficult problem because the molecular descriptors involved with the inverse-QSAR algorithm must adequately address the forward QSAR problem for a given biological activity if the subsequent recovery phase is to be meaningful. In addition, one should be able to construct a feasible molecule from such a descriptor. The difficulty of recovering the molecule from its descriptor is the major limitation of most inverse-QSAR methods.Results In this paper, we describe the reversibility of our previously reported descriptor, the vector space model molecular descriptor (VSMMD) based on a vector space model that is suitable for kernel studies in QSAR modeling. Our inverse-QSAR approach can be described using five steps: (1) generate the VSMMD for the compounds in the training set; (2) map the VSMMD in the input space to the kernel feature space using an appropriate kernel function; (3) design or generate a new point in the kernel feature space using a kernel feature space algorithm; (4) map the feature space point back to the input space of descriptors using a pre-image approximation algorithm; (5) build the molecular structure template using our VSMMD molecule recovery algorithm.Conclusion The empirical results reported in this paper show that our strategy of using kernel methodology for an inverse-Quantitative Structure-Activity Relationship is sufficiently powerful to find a meaningful solution for practical problems.Electronic supplementary materialThe online version of this article (doi:10.1186/1758-2946-1-4) contains supplementary material, which is available to authorized users.

Bayesian molecular design with a chemical language model

“…In general, the molecular design process involves two different types of prediction; the forward prediction is aimed at predicting physical, chemical and electric properties of a given molecular structure, and the backward prediction is to inversely identify appropriate molecular structures with the given desired properties. While the former design process is referred to as the quantitative structure-property relationship (QSPR) analysis, the latter is known as the inverse-QSPR analysis [1–9]. In this study, a Bayesian perspective is employed to unify the forward and backward prediction processes.…”

“…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.…”

“…Graph enumeration is generally less effective due to the combinatorial complexity of the design space. To narrow down the candidates, several ways to use a restricted class of molecular graphs have been investigated [9, 14]. Using GAs [15], which have been more intensively studied, searches for optimal or suboptimal designs by successively modifying chemical structures with genetic operators consisting of mutation, crossover, and selection.…”

On Exploring Structure–Activity Relationships

“…Visco et al employed the signature molecular descriptors [15] to perform an inverse QSAR analysis of 121 HIV protease inhibitors and Churchwell et al [16] employed these same descriptors to explore QSARs of peptides inhibiting ICAM-1. More recently, Wong et al [17] developed a novel descriptor to address inverse QSAR and coupled this to kernel method to allow explicit mapping between points in the high dimensional kernel space (i.e., candidate structures) back to the original descriptor space and thence to a set of candidate molecules.…”

Exploring differential evolution for inverse QSAR analysis

“…So far only few inverse QSAR studies have employed methods other than MLR. For example, it was attempted to construct chemical graphs from the centroid of activity of a set of compounds in Hilbert space defined by a kernel function 12 . In this case, a pre-image approximation algorithm was used to obtain coordinates in descriptor space and construct chemical graphs from these descriptor coordinates.…”