Universal Approach for Structural Interpretation of QSAR/QSPR Models

Abstract

In this paper we offer a novel approach for the structural interpretation of QSAR models. The major advantage of our developed methodology is its universality, i.e., it can be applied to any QSAR/QSPR model irrespective of chemical descriptors and machine learning methods applied. This universality was achieved by using only the information obtained from substructures of the compounds of interest to interpret model outcomes. Reliability of the offered approach was confirmed by the results of three case studies, including end-points of different types (continuous and binary classification) and nature (solubility, mutagenicity, and inhibition of Transglutaminase 2), various fragment and whole-molecule descriptors (Simplex and Dragon), and multiple modeling techniques (partial least squares, random forest, and support vector machines). We compared the global contributions of molecular fragments obtained using our methodology with known SAR rules derived experimentally. In all cases high concordance between our interpretation and results published by others was observed. Although the proposed interpretation approach could be easily extended to any type of descriptors, we would recommend using Simplex descriptors to achieve a larger variety of investigated molecular fragments. The developed approach is a good tool for interpretation of such "black box" models like random forest, neural networks, etc. Analysis of fragment global contributions and their deviation across a dataset could be useful for the identification of key fragments and structural alerts. This information could be helpful to maximize the positive influence of structural surroundings on the given fragment and to decrease the negative effects.

QSAR Modeling: Where Have You Been? Where Are You Going To?

“…174 The gist of this methodology is very simple (Figure 3): activities of a target molecule (P QSAR (AB) in Figure 3a or P QSAR (ABC) in Figure 3b) and its virtual analog (P QSAR (A) in Figure 2a or P QSAR (A…C), derived from this initial molecule by eliminating a molecular fragment with a pre-defined structure, are estimated using the QSAR model. The difference in the calculated activities of a target molecule and its virtual analog is considered as an influence (contribution) of eliminated fragment (P'(B) in Figure 3).…”

Universal fragment descriptors for predicting properties of inorganic crystals

“…Universal Property-Labeled Materials Fragments. Many cheminformatics investigations have demonstrated the critical importance of molecular descriptors, which are known to influence model accuracy more than the choice of the ML algorithm [50,51]. For the purposes of this investigation, fragment descriptors typically used for organic molecules were adapted to serve for materials characterization [52].…”

Alarms about structural alerts

“…47 By interpreting developed models, 27 we can gain insight into structural features responsible for the binding affinity of ligands to receptors and guide structural modifications that will modulate binding affinity of ligands. Figure 2 shows that a small change such as replacement of carboxymethyl by carboxyethyl substituent in the same position in a molecule causes changes in many descriptor values.…”

“…Although QSAR modeling is usually referred to as a “black box” approach, many studies conducted both by our group alone, 27,56 or in collaboration with other groups, 57,58 and by other groups 59–61 have shown that the models could be interpreted in terms of structural features responsible for activity or toxicity.…”