Abstract
The maximum common property similarity (MCPhd) method is presented using descriptors as a new approach to determine the similarity between two chemical compounds or molecular graphs. This method uses the concept of maximum common property arising from the concept of maximum common substructure and is based on the electrotopographic state index for atoms. A new algorithm to quantify the similarity values of chemical structures based on the presented maximum common property concept is also developed in this paper. To verify the validity of this approach, the similarity of a sample of compounds with antimalarial activity is calculated and compared with the results obtained by four different similarity methods: the small molecule subgraph detector (SMSD), molecular fingerprint based (OBabel_FP2), ISIDA descriptors and shape-feature similarity (SHAFTS). The results obtained by the MCPhd method differ significantly from those obtained by the compared methods, improving the quantification of the similarity. A major advantage of the proposed method is that it helps to understand the analogy or proximity between physicochemical properties of the molecular fragments or subgraphs compared with the biological response or biological activity. In this new approach, more than one property can be potentially used. The method can be considered a hybrid procedure because it combines descriptor and the fragment approaches.
Highlights
Molecular similarity is one of the most explored and employed concepts in cheminformatics [1]
We introduce a new concept called maximum common property (MCPhd), inspired by maximum common subgraph (MCS), to quantify the similarity based on substructure, using the electrotopographic state index for atoms ( Sstate3D ) [17], which was developed from its parent electrotopologic defined by Kier and Hall [18] from the connectivity matrix of the hydrogen-depleted chemical graph as an atomic descriptor
The molecular similarity methods compared in this work, small molecule subgraph detector (SMSD) OBabel_FP2, ISIDA, shape-feature similarity (SHAFTS) and MCPhd, use different approaches to quantify the similarity between two molecular graphs or molecules
Summary
Molecular similarity is one of the most explored and employed concepts in cheminformatics (chemical informatics or chemoinformatics) [1]. It is currently one of the central subjects in medicinal chemistry research [1, 2]. Similarity calculations based on molecular descriptors use fingerprint representations [3, 4]. These representations can be codified both by topological or topographic descriptors. Topological descriptors are the most popular because the 2D representation of molecules is computationally less difficult to work with than the 3D representation [1]
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