The rcdk and cluster R packages applied to drug candidate selection

Abstract

The aim of this article is to show how thevpower of statistics and cheminformatics can be combined, in R, using two packages: rcdk and cluster.We describe the role of clustering methods for identifying similar structures in a group of 23 molecules according to their fingerprints. The most commonly used method is to group the molecules using a "score" obtained by measuring the average distance between them. This score reflects the similarity/non-similarity between compounds and helps us identify active or potentially toxic substances through predictive studies.Clustering is the process by which the common characteristics of a particular class of compounds are identified. For clustering applications, we are generally measure the molecular fingerprint similarity with the Tanimoto coefficient. Based on the molecular fingerprints, we calculated the molecular distances between the methotrexate molecule and the other 23 molecules in the group, and organized them into a matrix. According to the molecular distances and Ward 's method, the molecules were grouped into 3 clusters. We can presume structural similarity between the compounds and their locations in the cluster map. Because only 5 molecules were included in the methotrexate cluster, we considered that they might have similar properties and might be further tested as potential drug candidates.

Chemoinformatic Screening for the Selection of Potential Senolytic Compounds from Natural Products

“…Although there are many clustering methods, only some are used in practice; two were used in this study: hierarchical grouping and the K-means method. They have been used successfully in fingerprint analysis [ 21 ]. In this study, fingerprint analysis allowed us to obtain 17 molecules due to their similarity with lead molecules from cluster one, indicating that the physicochemical properties of the 17 compounds have a greater similarity with the properties of senolytics.…”

“…To determine their fingerprints and perform a comparison, we used ChemmineR and rcdk in R-Studio version 3.4 [ 21 ]. We used the extended value with a default length of 1024 (number of bits), taking rings and atomic properties of the senolytic compound dataset as leads and the NPs obtained from databases as tested compounds.…”

“…We used the extended value with a default length of 1024 (number of bits), taking rings and atomic properties of the senolytic compound dataset as leads and the NPs obtained from databases as tested compounds. Then, we performed a cluster analysis by using the Tanimoto coefficient [ 21 ] to compare the drug-likeness of NPs versus the senolytic compounds. The molecules were then classified into three groups using as a parameter the molecular distances by Ward’s clustering method, which are the most popular hierarchical clustering algorithms used in drug discovery [ 22 ].…”

“…The molecules were then classified into three groups using as a parameter the molecular distances by Ward’s clustering method, which are the most popular hierarchical clustering algorithms used in drug discovery [ 22 ]. To confirm the clustering results, the Dunn index and the silhouette coefficient were used [ 21 ]. After the different clustering methods were performed, the cluster containing the senolytic lead was analyzed in order to obtain the putative senolytic molecules and construct a compound-target network.…”

Z-Matrix template-based substitution approach for enumeration of 3D molecular structures

“…Initial structures (Z-matrix or Cartesian coordinate) are important starting points for the in silico investigation of chemical species. Robust and exhaustive processes to generate chemical structures have been described previously in different contexts [1][2][3][4][5][6][7][8][9]. However, there are still no consistent onesize-fits-all standard for structural enumeration.…”