Classification analysis of fatty acid synthase inhibitors using multialgorithms on topological descriptors and structural fingerprints

Fatty acid synthase (FASN) is one of the enzymes required for fatty acid biosynthesis and is expressed as low or absent in most normal cells/tissues. However, this enzyme is upregulated in various cancer cells; hence, it can act as an important target to design and develop novel FASN inhibitors for cancer therapy. In the present investigation, a series of structurally diverse compounds that possessed FASN inhibitory activities were subjected to classification analysis using different algorithms such as support vector machine, decision tree, Naive Bayes and random forest. The physicochemical descriptors and MACCS fingerprints were calculated using PaDEL software, and the WEKA software was utilized for the classification model building. The statistical parameters/confusion matrix calculated from the analysis revealed that the selected models have significant predictive performances. The results showed that the topological properties of the molecules are the main determinant for the activity classification. The key descriptors comprised of hydrogen bonding groups, especially acceptor (nHBAcc, minHBint9, minHBint5 and nwHBa), charge on the topological surface of the molecules (JGI10 & GGI2), ionization potential (GATS5i and GATS1i) and branching and distance between the groups (ETA_Eta_B_RC) are significantly contributed in the classification models. Further, the presence of heteroatoms (MACCSFP82, MACCSFP93 and MACCSFP131), especially nitrogen atom(s) and hydrogen bond acceptor groups (N-N group, NC(=O)N, N-C(=O)), actively contributed to the inhibitory activities. The results concluded that the topological polar properties concentrated in a specific region have significant FASN inhibitory activity. Hence, these results shall be used to develop novel molecules with increased FASN inhibitory activity.