Abstract
BackgroundIn this work, we analyzed and compared the distribution profiles of a wide variety of molecular properties for three compound classes: drug-like compounds in MDL Drug Data Report (MDDR), non-drug-like compounds in Available Chemical Directory (ACD), and natural compounds in Traditional Chinese Medicine Compound Database (TCMCD).ResultsThe comparison of the property distributions suggests that, when all compounds in MDDR, ACD and TCMCD with molecular weight lower than 600 were used, MDDR and ACD are substantially different while TCMCD is much more similar to MDDR than ACD. However, when the three subsets of ACD, MDDR and TCMCD with similar molecular weight distributions were examined, the distribution profiles of the representative physicochemical properties for MDDR and ACD do not differ significantly anymore, suggesting that after the dependence of molecular weight is removed drug-like and non-drug-like molecules cannot be effectively distinguished by simple property-based filters; however, the distribution profiles of several physicochemical properties for TCMCD are obviously different from those for MDDR and ACD. Then, the performance of each molecular property on predicting drug-likeness was evaluated. No single molecular property shows good performance to discriminate between drug-like and non-drug-like molecules. Compared with the other descriptors, fractional negative accessible surface area (FASA-) performs the best. Finally, a PCA-based scheme was used to visually characterize the spatial distributions of the three classes of compounds with similar molecular weight distributions.ConclusionIf FASA- was used as a drug-likeness filter, more than 80% molecules in TCMCD were predicted to be drug-like. Moreover, the principal component plots show that natural compounds in TCMCD have different and even more diverse distributions than either drug-like compounds in MDDR or non-drug-like compounds in ACD.
Highlights
In this work, we analyzed and compared the distribution profiles of a wide variety of molecular properties for three compound classes: drug-like compounds in MDL Drug Data Report (MDDR), non-drug-like compounds in Available Chemical Directory (ACD), and natural compounds in Traditional Chinese Medicine Compound Database (TCMCD)
Substantial efforts have been made in the development of computational approaches for differentiating drug-like molecules from reagents, such as the simple property-based filters or rules [2,3,4,5,6,7,8], the drug-like index to rank molecules [9,10], the characterization of molecular frameworks and side chains [11,12,13], the classification models of drug-likeness based on decision trees (DTs), artificial neural networks (ANNs), support vector machines (SVMs), etc. [14,15,16,17,18]
To evaluate the drug-likeness of natural compounds from traditional Chinese medicines quantitatively, we have examined three compound collections, including ACD, MDDR and TCMCD, with respect to the distribution profiles of a variety of molecular descriptors
Summary
We analyzed and compared the distribution profiles of a wide variety of molecular properties for three compound classes: drug-like compounds in MDL Drug Data Report (MDDR), non-drug-like compounds in Available Chemical Directory (ACD), and natural compounds in Traditional Chinese Medicine Compound Database (TCMCD). Substantial efforts have been made in the development of computational approaches for differentiating drug-like molecules from reagents, such as the simple property-based filters or rules [2,3,4,5,6,7,8], the drug-like index to rank molecules [9,10], the characterization of molecular frameworks and side chains [11,12,13], the classification models of drug-likeness based on decision trees (DTs), artificial neural networks (ANNs), support vector machines (SVMs), etc. Substantial efforts have been made in the development of computational approaches for differentiating drug-like molecules from reagents, such as the simple property-based filters or rules [2,3,4,5,6,7,8], the drug-like index to rank molecules [9,10], the characterization of molecular frameworks and side chains [11,12,13], the classification models of drug-likeness based on decision trees (DTs), artificial neural networks (ANNs), support vector machines (SVMs), etc. [14,15,16,17,18]
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