Drug-likeness analysis of traditional Chinese medicines: 1. property distributions of drug-like compounds, non-drug-like compounds and natural compounds from traditional Chinese medicines

Mingyun Shen, Sheng Tian, Youyong Li, Qian Li, Xiaojie Xu, Junmei Wang, Tingjun Hou

Research output: Contribution to journalArticle

27 Citations (Scopus)

Abstract

Background: 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). Results: The 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. Conclusion: If 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.

Original languageEnglish (US)
Article number31
JournalJournal of Cheminformatics
Volume4
Issue number1
DOIs
StatePublished - 2012

Fingerprint

distribution (property)
medicine
Medicine
drugs
directories
drug
Pharmaceutical Preparations
Molecular weight distribution
Molecules
molecular properties
Molecular weight
molecular weight
Spatial distribution
profiles
filters
molecules
low molecular weights
set theory
performance

Keywords

  • Drug-likeness
  • Molecular properties
  • Principal component analysis (PCA)
  • Property distribution
  • Traditional Chinese medicines

ASJC Scopus subject areas

  • Physical and Theoretical Chemistry
  • Computer Graphics and Computer-Aided Design
  • Computer Science Applications
  • Library and Information Sciences

Cite this

Drug-likeness analysis of traditional Chinese medicines : 1. property distributions of drug-like compounds, non-drug-like compounds and natural compounds from traditional Chinese medicines. / Shen, Mingyun; Tian, Sheng; Li, Youyong; Li, Qian; Xu, Xiaojie; Wang, Junmei; Hou, Tingjun.

In: Journal of Cheminformatics, Vol. 4, No. 1, 31, 2012.

Research output: Contribution to journalArticle

@article{1cb97a87a243479dbdaa09c33d47d6da,
title = "Drug-likeness analysis of traditional Chinese medicines: 1. property distributions of drug-like compounds, non-drug-like compounds and natural compounds from traditional Chinese medicines",
abstract = "Background: 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). Results: The 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. Conclusion: If 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.",
keywords = "Drug-likeness, Molecular properties, Principal component analysis (PCA), Property distribution, Traditional Chinese medicines",
author = "Mingyun Shen and Sheng Tian and Youyong Li and Qian Li and Xiaojie Xu and Junmei Wang and Tingjun Hou",
year = "2012",
doi = "10.1186/1758-2946-4-31",
language = "English (US)",
volume = "4",
journal = "Journal of Cheminformatics",
issn = "1758-2946",
publisher = "Chemistry Central",
number = "1",

}

TY - JOUR

T1 - Drug-likeness analysis of traditional Chinese medicines

T2 - 1. property distributions of drug-like compounds, non-drug-like compounds and natural compounds from traditional Chinese medicines

AU - Shen, Mingyun

AU - Tian, Sheng

AU - Li, Youyong

AU - Li, Qian

AU - Xu, Xiaojie

AU - Wang, Junmei

AU - Hou, Tingjun

PY - 2012

Y1 - 2012

N2 - Background: 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). Results: The 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. Conclusion: If 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.

AB - Background: 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). Results: The 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. Conclusion: If 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.

KW - Drug-likeness

KW - Molecular properties

KW - Principal component analysis (PCA)

KW - Property distribution

KW - Traditional Chinese medicines

UR - http://www.scopus.com/inward/record.url?scp=84872081711&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84872081711&partnerID=8YFLogxK

U2 - 10.1186/1758-2946-4-31

DO - 10.1186/1758-2946-4-31

M3 - Article

C2 - 23181938

AN - SCOPUS:84872081711

VL - 4

JO - Journal of Cheminformatics

JF - Journal of Cheminformatics

SN - 1758-2946

IS - 1

M1 - 31

ER -