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

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.

[1]  Tudor I. Oprea,et al.  Property distribution of drug-related chemical databases* , 2000, J. Comput. Aided Mol. Des..

[2]  H. Zhou,et al.  Traditional Chinese medicine information database , 2005, Journal of ethnopharmacology.

[3]  A. Ghose,et al.  A knowledge-based approach in designing combinatorial or medicinal chemistry libraries for drug discovery. 1. A qualitative and quantitative characterization of known drug databases. , 1999, Journal of combinatorial chemistry.

[4]  Gang Chen,et al.  A New Rapid and Effective Chemistry Space Filter in Recognizing a Druglike Database , 2005, J. Chem. Inf. Model..

[5]  Tingjun Hou,et al.  Drug and Drug Candidate Building Block Analysis , 2010, J. Chem. Inf. Model..

[6]  Jun Xu,et al.  Drug-like Index: A New Approach To Measure Drug-like Compounds and Their Diversity , 2000, J. Chem. Inf. Comput. Sci..

[7]  Thomas A. Halgren Merck molecular force field. I. Basis, form, scope, parameterization, and performance of MMFF94 , 1996, J. Comput. Chem..

[8]  Tingjun Hou,et al.  ADME Evaluation in Drug Discovery. 3. Modeling Blood-Brain Barrier Partitioning Using Simple Molecular Descriptors [J. Chem. Inf. Comput. Sci. 43, 2137-2152 (2003)] , 2004, J. Chem. Inf. Model..

[9]  Tingjun Hou,et al.  ADME Evaluation in Drug Discovery. 3. Modeling Blood-Brain Barrier Partitioning Using Simple Molecular Descriptors , 2003, J. Chem. Inf. Comput. Sci..

[10]  F Darvas,et al.  Prediction of distribution coefficient from structure. 1. Estimation method. , 1997, Journal of pharmaceutical sciences.

[11]  H. Kubinyi,et al.  A scoring scheme for discriminating between drugs and nondrugs. , 1998, Journal of medicinal chemistry.

[12]  Miklos Feher,et al.  Property Distributions: Differences between Drugs, Natural Products, and Molecules from Combinatorial Chemistry , 2003, J. Chem. Inf. Comput. Sci..

[13]  Jin-Ling Tang,et al.  Traditional Chinese medicine , 2008, The Lancet.

[14]  Andreas Bender,et al.  A Large Descriptor Set and a Probabilistic Kernel-Based Classifier Significantly Improve Druglikeness Classification , 2007, J. Chem. Inf. Model..

[15]  Markus Wagener,et al.  Potential Drugs and Nondrugs: Prediction and Identification of Important Structural Features , 2000, J. Chem. Inf. Comput. Sci..

[16]  Igor V. Pletnev,et al.  Drug Discovery Using Support Vector Machines. The Case Studies of Drug-likeness, Agrochemical-likeness, and Enzyme Inhibition Predictions , 2003, J. Chem. Inf. Comput. Sci..

[17]  D. Normile The New Face of Traditional Chinese Medicine , 2003, Science.

[18]  Tingjun Hou,et al.  ADME Evaluation in Drug Discovery. 4. Prediction of Aqueous Solubility Based on Atom Contribution Approach , 2004, J. Chem. Inf. Model..

[19]  Tingjun Hou,et al.  Drug-likeness analysis of traditional Chinese medicines: prediction of drug-likeness using machine learning approaches. , 2012, Molecular pharmaceutics.

[20]  G. Bemis,et al.  The properties of known drugs. 1. Molecular frameworks. , 1996, Journal of medicinal chemistry.

[21]  Junmei Wang,et al.  Structure – ADME relationship : still a long way to go ? , 2008 .

[22]  Srikanta Sen,et al.  A Simple Approach for Indexing the Oral Druglikeness of a Compound: Discriminating Druglike Compounds from Nondruglike Ones , 2006, J. Chem. Inf. Model..

[23]  Craig M. Crews,et al.  Molecular Understanding and Modern Application of Traditional Medicines: Triumphs and Trials , 2007, Cell.

[24]  D. Newman,et al.  Natural products as sources of new drugs over the last 25 years. , 2007, Journal of natural products.

[25]  Michael C. Hutter Separating Drugs from Nondrugs: A Statistical Approach Using Atom Pair Distributions , 2007, J. Chem. Inf. Model..

[26]  Tingjun Hou,et al.  Recent development and application of virtual screening in drug discovery: an overview. , 2004, Current pharmaceutical design.

[27]  Jens Sadowski,et al.  Comparison of Support Vector Machine and Artificial Neural Network Systems for Drug/Nondrug Classification , 2003, J. Chem. Inf. Comput. Sci..

[28]  G Fisch,et al.  Traditional chinese medicine , 2015, Reactions Weekly.

[29]  Ajay,et al.  Can we learn to distinguish between "drug-like" and "nondrug-like" molecules? , 1998, Journal of medicinal chemistry.

[30]  T. Halgren Merck molecular force field. I. Basis, form, scope, parameterization, and performance of MMFF94 , 1996, J. Comput. Chem..

[31]  Wei Zhang,et al.  Recent advances in computational prediction of drug absorption and permeability in drug discovery. , 2006, Current medicinal chemistry.

[32]  I. Muegge,et al.  Simple selection criteria for drug-like chemical matter. , 2001, Journal of medicinal chemistry.

[33]  Kaixian Chen,et al.  Virtual screening on natural products for discovering active compounds and target information. , 2003, Current medicinal chemistry.

[34]  Tingjun Hou,et al.  ADME Evaluation in Drug Discovery, 7. Prediction of Oral Absorption by Correlation and Classification , 2007, J. Chem. Inf. Model..

[35]  Calvin Yu-Chian Chen,et al.  TCM Database@Taiwan: The World's Largest Traditional Chinese Medicine Database for Drug Screening In Silico , 2011, PloS one.

[36]  Tingjun Hou,et al.  ADME evaluation in drug discovery , 2002, Journal of molecular modeling.

[37]  G. Bemis,et al.  Properties of known drugs. 2. Side chains. , 1999, Journal of medicinal chemistry.

[38]  Tingjun Hou,et al.  ADME Evaluation in Drug Discovery. 5. Correlation of Caco-2 Permeation with Simple Molecular Properties , 2004, J. Chem. Inf. Model..

[39]  Dennis Normile,et al.  Asian medicine. The new face of traditional Chinese medicine. , 2003, Science.

[40]  Igor V. Tetko,et al.  Estimation of Aqueous Solubility of Chemical Compounds Using E-State Indices , 2001, J. Chem. Inf. Comput. Sci..

[41]  W. Patrick Walters,et al.  A guide to drug discovery: Designing screens: how to make your hits a hit , 2003, Nature Reviews Drug Discovery.

[42]  Tingjun Hou,et al.  Recent developments of in silico predictions of intestinal absorption and oral bioavailability. , 2009, Combinatorial chemistry & high throughput screening.

[43]  Tingjun Hou,et al.  A 3D Structure Database of Components from Chinese Traditional Medicinal Herbs , 2002, J. Chem. Inf. Comput. Sci..

[44]  F. Lombardo,et al.  Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. , 2001, Advanced drug delivery reviews.