In silico drug metabolism and pharmacokinetic profiles of natural products from medicinal plants in the Congo basin
© Ntie-Kang et al.; licensee Springer. 2013
Received: 21 May 2013
Accepted: 6 August 2013
Published: 8 August 2013
Drug metabolism and pharmacokinetics (DMPK) assessment has come to occupy a place of interest during the early stages of drug discovery today. The use of computer modelling to predict the DMPK and toxicity properties of a natural product library derived from medicinal plants from Central Africa (named ConMedNP). Material from some of the plant sources are currently employed in African Traditional Medicine.
Computer-based methods are slowly gaining ground in this area and are often used as preliminary criteria for the elimination of compounds likely to present uninteresting pharmacokinetic profiles and unacceptable levels of toxicity from the list of potential drug candidates, hence cutting down the cost of discovery of a drug.
In the present study, we present an in silico assessment of the DMPK and toxicity profile of a natural product library containing ~3,200 compounds, derived from 379 species of medicinal plants from 10 countries in the Congo Basin forests and savannas, which have been published in the literature. In this analysis, we have used 46 computed physico-chemical properties or molecular descriptors to predict the absorption, distribution, metabolism and elimination and toxicity (ADMET) of the compounds.
This survey demonstrated that about 45% of the compounds within the ConMedNP compound library are compliant, having properties which fall within the range of ADME properties of 95% of currently known drugs, while about 69% of the compounds have ≤ 2 violations. Moreover, about 73% of the compounds within the corresponding “drug-like” subset showed compliance.
In addition to the verified levels of “drug-likeness”, diversity and the wide range of measured biological activities, the compounds from medicinal plants in Central Africa show interesting DMPK profiles and hence could represent an important starting point for hit/lead discovery.
KeywordsADMET Drug discovery Descriptors In silico Medicinal plants Natural products
Natural products (NPs) have always played an important role in drug discovery until today (Li and Vederas 2009; Chin et al. 2006; Newman 2008; Harvey 2008; Koehn and Carter 2005). This is because they both serve as active principles in drugs and as templates for the synthesis of new drugs (Newman 2008; Efange 2002). Additionally, a good proportion of drugs which have been approved for clinical trials, are either NPs or their analogues (Butler 2005). What makes NPs unique is that they are often rich in stereogenic centres and cover segments of chemical space which are typically not occupied by a majority of their synthetic counterparts (Wetzel et al. 2007; Grabowski et al. 2008). Moreover, NPs generally contain more oxygen atoms and less aromatic atoms on average, when compared with “drug-like” molecules (Grabowski and Schneider 2007). They sometimes fail the test for “drug-likeness” due to the fact that they are often bulkier than synthetic drugs (Quinn et al. 2008).
The fact that more and more drugs fail to enter the market as a result of poor pharmacokinetic profiles, has necessitated the inclusion of pharmacokinetic considerations at earlier stages of drug discovery programs (Hodgson 2001; Navia and Chaturvedi 1996). This requires the search for lead compounds which can be easily orally absorbed, easily transported to their desired site of action, not easily metabolised into toxic products before reaching the targeted site of action and easily eliminated from the body before accumulating in sufficient amounts that may produce adverse side effects. The sum of the above mentioned properties is often referred to as ADME (absorption, distribution, metabolism and elimination) properties, or better still ADMET, ADME/T or ADMETox (when toxicity assessment is included).
Computer-based methods have been employed in the prediction of ADMET properties of drug leads at early stages of drug discovery and such approaches are becoming increasingly popular (Lipinski et al. 1997; Lombardo et al. 2003; Gleeson et al. 2011). The rationale behind in silico approaches are the relatively lower cost and the time factor involved, when compared to standard experimental approaches for ADMET profiling (DiMasi et al. 2003; Darvas et al. 2002). As an example, it takes a minute in an in silico model to screen 20,000 molecules, but takes 20 weeks in the “wet” laboratory to do the same exercise (Hodgson 2001). Due to the accumulated ADMET data in the late 1990s, many pharmaceutical companies are now using computational models that, in some cases, are replacing the “wet” screens (Hodgson 2001). This paradigm shift has therefore spurred up the development of several theoretical methods for the prediction of ADMET parameters. A host of these theoretical models have been implemented in a number of software programs currently available for drug discovery protocols (OCHEM platform 2009; Lhasa 2010; Schrodinger 2011a; Cruciani et al. 2000), even though some of the predictions are often disappointing (Tetko et al. 2006). The software tools currently used to predict the ADMET properties of potential drug candidates often make use of quantitative structure-activity relationships, QSAR (Tetko et al. 2006; Hansch et al. 2004) or knowledge-base methods (Greene et al. 1999; Button et al. 2003; Cronin 2003). A promising lead compound may therefore be defined as one which combines potency with an admirable ADMET profile (commonly referred to as a compound’s CV). As such, compounds with uninteresting predicted ADMET profiles may be completely dismissed from the list of potential drug candidates (even if these prove to be highly potent). Otherwise, the DMPK properties are “fine-tuned” in order to improve their chances of making it to clinical trials (Hou and Wang 2008). This may explain why the “graveyard” of very highly potent compounds which do not make it to clinical trials keeps filling up, to the extent that experts in drug discovery are often faced with the challenge of either resorting to new lead compounds or “resurrecting” some buried leads with the view of “fine-tuning” their DMPK properties.
A natural product compound database built on information collected from several literature sources on medicinal plants from Central African countries, currently used in ATM, has been recently developed at our laboratory. The plants had been harvested from 10 countries (Burundi, Cameroon, Central African Republic, Chad, Congo, Equatorial Guinea, Gabon, the Democratic Republic of Congo, Rwanda and the Republic of São Tomé and Príncipe). This NP library currently contains ~3,200 compounds and preliminary analyses have proven the dataset to be sufficiently “drug-like” and diverse to be employed in lead discovery programs (Ntie-Kang et al. 2013a; Ntie-Kang et al.: ConMedNP: a natural product library from Central African medicinal plants for drug discovery. RSC Adv, submitted). Additional arguments in favour of the use of this database are the wide range of the previously observed biological activities of the compounds and the wide range of ailments being treated by traditional medicine with the help of the herbs from which the compounds have been derived (Ntie-Kang et al. 2013a, b; Zofou et al. 2013; Ntie-Kang et al.: ConMedNP: a natural product library from Central African medicinal plants for drug discovery. RSC Adv, submitted).
Since numerous drugs and many more lead compounds fail due to adverse pharmacokinetic properties at a late stage of pharmaceutical development (Darvas et al. 2002), it has become important to incorporate ADME properties’ prediction into the lead compound selection early enough, by means of molecular descriptors. A molecular descriptor may be defined as a structural or physico-chemical property of a molecule or part of a molecule, for example logarithm of the n-octanol/water partition coefficient (log P), the molar weight (MW) and the total polar surface area (TPSA). A number of relevant molecular properties (descriptors) are often used to help in the assessment of the DMPK properties of potential drug leads. In this paper, an attempt has been made to carry out an in silico assessment of the ADMET profile of this dataset. A number of computed molecular descriptors, currently implemented in a wide range of software, have been used as indicators of the pharmacokinetic properties of a large proportion of currently known drugs.
Data sources and generation of 3D structures
The plant sources, geographical collection sites, chemical structures of pure compounds as well as their measured biological activities, were retrieved from literature sources and have been previously described (Ntie-Kang et al. 2013b; Zofou et al. 2013). The 3D structures of the compounds had been sketched and energy minimisation subsequently carried out using a previously described protocol (Ntie-Kang et al. 2013a).
Initial treatment of chemical structures and calculation of ADMET-related descriptors
Selected computed ADMET-related descriptors and their recommended ranges for 95% of known drugs
the total solvent-accessible molecular surface, in Å2 (probe radius 1.4 Å)
300 to 1000 Å2
the hydrophobic portion of the solvent-accessible molecular surface, in Å2 (probe radius 1.4 Å)
0 to 750 Å2
the total volume of molecule enclosed by solvent-accessible molecular surface, in Å3 (probe radius 1.4 Å)
500 to 2000 Å3
log S wat
−6.0 to 0.5
log K HSA
the logarithm of predicted binding constant to human serum albumin (Colmenarejo 2001)
−1.5 to 1.2
−3.0 to 1.0
< 5 low, > 100 high
the predicted apparent Madin-Darby canine kidney cell permeability in nm s-1 (Irvine et al. 1999)
< 25 poor, > 500 great
the index of cohesion interaction in solids, calculated from the number of hydrogen bond acceptors (HBA), donors (HBD) and the surface area accessible to the solvent, SASA (S mol ) by the relation (Jorgensen and Duffy 2000)
0.0 to 0.05
the globularity descriptor, Glob = (4πr2)/S mol , where r is the radius of the sphere whose volume is equal to the molecular volume
0.75 to 0.95
the predicted polarizability
13.0 to 70.0
concern < −5
−8.0 to −1.0
the number of likely metabolic reactions
1 to 8
Results and discussion
Overall DMPK compliance of the ConMedNP library
Summary of mean pharmacokinetic property distributions of the total ConMedNP library in comparison with the various subsets
a Lib. size
b No. compl.
c MW (Da)
i BIP caco-2 (nm s -1 )
j S mol (Å 2 )
k S mol,hfob (Å 2 )
l V mol (Å 3 )
m Log S wat (S in mol L -1 )
n Log K HSA
p Ind coh
r QP polrz (Å 3 )
t Log K p
u # metab
Summary of percentage compliances of selected ADMET-related descriptors of the total ConMedNP library in comparison with the various subsets
*LogS wat (S in mol L-1)
* Ind coh
* Log K p
* # metab
A molecule’s size, as well as its capacity to make hydrogen bonds, its overall lipophilicity and its shape and flexibility are important properties to consider when determining permeability. Molecular flexibility has been seen as a parameter which is dependent on the number of rotatable bonds (NRB), a property which influences bioavailability in rats (Veber et al. 2002). The distribution of the NRB for this dataset has been discussed in detail elsewhere (Ntie-Kang et al. ConMedNP: a natural product library from Central African medicinal plants for drug discovery. RSC Adv, submitted) and the results reveal that the compounds within the ConMedNP library show some degree of conformational flexibility, the peak value for the NRB being between 1 and 2, while the average value is 7.46 (Table 2).
Prediction of blood–brain barrier (BBB) penetration
Prediction of dermal penetration
This parameter showed variations from 0 to 1603 μ cm-2 hr-1, with only about 1.38% of the compounds in ConMedNP having predicted value of J m > 100 μ cm-2 hr-1.
Prediction of plasma-protein binding
Prediction of blockage of human ether-a-go-go-related gene potassium (HERG K+) channel
Usefulness of the compound library
The usefulness of the ConMedNP database in lead generation has been exemplified with the docking and pharmacophore-based screening for potential inhibitors of a validated anti-malarial drug target in our laboratory, and the results will be published in a subsequent paper. It is important to mention that virtual screening results could provide insight and direct natural product chemists to search for theoretically active principles with attractive ADMET profiles, which have been previously isolated, but not tested for activity against specified drug targets (if samples are absent). This “resurrection” process could prove to be a better procedure for lead search than the random screening, which is a common practice in our African laboratories. This dataset is constantly being updated; meanwhile a MySQL platform to facilitate the searching of this database and ordering of compound samples is under development within our group and will also be published subsequently. However, 3D structures of the compounds, as well as their physico-chemical properties that were used to evaluate the DMPK profile, can be freely downloaded as additional files accompanying this publication (Additional files 1, 2, 3 and 4). In addition, information about compound sample availability can be obtained on request from the authors of this paper or from the pan-African Natural Products Library (p-ANAPL) project (Chibale et al. 2012; p-ANAPL 2013).
Modern drug discovery programs usually involve the search for small molecule leads with attractive pharmacokinetic profiles. The presence of such within the ConMedNP library is of major importance and therefore renders the database attractive, in addition to the already known properties – “drug-like”, “lead-like”, “fragment-like” and diverse. This is an indication that the 3D structures of naturally occurring compounds within ConMedNP could be a good starting point for docking, neural networking and pharmacophore-based virtual screening campaigns, thus rendering ConMedNP a useful asset for the drug discovery community.
Availability and requirements
3D structures of the compounds, as well as their physico-chemical properties that were used to evaluate the DMPK profile of the ConMedNP library, can be freely downloaded (for non commercial use) as additional files which accompany this publication (Additional files 1, 2, 3 and 4). Physical samples for testing are available at the various research laboratories in Central Africa in varying quantities. Questions regarding the available of compound samples could be addressed directly to the authors of this paper. Otherwise samples could be obtainable from the p-ANAPL consortium, which has a mandate to collect samples of NPs from the entire continent of Africa and make them available for biological screening. This network is being set up under the auspices of the Network for Analytical and Bioassay Services in Africa (NABSA) (Chibale et al. 2012; p- ANAPL 2013).
WS and SMNE are professors of medicinal chemistry with an interest in CADD, while SMNE also focuses organic synthesis and on natural product leads from Cameroonian medicinal plants. LMM and JAM are natural product chemists actively involved in the isolation and characterization of secondary metabolites from Cameroonian medicinal plants. LLL holds a PhD in environmental science and manages a Chemical and Bioactivity Information Centre (CBIC) with a focus on developing databases for information from medicinal herbs in Africa. PNJ is a retired research officer of Lhasa Ltd who currently leads the CBIC branch in Leeds, UK. FNK is a PhD student working on CADD under the joint supervision of LCOO and EM.
Absorption, distribution, metabolism, excretion, and toxicity
Computer-aided drug design
Congo basin medicinal plant and natural products database
Drug metabolism and pharmacokinetics
- log P:
Logarithm of the octan-1-ol/water partition coefficient
Madin-Darby canine kidney
Network for analytical and bioassay services in Africa
Number of rotatable bonds
Optimized potentials for liquid simulations
pan-African Natural Products Library
Solvent accessible surface area
Total polar surface area
Financial support is acknowledged from the German Academic Exchange Service (DAAD) to FNK for his stay in Halle, Germany for part of his PhD and from the International Centre for Theoretical Physics (ICTP), Trieste, Italy, via the OEA-AC71 program. The authors also acknowledge the academic license generously offered by Schrodinger Inc, for this work.
- Ajay , Bermis GW, Murkco MA: Designing libraries with CNS activity. J Med Chem 1999, 42: 4942–4951. 10.1021/jm990017wView ArticlePubMedGoogle Scholar
- Aronov AM: Predictive in silico modeling for hERG channel blockers. Drug Discov Today 2005, 10: 149–155. 10.1016/S1359-6446(04)03278-7View ArticlePubMedGoogle Scholar
- Butler MS: Natural products to drugs: natural product derived compounds in clinical trials. Nat Prod Rep 2005, 22: 162–195. 10.1039/b402985mView ArticlePubMedGoogle Scholar
- Button WG, Judson PN, Long A, Vessey JD: Using absolute and relative reasoning in the prediction of the potential metabolism of xenobiotics. J Chem Inf Comput Sci 2003, 43: 1371–1377. 10.1021/ci0202739View ArticlePubMedGoogle Scholar
- Cavalli A, Poluzzi E, De Ponti F, Recanatini M: Toward a pharmacophore for drugs inducing the long QT syndrome: insights from a CoMFA study of HERG K + channel blockers. J Med Chem 2002, 45: 3844–3853. 10.1021/jm0208875View ArticlePubMedGoogle Scholar
- Chibale K, Davies-Coleman M, Masimirembwa C: Drug discovery in Africa: impacts of genomics, natural products, traditional medicines, insights into medicinal chemistry, and technology platforms in pursuit of new drugs. Berlin: Springer; 2012.View ArticleGoogle Scholar
- Chiesa N, Rosati B, Arcangeli A, Olivotto M, Wanke E: A novel role for HERG K + channels: spike-frequency adaptation. J Physiol 1997, 501: 313–318. 10.1111/j.1469-7793.1997.313bn.xPubMed CentralView ArticlePubMedGoogle Scholar
- Chin YW, Balunas MJ, Chai HB, Kinghorn AD: Drug discovery from natural sources. AAPS J 2006,8(2):E239-E253.PubMed CentralView ArticlePubMedGoogle Scholar
- Colmenarejo G, Alvarez-Pedraglio A, Lavandera J-L: Cheminformatic models to predict binding affinities to human serum albumin. J Med Chem 2001, 44: 4370–4378. 10.1021/jm010960bView ArticlePubMedGoogle Scholar
- Cronin MTD: Computer-assisted prediction of drug toxicity and metabolism in modern methods of drug discovery. In Modern methods of drug discovery. Edited by: Hilgenfeld R, Hillisch A. Basel: Birkhäuser; 2003.Google Scholar
- Cruciani C, Crivori P, Carrupt PA, Testa B: Molecular fields in quantitative structure-permeation relationships: the VolSurf approach. J Mol Struc-Theochem 2000, 503: 17–30. 10.1016/S0166-1280(99)00360-7View ArticleGoogle Scholar
- Darvas F, Keseru G, Papp A, Dormán G, Urge L, Krajcsi P: In Silico and Ex silico ADME approaches for drug discovery. Top Med Chem 2002, 2: 1287–1304. 10.2174/1568026023392841View ArticleGoogle Scholar
- De Ponti F, Poluzzi E, Montanaro N: Organising evidence on QT prolongation and occurrence of Torsades de Pointes with non-antiarrhythmic drugs: a call for consensus. Eur J Clin Pharmacol 2001, 57: 185–209. 10.1007/s002280100290View ArticlePubMedGoogle Scholar
- DiMasi JA, Hansen RW, Grabowsk HG: The price of innovation: new estimates of drug development costs. J Health Econ 2003, 22: 151–185. 10.1016/S0167-6296(02)00126-1View ArticlePubMedGoogle Scholar
- Duffy EM, Jorgensen WL: Prediction of properties from simulations: free energies of solvation in hexadecane, octanol, and water. J Am Chem Soc 2000, 122: 2878–2888. 10.1021/ja993663tView ArticleGoogle Scholar
- Efange SMN: Natural products: a continuing source of inspiration for the medicinal chemist. In Advances in phytomedicine. Edited by: Iwu MM, Wootton JC. Amsterdam: Elsevier Science; 2002.Google Scholar
- Gleeson MP, Hersey A, Hannongbua S: In-silico ADME models: a general assessment of their utility in drug discovery applications. Curr Top Med Chem 2011,11(4):358–381. 10.2174/156802611794480927View ArticlePubMedGoogle Scholar
- Grabowski K, Schneider G: Properties and architecture of drugs and natural products revisited. Curr Chem Biol 2007, 1: 115–127.Google Scholar
- Grabowski K, Baringhaus K-H, Schneider G: Scaffold diversity of natural products: inspiration for combinatorial library design. Nat Prod Rep 2008, 25: 892–904. 10.1039/b715668pView ArticlePubMedGoogle Scholar
- Greene N, Judson PN, Langowski JJ: Knowledge-based expert systems for toxicity and metabolism prediction: DEREK, StAR and METEOR. SAR QSAR Environ Res 1999, 10: 299–314. 10.1080/10629369908039182View ArticlePubMedGoogle Scholar
- Hansch C, Leo A, Mekapatia SB, Kurup A: QSAR and ADME. Bioorg Med Chem 2004, 12: 3391–3400. 10.1016/j.bmc.2003.11.037View ArticlePubMedGoogle Scholar
- Harvey AL: Natural products in drug discovery. Drug Discov Today 2008, 13: 894–901. 10.1016/j.drudis.2008.07.004View ArticlePubMedGoogle Scholar
- Hedley PL, Jørgensen P, Schlamowitz S, Wangari R, Moolman-Smook J, Brink PA, Kanters JK, Corfield VA, Christiansen M: The genetic basis of long QT and short QT syndromes: a mutation update. Human Mutation 2009, 30: 1486–1511. 10.1002/humu.21106View ArticlePubMedGoogle Scholar
- Hodgson J: ADMET – turning chemicals into drugs. Nat Biotechnol 2001, 19: 722–726. 10.1038/90761View ArticlePubMedGoogle Scholar
- Hou T, Wang J: Structure-ADME relationship: still a long way to go? Expert Opin Drug Metab Toxicol 2008,4(6):759–770. 10.1517/17425255.4.6.759View ArticlePubMedGoogle Scholar
- Irvine JD, Takahashi L, Lockhart K, Cheong J, Tolan JW, Selick HE, Grove JR: MDCK (Madin-Darby canine kidney) cells: a tool for membrane permeability screening. J Pharm Sci 1999, 88: 28–33. 10.1021/js9803205View ArticlePubMedGoogle Scholar
- Jorgensen WL, Duffy EM: Prediction of drug solubility from Monte Carlo simulations. Bioorg Med Chem Lett 2000, 10: 1155–1158. 10.1016/S0960-894X(00)00172-4View ArticlePubMedGoogle Scholar
- Jorgensen WL, Duffy EM: Prediction of drug solubility from structure. Adv Drug Deliv Rev 2002, 54: 355–366. 10.1016/S0169-409X(02)00008-XView ArticlePubMedGoogle Scholar
- Jorgensen WL, Tirado-Rives J: The OPLS [optimized potentials for liquid simulations] potential functions for proteins, energy minimizations for crystals of cyclic peptides and crambin. J Am Chem Soc 1988,110(6):1657–1666. 10.1021/ja00214a001View ArticleGoogle Scholar
- Jorgensen WL, Maxwell DS, Tirado-Rives J: Development and testing of the OPLS all-atom force field on conformational energetics and properties of organic liquids. J Am Chem Soc 1996,118(45):11225–11236. 10.1021/ja9621760View ArticleGoogle Scholar
- Kelder J, Grootenhuis PD, Bayada DM, Delbresine LP, Ploemen JP: Polar molecular surface as a dominating determinant for oral absorption and brain pernetration of drugs. Pharm Res 1999, 16: 1514–1519. 10.1023/A:1015040217741View ArticlePubMedGoogle Scholar
- Koehn FE, Carter GT: The evolving role of natural products in drug discovery. Nat Rev Drug Discov 2005, 4: 206–220. 10.1038/nrd1657View ArticlePubMedGoogle Scholar
- Lhasa Ltd: Meteor, version 13.0.0. Leeds, UK: Lhasa; 2010.Google Scholar
- Li JWH, Vederas JC: Drug discovery and natural products: end of an era or an endless frontier? Science 2009, 325: 161–165. 10.1126/science.1168243View ArticlePubMedGoogle Scholar
- Lipinski CA, Lombardo F, Dominy BW, Feeney PJ: Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Delivery Rev 1997, 23: 3–25. 10.1016/S0169-409X(96)00423-1View ArticleGoogle Scholar
- Lombardo F, Gifford E, Shalaeva MY: In silico ADME prediction: data, models, facts and myths. Mini Rev Med Chem 2003, 3: 861–875. 10.2174/1389557033487629View ArticlePubMedGoogle Scholar
- Luco JM: Prediction of brain–blood distribution of a large set of drugs from structurally derived descriptors using partial least squares (PLS) modelling. J Chem Inf Comput Sci 1999, 39: 396–404. 10.1021/ci980411nView ArticlePubMedGoogle Scholar
- Navia MA, Chaturvedi PR: Design principles for orally bioavailable drugs. Drug Dev Today 1996, 1: 179–189. 10.1016/1359-6446(96)10020-9View ArticleGoogle Scholar
- Newman DJ: Natural products as leads to potential drugs: an old process or the new hope for drug discovery? J Med Chem 2008, 51: 2589–2599. 10.1021/jm0704090View ArticlePubMedGoogle Scholar
- Ntie-Kang F, Mbah JA, Mbaze LM, Lifongo LL, Scharfe M, Ngo Hanna J, Cho-Ngwa F, Amoa Onguéné P, Owono Owono LC, Megnassan E, Sippl W, Efange SMN: CamMedNP: Building the Cameroonian 3D structural natural products database for virtual screening. BMC Complement Altern Med 2013, 13: 88. 10.1186/1472-6882-13-88PubMed CentralView ArticlePubMedGoogle Scholar
- Ntie-Kang F, Lifongo LL, Mbaze LM, Ekwelle N, Owono Owono LC, Megnassan E, Judson PN, Sippl W, Efange SMN: Cameroonian medicinal plants: a bioactivity versus ethnobotanical survey and chemotaxonomic classification. BMC Complement Altern Med 2013b, 13: 147. 10.1186/1472-6882-13-147View ArticleGoogle Scholar
- OCHEM: A platform for the creation of in silico ADME/Tox prediction models. 2009. http://www.eadmet.com/en/ochem.phpGoogle Scholar
- Oprea TI: Current trends in lead discovery: are we looking for the appropriate properties? J Comput-Aided Mol Des 2002, 16: 325–334. 10.1023/A:1020877402759View ArticlePubMedGoogle Scholar
- p-ANAPL: pan-ANAPL: pan-African natural products library. 2013. http://www.linkedin.com/groups/pANPL-4098579/aboutGoogle Scholar
- Potts RO, Guy RH: Skin permeability. Pharm Res 1992, 9: 663–669. 10.1023/A:1015810312465View ArticlePubMedGoogle Scholar
- Potts RO, Guy RH: A predictive algorithm for skin permeability: the effects of molecular size and hydrogen bond activity. Pharm Res 1995, 12: 1628–1633. 10.1023/A:1016236932339View ArticlePubMedGoogle Scholar
- Quinn RJ, Carroll AR, Pham MB, Baron P, Palframan ME, Suraweera L, Pierens GK, Muresan S: Developing a drug-like natural product library. J Nat Prod 2008, 71: 464–468. 10.1021/np070526yView ArticlePubMedGoogle Scholar
- Schneider G: Trends in virtual computational library design. Curr Med Chem 2002, 9: 2095–2102. 10.2174/0929867023368755View ArticlePubMedGoogle Scholar
- Schrödinger: QikProp, version 3.4. New York, NY: LLC; 2011a.Google Scholar
- Schrödinger: LigPrep software, version 2.5. New York, NY: LLC; 2011b.Google Scholar
- Schrödinger: Maestro, version 9.2. New York, NY: LLC; 2011c.Google Scholar
- Schrödinger Press: QikProp 3.4 User Manual. New York, NY: LLC; 2011.Google Scholar
- Shivakumar D, Williams J, Wu Y, Damm W, Shelley J, Sherman W: Prediction of absolute solvation free energies using molecular dynamics free energy perturbation and the OPLS force field. J Chem Theory Comput 2010, 6: 1509–1519. 10.1021/ct900587bView ArticleGoogle Scholar
- Stenberg P, Norinder U, Luthman K, Artursson P: Experimental and computational screening models for the prediction of intestinal drug absorption. J Med Chem 2001, 44: 1927–1937. 10.1021/jm001101aView ArticlePubMedGoogle Scholar
- Teague SJ, Davis AM, Leeson PD, Opea TI: The design of leadlike combinatorial libraries. Angew Chem, Int Ed 1999, 38: 3743–3748. 10.1002/(SICI)1521-3773(19991216)38:24<3743::AID-ANIE3743>3.0.CO;2-UView ArticleGoogle Scholar
- Tetko IV, Bruneau P, Mewes H-W, Rohrer DC, Poda GI: Can we estimate the accuracy of ADMET predictions? Drug Discov Today 2006, 11: 700–707. 10.1016/j.drudis.2006.06.013View ArticlePubMedGoogle Scholar
- Van de Waterbeemd H, Gifford E: ADMET in silico modelling: towards prediction paradise? Nat Rev Drug Discov 2003, 2: 192–204. 10.1038/nrd1032View ArticlePubMedGoogle Scholar
- Vandenberg JI, Walker BD, Campbell TJ: HERG K + channels: friend or foe. Trends Pharmacol Sci 2001, 22: 240–246. 10.1016/S0165-6147(00)01662-XView ArticlePubMedGoogle Scholar
- Veber DF, Johnson SR, Cheng HY, Smith BR, Ward KW, Kopple KD: Molecular properties that influence the oral bioavailability of drug candidates. J Med Chem 2002, 45: 2615–2623. 10.1021/jm020017nView ArticlePubMedGoogle Scholar
- Verdonk ML, Cole JC, Hartshorn ML, Murray CW, Taylor RD: Improved protein-ligand docking using GOLD. Proteins 2003, 52: 609–623. 10.1002/prot.10465View ArticlePubMedGoogle Scholar
- Wetzel S, Schuffenhauer A, Roggo S, Ertl P, Waldmann H: Cheminformatic analysis of natural products and their chemical space. Chimia Int J Chem 2007, 61: 355–360. 10.2533/chimia.2007.355View ArticleGoogle Scholar
- Yazdanian M, Glynn SL, Wright JL, Hawi A: Correlating partitioning and caco-2 cell permeability of structurally diverse small molecular weight compounds. Pharm Res 1998, 15: 1490–1494. 10.1023/A:1011930411574View ArticlePubMedGoogle Scholar
- Zofou D, Ntie-Kang F, Sippl W, Efange SMN: Bioactive natural products derived from the Central African flora against neglected tropical diseases and HIV. Nat Prod Rep 2013, 30: 1098–1120. 10.1039/c3np70030eView ArticlePubMedGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.