Ligand- and Structure-based Approaches for Transmembrane Transporter
Modeling

Melanie      Grandits; Gerhard   F.   Ecker
doi:10.2174/2589977515666230508123041
Abstract

The study of transporter proteins is key to understanding the mechanism behind multidrug resistance and drug-drug interactions causing severe side effects. While ATP-binding transporters are well-studied, solute carriers illustrate an understudied family with a high number of orphan proteins. To study these transporters, in silico methods can be used to shed light on the basic molecular machinery by studying protein-ligand interactions. Nowadays, computational methods are an integral part of the drug discovery and development process. In this short review, computational approaches, such as machine learning, are discussed, which try to tackle interactions between transport proteins and certain compounds to locate target proteins. Furthermore, a few cases of selected members of the ATP binding transporter and solute carrier family are covered, which are of high interest in clinical drug interaction studies, especially for regulatory agencies. The strengths and limitations of ligand-based and structure-based methods are discussed to highlight their applicability for different studies. Furthermore, the combination of multiple approaches can improve the information obtained to find crucial amino acids that explain important interactions of protein-ligand complexes in more detail. This allows the design of drug candidates with increased activity towards a target protein, which further helps to support future synthetic efforts.
« Previous
Graphical Abstract

[1]
Theodoulou FL, Kerr ID. ABC transporter research: Going strong 40 years on. Biochem Soc Trans  2015; 43(5): 1033-40.
 [http://dx.doi.org/10.1042/BST20150139] [PMID:  26517919]

[2]
Amawi H, Sim HM, Tiwari AK, Ambudkar SV, Shukla S. ABC transporter-mediated multidrug-resistant cancer. Adv Exp Med Biol  2019; 1141: 549-80.
 [http://dx.doi.org/10.1007/978-981-13-7647-4_12] [PMID:  31571174]

[3]
Nigam SK. What do drug transporters really do? Nat Rev Drug Discov  2015; 14(1): 29-44.
 [http://dx.doi.org/10.1038/nrd4461] [PMID:  25475361]

[4]
Pizzagalli MD, Bensimon A, Superti-Furga G. A guide to plasma membrane solute carrier proteins. FEBS J  2021; 288(9): 2784-835.
 [http://dx.doi.org/10.1111/febs.15531] [PMID:  32810346]

[5]
Otsuka M, Matsumoto T, Morimoto R, Arioka S, Omote H, Moriyama Y. A human transporter protein that mediates the final excretion step for toxic organic cations. Proc Natl Acad Sci USA  2005; 102(50): 17923-8.
 [http://dx.doi.org/10.1073/pnas.0506483102] [PMID:  16330770]

[6]
Robey RW, Pluchino KM, Hall MD, Fojo AT, Bates SE, Gottesman MM. Revisiting the role of ABC transporters in multidrug-resistant cancer. Nat Rev Cancer  2018; 18(7): 452-64.
 [http://dx.doi.org/10.1038/s41568-018-0005-8] [PMID:  29643473]

[7]
Liu X. Transporter-mediated drug-drug interactions and their significance. Adv Exp Med Biol  2019; 1141: 241-91.
 [http://dx.doi.org/10.1007/978-981-13-7647-4_5] [PMID:  31571167]

[8]
Liu X. SLC family transporters. Adv Exp Med Biol  2019; 1141: 101-202.
 [http://dx.doi.org/10.1007/978-981-13-7647-4_3] [PMID:  31571165]

[9]
Yee SW, Brackman DJ, Ennis EA, et al. Influence of transporter polymorphisms on drug disposition and response: A perspective from the international transporter consortium. Clin Pharmacol Ther  2018; 104(5): 803-17.
 [http://dx.doi.org/10.1002/cpt.1098] [PMID:  29679469]

[10]
 In vitro Metabolism and Transporter-Mediated Drug-Drug Interaction Studies - Guidance for Industry 2017. Available From:
          https://www.fda.gov/files/drugs/published/In-Vitro-Metabolism-and-Transporter-Mediated-Drug-Drug-Interaction-Studies-Guidance-for-Industry.pdf(Accessed on: 2023 Mar 15).

[11]
Superti-Furga G, Lackner D, Wiedmer T, et al. The RESOLUTE consortium: Unlocking SLC transporters for drug discovery. Nat Rev Drug Discov  2020; 19(7): 429-30.
 [http://dx.doi.org/10.1038/d41573-020-00056-6] [PMID:  32265506]

[12]
Mendez D, Gaulton A, Bento AP, et al. ChEMBL: Towards direct deposition of bioassay data. Nucleic Acids Res  2019; 47(D1): D930-40.
 [http://dx.doi.org/10.1093/nar/gky1075] [PMID:  30398643]

[13]
 U.S. national library of medicine. National center for biotechnology information, PubChem. Available From:
          https://pubchem.ncbi.nlm.nih.gov [Accessed on: Mar 15, 2023].

[14]
Saier MH Jr, Reddy VS, Tsu BV, Ahmed MS, Li C, Moreno-Hagelsieb G. The transporter classification database (TCDB): Recent advances. Nucleic Acids Res  2016; 44(D1): D372-9.
 [http://dx.doi.org/10.1093/nar/gkv1103] [PMID:  26546518]

[15]
Mak L, Marcus D, Howlett A, et al. Metrabase: A cheminformatics and bioinformatics database for small molecule transporter data analysis and (Q)SAR modeling. J Cheminform  2015; 7(1): 31.
 [http://dx.doi.org/10.1186/s13321-015-0083-5] [PMID:  26106450]

[16]
Williams AJ, Harland L, Groth P, et al. Open PHACTS: Semantic interoperability for drug discovery. Drug Discov Today  2012; 17(21-22): 1188-98.
 [http://dx.doi.org/10.1016/j.drudis.2012.05.016] [PMID:  22683805]

[17]
Chang G. Multidrug resistance ABC transporters. FEBS Lett  2003; 555(1): 102-5.
 [http://dx.doi.org/10.1016/S0014-5793(03)01085-8] [PMID:  14630327]

[18]
Klopman G, Srivastava S, Kolossvary I, Epand RF, Ahmed N, Epand RM. Structure-activity study and design of multidrug-resistant reversal compounds by a computer automated structure evaluation methodology. Cancer Res  1992; 52(15): 4121-9.
 [PMID:  1638525]

[19]
Li Y, Wang YH, Yang L, Zhang SW, Liu CH, Yang SL. Comparison of steroid substrates and inhibitors of P-glycoprotein by 3D-QSAR analysis. J Mol Struct  2005; 733(1-3): 111-8.
 [http://dx.doi.org/10.1016/j.molstruc.2004.08.012]

[20]
Montanari F, Zdrazil B. How open data shapes in silico transporter modeling. Molecules  2017; 22(3): 422-32.
 [http://dx.doi.org/10.3390/molecules22030422] [PMID:  28272367]

[21]
Pajeva I, Wiese M. Molecular modeling of phenothiazines and related drugs as multidrug resistance modifiers: A comparative molecular field analysis study. J Med Chem  1998; 41(11): 1815-26.
 [http://dx.doi.org/10.1021/jm970786k] [PMID:  9599232]

[22]
Ecker G, Chiba P, Hitzler M, et al. Structure-activity relationship studies on benzofuran analogs of propafenone-type modulators of tumor cell multidrug resistance. J Med Chem  1996; 39(24): 4767-74.
 [http://dx.doi.org/10.1021/jm960384x] [PMID:  8941391]

[23]
Chiba P, Hitzler M, Richter E, et al. Studies on propafenone-type modulators of multidrug resistance III: Variations on the nitrogen. Quant Struct-Act Relationsh  1997; 16(5): 361-6.
 [http://dx.doi.org/10.1002/qsar.19970160502]

[24]
Cseke A, Schwarz T, Jain S, et al. Propafenone analogue with additional H-bond acceptor group shows increased inhibitory activity on P-glycoprotein. Arch Pharm Chem Life Sci  2020; e1900269.

[25]
Ecker G, Huber M, Schmid D, Chiba P. The importance of a nitrogen atom in modulators of multidrug resistance. Mol Pharmacol  1999; 56(4): 791-6.
 [PMID:  10496963]

[26]
Kühnle M, Egger M, Müller C, et al. Potent and selective inhibitors of breast cancer resistance protein (ABCG2) derived from the p-glycoprotein (ABCB1) modulator tariquidar. J Med Chem  2009; 52(4): 1190-7.
 [http://dx.doi.org/10.1021/jm8013822] [PMID:  19170519]

[27]
Globisch C, Pajeva IK, Wiese M. Structure–activity relationships of a series of tariquidar analogs as multidrug resistance modulators. Bioorg Med Chem  2006; 14(5): 1588-98.
 [http://dx.doi.org/10.1016/j.bmc.2005.10.058] [PMID:  16307883]

[28]
Palmeira A, Rodrigues F, Sousa E, Pinto M, Vasconcelos MH, Fernandes MX. New uses for old drugs: Pharmacophore-based screening for the discovery of P-glycoprotein inhibitors. Chem Biol Drug Des  2011; 78(1): 57-72.
 [http://dx.doi.org/10.1111/j.1747-0285.2011.01089.x] [PMID:  21235729]

[29]
Pan Y, Chothe PP, Swaan PW. Identification of novel breast cancer resistance protein (BCRP) inhibitors by virtual screening. Mol Pharm  2013; 10(4): 1236-48.
 [http://dx.doi.org/10.1021/mp300547h] [PMID:  23418667]

[30]
Ritschel T, Hermans SMA, Schreurs M, et al. In silico identification and in vitro validation of potential cholestatic compounds through 3D ligand-based pharmacophore modeling of BSEP inhibitors. Chem Res Toxicol  2014; 27(5): 873-81.
 [http://dx.doi.org/10.1021/tx5000393] [PMID:  24713091]

[31]
Langer T, Eder M, Hoffmann RD, Chiba P, Ecker GF. Lead identification for modulators of multidrug resistance based on in silico screening with a pharmacophoric feature model. Arch Pharm  2004; 337(6): 317-27.
 [http://dx.doi.org/10.1002/ardp.200300817] [PMID:  15188221]

[32]
Cramer J, Kopp S, Bates SE, Chiba P, Ecker GF. Multispecificity of drug transporters: Probing inhibitor selectivity for the human drug efflux transporters ABCB1 and ABCG2. ChemMedChem  2007; 2(12): 1783-8.
 [http://dx.doi.org/10.1002/cmdc.200700160] [PMID:  17994597]

[33]
Schwarz T, Montanari F, Cseke A, et al. Subtle structural differences trigger inhibitory activity of propafenone analogs at the two polyspecific ABC transporters P-glycoprotein and the breast cancer resistance protein. ChemMedChem  2016; 11(12): 1380-94.
 [http://dx.doi.org/10.1002/cmdc.201500592] [PMID:  26970257]

[34]
Broccatelli F, Carosati E, Neri A, et al. A novel approach for predicting P-glycoprotein (ABCB1) inhibition using molecular interaction fields. J Med Chem  2011; 54(6): 1740-51.
 [http://dx.doi.org/10.1021/jm101421d] [PMID:  21341745]

[35]
Chen L, Li Y, Zhao Q, Peng H, Hou T. ADME evaluation in drug discovery. 10. Predictions of P-glycoprotein inhibitors using recursive partitioning and naive Bayesian classification techniques. Mol Pharm  2011; 8(3): 889-900.
 [http://dx.doi.org/10.1021/mp100465q] [PMID:  21413792]

[36]
Montanari F, Ecker GF. BCRP inhibition: From data collection to ligand-based modeling. Mol Inform  2014; 33(5): 322-31.
 [http://dx.doi.org/10.1002/minf.201400012] [PMID:  27485889]

[37]
Warner DJ, Chen H, Cantin LD, et al. Mitigating the inhibition of human bile salt export pump by drugs: opportunities provided by physicochemical property modulation, in silico modeling, and structural modification. Drug Metab Dispos  2012; 40(12): 2332-41.
 [http://dx.doi.org/10.1124/dmd.112.047068] [PMID:  22961681]

[38]
Jain S, Norinder U, Escher SE, Zdrazil B. Combining in vivo data with in silico predictions for modeling hepatic steatosis by using stratified bagging and conformal prediction. Chem Res Toxicol  2021; 34(2): 656-68.
 [http://dx.doi.org/10.1021/acs.chemrestox.0c00511] [PMID:  33347274]

[39]
Kotsampasakou E, Escher SE, Ecker GF. Linking organic anion transporting polypeptide 1B1 and 1B3 (OATP1B1 and OATP1B3) interaction profiles to hepatotoxicity-the hyperbilirubinemia use case. Eur J Pharm Sci  2017; 100: 9-16.
 [http://dx.doi.org/10.1016/j.ejps.2017.01.002] [PMID:  28063966]

[40]
Türková A, Jain S, Zdrazil B. Integrative data mining, scaffold analysis, and sequential binary classification models for exploring ligand profiles of hepatic organic anion transporting polypeptides. J Chem Inf Model  2019; 59(5): 1811-25.
 [http://dx.doi.org/10.1021/acs.jcim.8b00466] [PMID:  30372058]

[41]
Meixner E, Goldmann U, Sedlyarov V, et al. A substrate-based ontology for human solute carriers. Mol Syst Biol  2020; 16(7)e9652
 [http://dx.doi.org/10.15252/msb.20209652] [PMID:  32697042]

[42]
Burggraaff L, Oranje P, Gouka R, et al. Identification of novel small molecule inhibitors for solute carrier SGLT1 using proteochemometric modeling. J Cheminform  2019; 11(1): 15.
 [http://dx.doi.org/10.1186/s13321-019-0337-8] [PMID:  30767155]

[43]
Montanari F, Knasmüller B, Kohlbacher S, et al. Vienna LiverTox Workspace-a set of machine learning models for prediction of interaction profiles of small molecules with transporters relevant for regulatory agencies. Front Chem  2020; 7: 899.
 [http://dx.doi.org/10.3389/fchem.2019.00899] [PMID:  31998690]

[44]
Szakács G, Annereau JP, Lababidi S, et al. Predicting drug sensitivity and resistance. Cancer Cell  2004; 6(2): 129-37.
 [http://dx.doi.org/10.1016/j.ccr.2004.06.026] [PMID:  15324696]

[45]
Domingos PM. MetaCost: A general method for making classifiers cost-sensitive. Knowledge Discovery and Data Mining  1999; 155-64.

[46]
Ecker GF, Csaszar E, Kopp S, et al. Identification of ligand-binding regions of P-glycoprotein by activated-pharmacophore photoaffinity labeling and MALDI-TOF mass spectrometry. Mol Pharmacol  2002; 61: 637-48.
 [http://dx.doi.org/10.1124/mol.61.3.637] [PMID:  11854445]

[47]
Klepsch F, Chiba P, Ecker GF. Exhaustive sampling of docking poses reveals binding hypotheses for propafenone type inhibitors of P-glycoprotein. PLOS Comput Biol  2011; 7(5)e1002036
 [http://dx.doi.org/10.1371/journal.pcbi.1002036] [PMID:  21589945]

[48]
Sarker S, Weissensteiner R, Steiner I, et al. The high-affinity binding site for tricyclic antidepressants resides in the outer vestibule of the serotonin transporter. Mol Pharmacol  2010; 78(6): 1026-35.
 [http://dx.doi.org/10.1124/mol.110.067538] [PMID:  20829432]

[49]
Richter L, de Graaf C, Sieghart W, et al. Diazepam-bound GABAA receptor models identify new benzodiazepine binding-site ligands. Nat Chem Biol  2012; 8(5): 455-64.
 [http://dx.doi.org/10.1038/nchembio.917] [PMID:  22446838]

[50]
Ferreira RJ, Ferreira MJU, dos Santos DJVA. Molecular docking characterizes substrate-binding sites and efflux modulation mechanisms within P-glycoprotein. J Chem Inf Model  2013; 53(7): 1747-60.
 [http://dx.doi.org/10.1021/ci400195v] [PMID:  23802684]

[51]
Ferreira RJ, Ferreira MJU, dos Santos DJVA. Insights on P-glycoprotein’s efflux mechanism obtained by molecular dynamics simulations. J Chem Theory Comput  2012; 8(6): 1853-64.
 [http://dx.doi.org/10.1021/ct300083m] [PMID:  26593820]

[52]
Jackson SM, Manolaridis I, Kowal J, et al. Structural basis of small-molecule inhibition of human multidrug transporter ABCG2. Nat Struct Mol Biol  2018; 25(4): 333-40.
 [http://dx.doi.org/10.1038/s41594-018-0049-1] [PMID:  29610494]

[53]
Coleman JA, Green EM, Gouaux E. X-ray structures and mechanism of the human serotonin transporter. Nature  2016; 532(7599): 334-9.
 [http://dx.doi.org/10.1038/nature17629] [PMID:  27049939]

[54]
Colas C. Toward a systematic structural and functional annotation of solute carriers transporters—example of the SLC6 and SLC7 families. Front Pharmacol  2020; 11: 1229.
 [http://dx.doi.org/10.3389/fphar.2020.01229] [PMID:  32973497]

[55]
Colas C, Laine E. Targeting solute carrier transporters through functional mapping. Trends Pharmacol Sci  2021; 42(1): 3-6.
 [http://dx.doi.org/10.1016/j.tips.2020.11.005] [PMID:  33234336]

[56]
Gebhardt J, Kiesel M, Riniker S, Hansen N. Combining molecular dynamics and machine learning to predict self-solvation free energies and limiting activity coefficients. J Chem Inf Model  2020; 60(11): 5319-30.
 [http://dx.doi.org/10.1021/acs.jcim.0c00479] [PMID:  32786697]

[57]
Ries B, Linker SM, Hahn DF, König G, Riniker S. Ensembler: A simple package for fast prototyping and teaching molecular simulations. J Chem Inf Model  2021; 61(2): 560-4.
 [http://dx.doi.org/10.1021/acs.jcim.0c01283] [PMID:  33512157]

[58]
Subramanian N, Schumann-Gillett A, Mark AE, O’Mara ML. Probing the pharmacological binding sites of P-glycoprotein using umbrella sampling simulations. J Chem Inf Model  2019; 59(5): 2287-98.
 [http://dx.doi.org/10.1021/acs.jcim.8b00624] [PMID:  30540465]

[59]
Condic-Jurkic K, Subramanian N, Mark AE, O’Mara ML. The reliability of molecular dynamics simulations of the multidrug transporter P-glycoprotein in a membrane environment. PLoS One  2018; 13(1)e0191882
 [http://dx.doi.org/10.1371/journal.pone.0191882] [PMID:  29370310]

[60]
Riniker S, Wang S, Bleiziffer P, Böselt L, Esposito C. Machine learning with and for molecular dynamics simulations. Chimia  2019; 73(12): 1024-7.
 [http://dx.doi.org/10.2533/chimia.2019.1024] [PMID:  31883555]

[61]
Wang S, Riniker S. Chapter 9. Machine learning in the area of molecular dynamics simulations. Artificial Intelligence in Drug Discovery.   2020; pp. 184-214.
 [http://dx.doi.org/10.1039/9781788016841-00184]

[62]
Bleiziffer P, Schaller K, Riniker S. Machine learning of partial charges derived from high-quality quantum-mechanical calculations. J Chem Inf Model  2018; 58(3): 579-90.
 [http://dx.doi.org/10.1021/acs.jcim.7b00663] [PMID:  29461814]

[63]
Riniker S. Molecular Dynamics Fingerprints (MDFP): Machine learning from md data to predict free-energy differences. J Chem Inf Model  2017; 57(4): 726-41.
 [http://dx.doi.org/10.1021/acs.jcim.6b00778] [PMID:  28368113]

[64]
Esposito C, Wang S, Lange UEW, Oellien F, Riniker S. Combining machine learning and molecular dynamics to predict P-glycoprotein substrates. J Chem Inf Model  2020; 60(10): 4730-49.
 [http://dx.doi.org/10.1021/acs.jcim.0c00525] [PMID:  32786699]

[65]
Klepsch F, Vasanthanathan P, Ecker GF. Ligand and structure-based classification models for prediction of P-glycoprotein inhibitors. J Chem Inf Model  2014; 54(1): 218-29.
 [http://dx.doi.org/10.1021/ci400289j] [PMID:  24050383]

[66]
Dolghih E, Bryant C, Renslo AR, Jacobson MP. Predicting binding to p-glycoprotein by flexible receptor docking. PLOS Comput Biol  2011; 7(6)e1002083
 [http://dx.doi.org/10.1371/journal.pcbi.1002083] [PMID:  21731480]

[67]
Chen L, Li Y, Yu H, Zhang L, Hou T. Computational models for predicting substrates or inhibitors of P-glycoprotein. Drug Discov Today  2012; 17(7-8): 343-51.
 [http://dx.doi.org/10.1016/j.drudis.2011.11.003] [PMID:  22119877]

[68]
Jain S, Grandits M, Richter L, Ecker GF. Structure based classification for bile salt export pump (BSEP) inhibitors using comparative structural modeling of human BSEP. J Comput Aided Mol Des  2017; 31(6): 507-21.
 [http://dx.doi.org/10.1007/s10822-017-0021-x] [PMID:  28527154]

[69]
Ozawa N, Shimizu T, Morita R, et al. Transporter database, TP-Search: A web-accessible comprehensive database for research in pharmacokinetics of drugs. Pharm Res  2004; 21(11): 2133-4.
 [http://dx.doi.org/10.1023/B:PHAM.0000048207.11160.d0] [PMID:  15587938]

[70]
Morrissey KM, Wen CC, Johns SJ, Zhang L, Huang S-M, Giacomini KM. The UCSF-FDA TransPortal: A public drug transporter database. Clin Pharmacol Ther  2012; 92(5): 545-6.
 [http://dx.doi.org/10.1038/clpt.2012.44] [PMID:  23085876]

[71]
Papadatos G, Gaulton A, Hersey A, Overington JP. Activity, assay and target data curation and quality in the ChEMBL database. J Comput Aided Mol Des  2015; 29(9): 885-96.
 [http://dx.doi.org/10.1007/s10822-015-9860-5] [PMID:  26201396]

[72]
Zdrazil B, Pinto M, Vasanthanathan P, et al. Annotating human P-Glycoprotein bioassay data. Mol Inform  2012; 31(8): 599-609.
 [http://dx.doi.org/10.1002/minf.201200059] [PMID:  23293680]

[73]
Berthold MR, Cebron N, Dill F, et al. KNIME: The Konstanz Information Miner.  In: Data Analysis, Machine Learning and Applications Studies in Classification, Data Analysis, and Knowledge Organization. Berlin, Heidelberg: Springer 2008.

[74]
Montanari F, Zdrazil B, Digles D, Ecker GF. Selectivity profiling of BCRP versus P-gp inhibition: From automated collection of polypharmacology data to multi-label learning. J Cheminform  2016; 8(1): 7.
 [http://dx.doi.org/10.1186/s13321-016-0121-y] [PMID:  26855674]

[75]
Aniceto N, Freitas AA, Bender A, Ghafourian T. Simultaneous prediction of four ATP-binding cassette transporters’ substrates using multi-label QSAR. Mol Inform  2016; 35(10): 514-28.
 [http://dx.doi.org/10.1002/minf.201600036] [PMID:  27582431]

[76]
Shaikh N, Sharma M, Garg P. Selective fusion of heterogeneous classifiers for predicting substrates of membrane transporters. J Chem Inf Model  2017; 57(3): 594-607.
 [http://dx.doi.org/10.1021/acs.jcim.6b00508] [PMID:  28228010]

[77]
Kickinger S, Seiler A, Ecker GF. Proteochemometric modeling of GABA transporters. ACS Spring 2020 National Meeting & Expo. Philadelphia USA, 2020.

[78]
Zdrazil B, Hellsberg E, Viereck M, Ecker GF. From linked open data to molecular interaction: Studying selectivity trends for ligands of the human serotonin and dopamine transporter. MedChemComm  2016; 7(9): 1819-31.
 [http://dx.doi.org/10.1039/C6MD00207B] [PMID:  27891211]

[79]
Türková A, Zdrazil B. Current advances in studying clinically relevant transporters of the solute carrier (SLC) family by connecting computational modeling and data science. Comput Struct Biotechnol J  2019; 17: 390-405.
 [http://dx.doi.org/10.1016/j.csbj.2019.03.002] [PMID:  30976382]

[80]
Kolmar SS, Grulke CM. The effect of noise on the predictive limit of QSAR models. J Cheminform  2021; 13(1): 92.
 [http://dx.doi.org/10.1186/s13321-021-00571-7] [PMID:  34823605]

[81]
Rácz A, Bajusz D, Héberger K. Intercorrelation limits in molecular descriptor preselection for QSAR/QSPR. Mol Inform  2019; 38(8-9)1800154
 [http://dx.doi.org/10.1002/minf.201800154] [PMID:  30945814]

[82]
Tong W, Hong H, Xie Q, Shi L, Fang H, Perkins R. Assessing QSAR Limitations - A Regulatory Perspective. Curr Computeraided Drug Des  2005; 1(2): 195-205.
 [http://dx.doi.org/10.2174/1573409053585663]

[83]
Langer T, Hoffman RD. Pharmacophores and Pharmacophore Searches.  Hoboken: John Wiley & Sons 2006; p. 395.
 [http://dx.doi.org/10.1002/3527609164]

[84]
Kirchmair J, Wolber G, Laggner C, Langer T. Comparative performance assessment of the conformational model generators omega and catalyst: A large-scale survey on the retrieval of protein-bound ligand conformations. J Chem Inf Model  2006; 46(4): 1848-61.
 [http://dx.doi.org/10.1021/ci060084g] [PMID:  16859316]

[85]
Kirchmair J, Laggner C, Wolber G, Langer T. Comparative analysis of protein-bound ligand conformations with respect to catalyst’s conformational space subsampling algorithms. J Chem Inf Model  2005; 45(2): 422-30.
 [http://dx.doi.org/10.1021/ci049753l] [PMID:  15807508]

[86]
Fourches D, Muratov E, Tropsha A. Trust, but verify: on the importance of chemical structure curation in cheminformatics and QSAR modeling research. J Chem Inf Model  2010; 50(7): 1189-204.
 [http://dx.doi.org/10.1021/ci100176x] [PMID:  20572635]

[87]
Kaserer T, Beck K, Akram M, Odermatt A, Schuster D. Pharmacophore models and pharmacophore-based virtual screening: Concepts and applications exemplified on hydroxysteroid dehydrogenases. Molecules  2015; 20(12): 22799-832.
 [http://dx.doi.org/10.3390/molecules201219880] [PMID:  26703541]

[88]
Schomburg KT, Bietz S, Briem H, Henzler AM, Urbaczek S, Rarey M. Facing the challenges of structure-based target prediction by inverse virtual screening. J Chem Inf Model  2014; 54(6): 1676-86.
 [http://dx.doi.org/10.1021/ci500130e] [PMID:  24851945]

[89]
Qing X, Lee XY, De Raeymaecker J, et al. Pharmacophore modeling: Advances, limitations, and current utility in drug discovery. J Recept  2014; 7: 81-92.

[90]
 Amit R. 7 limitations of molecular docking & computer aided drug design and discovery. 2018. Available From:
          https://amitray.com/7-limitations-of-molecular-docking-computer-aided-drug-design-and-discovery/[Accessed on: March 15, 2023].

[91]
Bopp PA, Buhn JB, Maier HA, Hampe MJ. Scope and limits of molecular simulations. Chem Eng Commun  2008; 195(11): 1437-56.
 [http://dx.doi.org/10.1080/00986440801964137]

[92]
Jumper J, Evans R, Pritzel A, et al. Highly accurate protein structure prediction with Alpha Fold. Nature  2021; 596(7873): 583-9.
 [http://dx.doi.org/10.1038/s41586-021-03819-2] [PMID:  34265844]

[93]
Perrakis A, Sixma TK. AI revolutions in biology. EMBO Rep  2021; 22(11)e54046
 [http://dx.doi.org/10.15252/embr.202154046] [PMID:  34668287]

[94]
Kotsampasakou E, Ecker GF. Predicting drug-induced cholestasis with the help of hepatic transporters-an in silico modeling approach. J Chem Inf Model  2017; 57(3): 608-15.
 [http://dx.doi.org/10.1021/acs.jcim.6b00518] [PMID:  28166633]
Rights & Permissions Print Cite
Journal Information
For Authors
For Editors
For Reviewers
Explore Articles
Open Access
Open Access Articles
For Visitors
DOI https://dx.doi.org/10.2174/2589977515666230508123041	Print ISSN 2589-9775
Publisher Name Bentham Science Publisher	Online ISSN 2589-9783
Current Drug Research Reviews

Ligand- and Structure-based Approaches for Transmembrane Transporter Modeling

Abstract Play Pause

Graphical Abstract

Related Journals

Related Books

Abstract
