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Structure-based molecular modeling in SAR analysis and lead optimization

. 2021 ; 19 () : 1431-1444. [epub] 20210304

Status PubMed-not-MEDLINE Language English Country Netherlands Media electronic-ecollection

Document type Journal Article, Review

Grant support
T 942 Austrian Science Fund FWF - Austria

Links

PubMed 33777339
PubMed Central PMC7979990
DOI 10.1016/j.csbj.2021.02.018
PII: S2001-0370(21)00069-6
Knihovny.cz E-resources

In silico methods like molecular docking and pharmacophore modeling are established strategies in lead identification. Their successful application for finding new active molecules for a target is reported by a plethora of studies. However, once a potential lead is identified, lead optimization, with the focus on improving potency, selectivity, or pharmacokinetic parameters of a parent compound, is a much more complex task. Even though in silico molecular modeling methods could contribute a lot of time and cost-saving by rationally filtering synthetic optimization options, they are employed less widely in this stage of research. In this review, we highlight studies that have successfully used computer-aided SAR analysis in lead optimization and want to showcase sound methodology and easily accessible in silico tools for this purpose.

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Topliss J.G. Utilization of operational schemes for analog synthesis in drug design. J Med Chem. 1972;15:1006–1011. PubMed

Kubinyi H. Free Wilson Analysis. Theory, applications and its relationship to Hansch analysis. Quant Struct-Act Rel. 1988;7:121–133.

Issa N.T., Wathieu H., Ojo A., Byers S.W., Dakshanamurthy S. Drug metabolism in preclinical drug development: a survey of the discovery process, toxicology, and computational tools. Curr Drug Metab. 2017;18(6) PubMed PMC

Schnecke V., Boström J. Computational chemistry-driven decision making in lead generation. Drug Discov Today. 2006;11:43–50. PubMed

Jorgensen W.L. Efficient drug lead discovery and optimization. Acc Chem Res. 2009;42:724–733. PubMed PMC

Levinthal C., Wodak S.J., Kahn P., Dadivanian A.K. Hemoglobin interaction in sickle cell fibers. I: Theoretical approaches to the molecular contacts. Proc Natl Acad Sci USA. 1975;72:1330–1334. PubMed PMC

Kuntz I.D., Blaney J.M., Oatley S.J., Langridge R., Ferrin T.E. A geometric approach to macromolecule-ligand interactions. J Mol Biol. 1982;161:269–288. PubMed

Rudden L.S.P., Degiacomi M.T. Protein docking using a single representation for protein surface, electrostatics, and local dynamics. J Chem Theory Comput. 2019;15(9):5135–5143. PubMed PMC

Leach A.R. Ligand docking to proteins with discrete side-chain flexibility. J Mol Biol. 1994;235:345–356. PubMed

Knegtel R.M., Kuntz I.D., Oshiro C.M. Molecular docking to ensembles of protein structures. J Mol Biol. 1997;266:424–440. PubMed

Sousa S.F., Fernandes P.A., Ramos M.J. Protein–ligand docking: current status and future challenges. Proteins: Struct., Funct Bioinf. 2006;65:15–26. PubMed

Liu M., Wang S. MCDOCK: a Monte Carlo simulation approach to the molecular docking problem. J Comput Aided Mol Des. 1999;13:435–451. PubMed

Morris G.M., Goodsell D.S., Halliday R.S., Huey R., Hart W.E., Belew R.K. Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function. J Comput Chem. 1998;19:1639–1662.

Gardiner E.J., Willett P., Artymiuk P.J. Protein docking using a genetic algorithm. Proteins: Struct., Funct Bioinf. 2001;44:44–56. PubMed

Liu J., Wang R. Classification of current scoring functions. J Chem Inf Model. 2015;55:475–482. PubMed

Ferreira L.G., Dos Santos R.N., Oliva G., Andricopulo A.D. Molecular docking and structure-based drug design strategies. Molecules. 2015;20:13384–13421. PubMed PMC

Li J., Fu A., Zhang L. An overview of scoring functions used for protein-ligand interactions in molecular docking. Interdiscip Sci. 2019;11:320–328. PubMed

Heberlé G., de Azevedo W.F., Jr. Bio-inspired algorithms applied to molecular docking simulations. Curr. Med. Chem. 2011;18:1339–1352. PubMed

Fernandez M., Caballero J., Fernandez L., Sarai A. Genetic algorithm optimization in drug design QSAR: Bayesian-regularized genetic neural networks (BRGNN) and genetic algorithm-optimized support vectors machines (GA-SVM) Mol Divers. 2011;15(1):269–289. PubMed

Dias R., de Azevedo W.F., Jr. Molecular docking algorithms. Curr. Drug Targets. 2008;9:1040–1047. PubMed

Brooijmans N., Kuntz I.D. Molecular recognition and docking algorithms. Annu Rev Biophys Biomol Struct. 2003;32:335–373. PubMed

Willett P. Genetic algorithms in molecular recognition and design. Trends Biotechnol. 1995;13:516–521. PubMed

Li H., Sze K.-H., Lu G., Ballester P.J. Machine-learning scoring functions for structure-based drug lead optimization. WIREs Comput Mol Sci. 2020;10

Shen C., Ding J., Wang Z., Cao D., Ding X., Hou T. From machine learning to deep learning: Advances in scoring functions for protein–ligand docking. WIREs Comput Mol Sci. 2020;10(1) doi: 10.1002/wcms.v10.110.1002/wcms.1429. DOI

Adeniyi A.A., Soliman M.E.S. Implementing QM in docking calculations: is it a waste of computational time? Drug Discov Today. 2017;22:1216–1223. PubMed

Liu Z., Su M., Han L., Liu J., Yang Q., Li Y. Forging the basis for developing protein-ligand interaction scoring functions. Acc Chem Res. 2017;50:302–309. PubMed

Berman H.M., Westbrook J., Feng Z., Gilliland G., Bhat T.N., Weissig H. The protein data bank. Nucleic Acids Res. 2000;28:235–242. PubMed PMC

Kirchmair J., Markt P., Distinto S., Schuster D., Spitzer G.M., Liedl K.R. The protein data bank (PDB), its related services and software tools as key components for in silico guided drug discovery. J Med Chem. 2008;51:7021–7040. PubMed

Friedrich N.-O., Meyder A., de Bruyn Kops C., Sommer K., Flachsenberg F., Rarey M. High-quality dataset of protein-bound ligand conformations and its application to benchmarking conformer ensemble generators. J Chem Inf Model. 2017;57:529–539. PubMed

Pagadala N.S., Syed K., Tuszynski J. Software for molecular docking: a review. Biophys Rev. 2017;9:91–102. PubMed PMC

Luo J., Wei W., Waldispühl J., Moitessier N. Challenges and current status of computational methods for docking small molecules to nucleic acids. Eur J Med Chem. 2019;168:414–425. PubMed

Wang Z., Sun H., Yao X., Li D., Xu L., Li Y. Comprehensive evaluation of ten docking programs on a diverse set of protein–ligand complexes: the prediction accuracy of sampling power and scoring power. Phys Chem Chem Phys. 2016;18(18):12964–12975. PubMed

Agrawal P., Singh H., Srivastava H.K., Singh S., Kishore G., Raghava G.P.S. Benchmarking of different molecular docking methods for protein-peptide docking. BMC Bioinf. 2019;19:426. PubMed PMC

Weng G., Gao J., Wang Z., Wang E., Hu X., Yao X. Comprehensive evaluation of fourteen docking programs on protein-peptide complexes. J Chem Theory Comput. 2020;16(6):3959–3969. PubMed

Çınaroğlu S.S., Timuçin E. Comparative assessment of seven docking programs on a nonredundant metalloprotein subset of the PDBbind refined. J Chem Inf Model. 2019;59:3846–3859. PubMed

Hevener K.E., Zhao W., Ball D.M., Babaoglu K., Qi J., White S.W. Validation of molecular docking programs for virtual screening against dihydropteroate synthase. J Chem Inf Model. 2009;49:444–460. PubMed PMC

Castro-Alvarez A., Costa A.M., Vilarrasa J. The performance of several docking programs at reproducing protein-macrolide-like crystal structures. Molecules. 2017;22:136. PubMed PMC

Mukherjee S., Balius T.E., Rizzo R.C. Docking validation resources: protein family and ligand flexibility experiments. J Chem Inf Model. 2010;50(11):1986–2000. PubMed PMC

The PyMOL Molecular Graphics System, Version 2.4.1, Schrödinger, LLC.

Huang N., Shoichet B.K., Irwin J.J. Benchmarking sets for molecular docking. J Med Chem. 2006;49:6789–6801. PubMed PMC

Triballeau N., Acher F., Brabet I., Pin J.-P., Bertrand H.-O. Virtual screening workflow development guided by the “receiver operating characteristic” curve approach. application to high-throughput docking on metabotropic glutamate receptor subtype 4. J Med Chem. 2005;48:2534–2547. PubMed

Zhao Y., Li Y. Design of environmentally friendly neonicotinoid insecticides with bioconcentration tuning and Bi-directional selective toxic effects. J Clean Prod. 2019;221:113–121.

Gu W., Li Q., Li Y. Environment-friendly PCN derivatives design and environmental behavior simulation based on a multi-activity 3D-QSAR model and molecular dynamics. J Hazard Mater. 2020;393:122339. doi: 10.1016/j.jhazmat.2020.122339. PubMed DOI

Qiu Y., Zhang S., Li Y. High ultraviolet sensitivity of phthalic acid esters with environmental friendliness after modification through pharmacophore modeling associated with the solvation effect. Pol J Environ Stud. 2020;29(3):2303–2316.

Enyedy I.J., Egan W.J. Can we use docking and scoring for hit-to-lead optimization? J Comput Aided Mol Des. 2008;22:161–168. PubMed

Šinko G. Assessment of scoring functions and in silico parameters for AChE-ligand interactions as a tool for predicting inhibition potency. Chem-Biol Interact. 2019;308:216–223. PubMed

Pein H., Ville A., Pace S., Temml V., Garscha U., Raasch M. Endogenous metabolites of vitamin E limit inflammation by targeting 5-lipoxygenase. Nat Commun. 2018;9:3834. PubMed PMC

Cheung S.-Y., Werner M., Esposito L., Troisi F., Cantone V., Liening S. Discovery of a benzenesulfonamide-based dual inhibitor of microsomal prostaglandin E2 synthase-1 and 5-lipoxygenase that favorably modulates lipid mediator biosynthesis in inflammation. Eur J Med Chem. 2018;156:815–830. PubMed

Levoin N., Calmels T., Poupardin-Olivier O., Labeeuw O., Danvy D., Robert P. Refined docking as a valuable tool for lead optimization: application to histamine h3 receptor antagonists. Arch Pharm (Weinheim) 2008;341(10):610–623. PubMed

Galindez G., Matschinske J., Rose T.D., Sadegh S., Salgado-Albarrán M., Späth J. Lessons from the COVID-19 pandemic for advancing computational drug repurposing strategies. Nat Comput Sci. 2021;1:33–41. PubMed

Komura H, Watanabe R, Kawashima H, Ohashi R, Kuroda M, Sato T, Honma T, Mizuguchi K. (2021) A public–private partnership to enrich the development of in silico predictive models for pharmacokinetic and cardiotoxic properties, Drug Discov. Today. In Press. PubMed

Ekins S., Mottin M., Ramos P.R.P.S., Sousa B.K.P., Neves B.J., Foil D.H. Déjà vu: stimulating open drug discovery for SARS-CoV-2. Drug Discov Today. 2020;25:928–941. PubMed PMC

Mitusińska K., Raczyńska A., Bzówka M., Bagrowska W., Góra A. Applications of water molecules for analysis of macromolecule properties. Comput Struct Biotechnol J. 2020;18:355–365. PubMed PMC

Banchi L., Fingerhuth M., Babej T., Ing C., Arrazola J.M. Molecular docking with Gaussian Boson Sampling. Science Advances. 2020;6 eaax1950. PubMed PMC

Wang E., Sun H., Wang J., Wang Z., Liu H., Zhang J.Z.H. End-point binding free energy calculation with MM/PBSA and MM/GBSA: strategies and applications in drug design. Chem Rev. 2019;119(16):9478–9508. PubMed

Bhhatarai B., Walters W.P., Hop C., Lanza G., Ekins S. Opportunities and challenges using artificial intelligence in ADME/Tox. Nat Mater. 2019;18:418–422. PubMed PMC

Kazmi S.R., Jun R., Yu M.-S., Jung C., Na D. In silico approaches and tools for the prediction of drug metabolism and fate: a review. Comput Biol Med. 2019;106:54–64. PubMed

Ehmki E.S.R., Rarey M. Exploring structure-activity relationships with three-dimensional matched molecular pairs-a review. Chem Med Chem. 2018;13:482–489. PubMed

Wang S.H., Yu J. Structure-based design for binding peptides in anti-cancer therapy. Biomaterials. 2018;156:1–15. PubMed

Réau M., Langenfeld F., Zagury J.-F., Lagarde N., Montes M. Decoys selection in benchmarking datasets: overview and perspectives. Front Pharmacol. 2018;9:11. PubMed PMC

Sotriffer C. Docking of covalent ligands: challenges and approaches. Mol Inform. 2018;37(9-10):1800062. doi: 10.1002/minf.v37.9-1010.1002/minf.201800062. PubMed DOI

Defelipe L., Arcon J., Modenutti C., Marti M., Turjanski A., Barril X. Solvents to fragments to drugs: MD applications in drug design. Molecules. 2018;23(12):3269. doi: 10.3390/molecules23123269. PubMed DOI PMC

Ciemny M., Kurcinski M., Kamel K., Kolinski A., Alam N., Schueler-Furman O. Protein–peptide docking: opportunities and challenges. Drug Discov Today. 2018;23:1530–1537. PubMed

Riccardi L., Genna V., De Vivo M. Metal–ligand interactions in drug design. Nat Rev Chem. 2018;2:100–112.

Amaro R.E., Baudry J., Chodera J., Demir Ö., McCammon J.A., Miao Y. Ensemble docking in drug discovery. Biophys J. 2018;114:2271–2278. PubMed PMC

Salmaso V., Moro S. Bridging molecular docking to molecular dynamics in exploring ligand-protein recognition process: an overview. Front Pharmacol. 2018:9. PubMed PMC

Stefaniak F., Chudyk E.I., Bodkin M., Dawson W.K., Bujnicki J.M. Modeling of ribonucleic acid–ligand interactions, WIREs Comput. Mol Sci. 2015;5(6):425–439.

Kirchmair J., Göller A.H., Lang D., Kunze J., Testa B., Wilson I.D. Predicting drug metabolism: experiment and/or computation? Nat Rev Drug Discov. 2015;14(6):387–404. PubMed

Granchi C., Rizzolio F., Palazzolo S., Carmignani S., Macchia M., Saccomanni G. Structural optimization of 4-chlorobenzoylpiperidine derivatives for the development of potent, reversible, and selective monoacylglycerol lipase (MAGL) inhibitors. J Med Chem. 2016;59:10299–10314. PubMed

Baell J.B., Holloway G.A. New substructure filters for removal of pan assay interference compounds (PAINS) from screening libraries and for their exclusion in bioassays. J Med Chem. 2010;53:2719–2740. PubMed

Baell J.B., Nissink J.W.M. Seven year itch: pan-assay interference compounds (PAINS) in 2017—utility and limitations. ACS Chem Biol. 2018;13(1):36–44. PubMed PMC

Salentin S., Schreiber S., Haupt V.J., Adasme M.F., Schroeder M. PLIP: fully automated protein-ligand interaction profiler. Nucleic Acids Res. 2015;43:W443–W447. PubMed PMC

Stornaiuolo M., La Regina G., Passacantilli S., Grassia G., Coluccia A., La Pietra V. Structure-based lead optimization and biological evaluation of BAX direct activators as novel potential anticancer agents. J Med Chem. 2015;58:2135–2148. PubMed

Daina A., Michielin O., Zoete V. SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci Rep. 2017;7:42717. PubMed PMC

Djoumbou-Feunang Y., Fiamoncini J., Gil-de-la-Fuente A., Greiner R., Manach C., Wishart D.S. BioTransformer: a comprehensive computational tool for small molecule metabolism prediction and metabolite identification. J Cheminform. 2019;11:2. PubMed PMC

Ramachandran S.A., Jadhavar P.S., Miglani S.K., Singh M.P., Kalane D.P., Agarwal A.K. Design, synthesis and optimization of bis-amide derivatives as CSF1R inhibitors. Bioorg Med Chem Lett. 2017;27:2153–2160. PubMed

Kawada H., Ebiike H., Tsukazaki M., Nakamura M., Morikami K., Yoshinari K. Lead optimization of a dihydropyrrolopyrimidine inhibitor against phosphoinositide 3-kinase (PI3K) to improve the phenol glucuronic acid conjugation. Bioorg Med Chem Lett. 2013;23:673–678. PubMed

Giustiniano M., Daniele S., Pelliccia S., La Pietra V., Pietrobono D., Brancaccio D. Computer-aided identification and lead optimization of dual murine double minute 2 and 4 binders: structure-activity relationship studies and pharmacological activity. J Med Chem. 2017;60:8115–8130. PubMed

Snow O., Lallous N., Singh K., Lack N., Rennie P., Cherkasov A. Androgen receptor plasticity and its implications for prostate cancer therapy. Cancer Treat Rev. 2019:81. PubMed

Ban F., Dalal K., Li H., LeBlanc E., Rennie P.S., Cherkasov A. Best practices of computer-aided drug discovery: lessons learned from the development of a preclinical candidate for prostate cancer with a new mechanism of action. J Chem Inf Model. 2017;57(5):1018–1028. PubMed

Dalal K., Ban F., Li H., Morin H., Roshan-Moniri M., Tam K.J. Selectively targeting the dimerization interface of human androgen receptor with small-molecules to treat castration-resistant prostate cancer. Cancer Lett. 2018;437:35–43. PubMed

Lyu J., Wang S., Balius T.E., Singh I., Levit A., Moroz Y.S. Ultra-large library docking for discovering new chemotypes. Nature. 2019;566:224–229. PubMed PMC

Trani G., Barker J.J., Bromidge S.M., Brookfield F.A., Burch J.D., Chen Y. Design, synthesis and structure-activity relationships of a novel class of sulfonylpyridine inhibitors of Interleukin-2 inducible T-cell kinase (ITK) Bioorg Med Chem Lett. 2014;24(24):5818–5823. PubMed

Alder C.M., Ambler M., Campbell A.J., Champigny A.C., Deakin A.M., Harling J.D. Identification of a novel and selective series of Itk inhibitors via a template-hopping strategy. ACS Med Chem Lett. 2013;4:948–952. PubMed PMC

Zou Y., Li L., Chen W., Chen T., Ma L., Wang X. Virtual screening and structure-based discovery of indole acylguanidines as potent beta-secretase (BACE1) inhibitors. Molecules. 2013;18:5706–5722. PubMed PMC

Joseph-McCarthy D., Parris K., Huang A., Failli A., Quagliato D., Dushin E.G. Use of structure-based drug design approaches to obtain novel anthranilic acid acyl carrier protein synthase inhibitors. J Med Chem. 2005;48:7960–7969. PubMed

Hoffer L., Voitovich Y.V., Raux B., Carrasco K., Muller C., Fedorov A.Y. Integrated strategy for lead optimization based on fragment growing: the diversity-oriented-target-focused-synthesis approach. J Med Chem. 2018;61:5719–5732. PubMed

Yan S., Elmes M.W., Tong S., Hu K., Awwa M., Teng G.Y.H. SAR studies on truxillic acid mono esters as a new class of antinociceptive agents targeting fatty acid binding proteins. Eur J Med Chem. 2018;154:233–252. PubMed PMC

Ranise A., Spallarossa A., Schenone S., Bruno O., Bondavalli F., Vargiu L. Design, synthesis, SAR, and molecular modeling studies of acylthiocarbamates: a novel series of potent non-nucleoside HIV-1 reverse transcriptase inhibitors structurally related to phenethylthiazolylthiourea derivatives. J Med Chem. 2003;46:768–781. PubMed

Mai A., Sbardella G., Artico M., Ragno R., Massa S., Novellino E. Structure-based design, synthesis, and biological evaluation of conformationally restricted novel 2-alkylthio-6-[1-(2,6-difluorophenyl)alkyl]-3,4-dihydro-5-alkylpyrimidin-4(3H)-on es as non-nucleoside inhibitors of HIV-1 reverse transcriptase. J Med Chem. 2001;44:2544–2554. PubMed

Jeankumar V.U., Renuka J., Kotagiri S., Saxena S., Kakan S.S., Sridevi J.P. Gyrase ATPase domain as an antitubercular drug discovery platform: structure-based design and lead optimization of nitrothiazolyl carboxamide analogues. Chem Med Chem. 2014;9:1850–1859. PubMed

Wu P., Ma D.-L., Leung C.-H., Yan S.-C., Zhu N., Abagyan R. Stabilization of G-quadruplex DNA with platinum(II) Schiff base complexes: luminescent probe and down-regulation of c-myc oncogene expression. Chemistry. 2009;15(47):13008–13021. PubMed

Crawford T.D., Ndubaku C.O., Chen H., Boggs J.W., Bravo B.J., Delatorre K. Discovery of selective 4-Amino-pyridopyrimidine inhibitors of MAP4K4 using fragment-based lead identification and optimization. J Med Chem. 2014;57:3484–3493. PubMed

Poulsen A., Williams M., Nagaraj H.M., William A.D., Wang H., Soh C.K. Structure-based optimization of morpholino-triazines as PI3K and mTOR inhibitors. Bioorg Med Chem Lett. 2012;22:1009–1013. PubMed

Zhu J., Huang J.W., Tseng P.H., Yang Y.T., Fowble J., Shiau C.W. From the cyclooxygenase-2 inhibitor celecoxib to a novel class of 3-phosphoinositide-dependent protein kinase-1 inhibitors. Cancer res. 2004;64:4309–4318. PubMed

Xia Z., Knaak C., Ma J., Beharry Z.M., McInnes C., Wang W. Synthesis and evaluation of novel inhibitors of Pim-1 and Pim-2 protein kinases. J Med Chem. 2009;52:74–86. PubMed PMC

Chen Y., Wen D., Huang Z., Huang M., Luo Y., Liu B. 2-(4-Chlorophenyl)-2-oxoethyl 4-benzamidobenzoate derivatives, a novel class of SENP1 inhibitors: virtual screening, synthesis and biological evaluation. Bioorg Med Chem Lett. 2012;22(22):6867–6870. PubMed

Iwata Y., Arisawa M., Hamada R., Kita Y., Mizutani M.Y., Tomioka N. Discovery of novel aldose reductase inhibitors using a protein structure-based approach: 3D-database search followed by design and synthesis. J Med Chem. 2001;44:1718–1728. PubMed

Ramunno A., Cosconati S., Sartini S., Maglio V., Angiuoli S., La Pietra V. Progresses in the pursuit of aldose reductase inhibitors: the structure-based lead optimization step. Eur J Med Chem. 2012;51:216–226. PubMed

Rakse M., Karthikeyan C., Deora G.S., Moorthy N.S., Rathore V., Rawat A.K. Design, synthesis and molecular modelling studies of novel 3-acetamido-4-methyl benzoic acid derivatives as inhibitors of protein tyrosine phosphatase 1B. Eur J Med Chem. 2013;70:469–476. PubMed

Moretto A.F., Kirincich S.J., Xu W.X., Smith M.J., Wan Z.K., Wilson D.P. Bicyclic and tricyclic thiophenes as protein tyrosine phosphatase 1B inhibitors. Bioorg Med Chem. 2006;14:2162–2177. PubMed

Wityak J., McGee K.F., Conlon M.P., Song R.H., Duffy B.C., Clayton B. Lead optimization toward proof-of-concept tools for huntington’s disease within a 4-(1h-pyrazol-4-yl)pyrimidine class of Pan-JNK inhibitors. J Med Chem. 2015;58:2967–2987. PubMed

Bajaj K., Burudkar S., Shah P., Keche A., Ghosh U., Tannu P. Lead optimization of isocytosine-derived xanthine oxidase inhibitors. Bioorg Med Chem Lett. 2013;23:834–838. PubMed

Khanna S., Burudkar S., Bajaj K., Shah P., Keche A., Ghosh U. Isocytosine-based inhibitors of xanthine oxidase: design, synthesis, SAR, PK and in vivo efficacy in rat model of hyperuricemia. Bioorg Med Chem Lett. 2012;22(24):7543–7546. PubMed

Kumar G., Parasuraman P., Sharma S.K., Banerjee T., Karmodiya K., Surolia N. Discovery of a rhodanine class of compounds as inhibitors of Plasmodium falciparum enoyl-acyl carrier protein reductase. J Med Chem. 2007;50:2665–2675. PubMed

La Pietra V., Marinelli L., Cosconati S., Di Leva F.S., Nuti E., Santamaria S. Identification of novel molecular scaffolds for the design of MMP-13 inhibitors: a first round of lead optimization. Eur J Med Chem. 2012;47:143–152. PubMed

Wu B., Murray J.K., Andrews K.L., Sham K., Long J., Aral J. Discovery of tarantula venom-derived NaV1.7-inhibitory JzTx-V peptide 5-Br-Trp24 analogue AM-6120 with systemic block of histamine-induced pruritis. J Med Chem. 2018;61:9500–9512. PubMed

Yang S.-Y. Pharmacophore modeling and applications in drug discovery: challenges and recent advances, Drug Discov. Today. 2010;15(11-12):444–450. PubMed

Wermuth CG, Ganellin CR, Lindberg P, Mitscher LA. Glossary of terms used in medicinal chemistry (IUPAC Recommendations 1998); 1998, 70, 1129.

Wolber G., Langer T. LigandScout: 3-D pharmacophores derived from protein-bound ligands and their use as virtual screening filters. J Chem Inf Model. 2005;45:160–169. PubMed

Schneider G., Neidhart W., Giller T., Schmid G. “Scaffold-hopping” by topological pharmacophore search: a contribution to virtual screening. Angew Chem Int. 1999;38:2894–2896. PubMed

Schaller D., Šribar D., Noonan T., Deng L., Nguyen T.N., Pach S. Next generation 3D pharmacophore modeling. WIREs Comput Mol Sci. 2020;10

Dixon S.L., Smondyrev A.M., Rao S.N. PHASE: a novel approach to pharmacophore modeling and 3D database searching. Chem Biol Drug Des. 2006;67(5):370–372. PubMed

Temml V., Kaserer T., Kutil Z., Landa P., Vanek T., Schuster D. Pharmacophore modeling for COX-1 and -2 inhibitors with LigandScout in comparison to Discovery Studio. Future Med Chem. 2014;6:1869–1881. PubMed

Kim M., Park K., Kim W., Jung S., Cho A.E. Target-specific drug design method combining deep learning and water pharmacophore. J Chem Inf Model. 2021;61:36–45. PubMed

Brown BP, Mendenhall J, Geanes AR, Meiler J. General Purpose Structure-Based drug discovery neural network score functions with human-interpretable pharmacophore maps, J. Chem. Inf. Model; 2021. PubMed PMC

Seidel T., Wieder O., Garon A., Langer T. Applications of the pharmacophore concept in natural product inspired drug design. Mol Inform. 2020;39:2000059. PubMed PMC

Wieder M., Garon A., Perricone U., Boresch S., Seidel T., Almerico A.M. Common hits approach: combining pharmacophore modeling and molecular dynamics simulations. J Chem Inf Model. 2017;57(2):365–385. PubMed

Hu Y., Stumpfe D., Bajorath J. Recent advances in scaffold hopping. J Med Chem. 2017;60:1238–1246. PubMed

Putz M.V., Duda-Seiman C., Duda-Seiman D., Putz A.-M., Alexandrescu I., Mernea M. Chemical structure-biological activity models for pharmacophores’ 3D-interactions. Int J Mol Sci. 2016;17:1087. PubMed PMC

Van Drie J.H. Generation of three-dimensional pharmacophore models. WIREs Comput Mol Sci. 2013;3(5):449–464.

Guner O.F., Bowen J.P. Pharmacophore modeling for ADME. Curr Top Med Chem. 2013;13:1327–1342. PubMed

Thai K.M., Ngo T.D., Tran T.D., Le M.T. Pharmacophore modeling for antitargets. Curr Top Med Chem. 2013;13:1002–1014. PubMed

Sanders M.P.A., McGuire R., Roumen L., de Esch I.J.P., de Vlieg J., Klomp J.P.G. From the protein's perspective: the benefits and challenges of protein structure-based pharmacophore modeling. Med Chem Comm. 2012;3(1):28–38.

Caporuscio F., Tafi A. Pharmacophore modelling: a forty year old approach and its modern synergies. Curr Med Chem. 2011;18:2543–2553. PubMed

Shim J., Mackerell A.D., Jr. Computational ligand-based rational design: role of conformational sampling and force fields in model development. Med Chem Comm. 2011;2:356–370. PubMed PMC

Wolber G., Seidel T., Bendix F., Langer T. Molecule-pharmacophore superpositioning and pattern matching in computational drug design. Drug Discov Today. 2008;13:23–29. PubMed

Ekins S., Mirny L., Schuetz E.G. A ligand-based approach to understanding selectivity of nuclear hormone receptors PXR, CAR, FXR, LXRα, and LXRβ. Pharm Res. 2002;19:1788–1800. PubMed

Zhu X., Kim J.L., Newcomb J.R., Rose P.E., Stover D.R., Toledo L.M. Structural analysis of the lymphocyte-specific kinase Lck in complex with non-selective and Src family selective kinase inhibitors. Structure. 1999;7:651–661. PubMed

Lawrie A.M., Noble M.E.M., Tunnah P., Brown N.R., Johnson L.N., Endicott J.A. Protein kinase inhibition by staurosporine revealed in details of the molecular interaction with CDK2. Nat Struct Biol. 1997;4:796–801. PubMed

Vuorinen A., Schuster D. Methods for generating and applying pharmacophore models as virtual screening filters and for bioactivity profiling. Methods. 2015;71:113–134. PubMed

Kirchmair J., Markt P., Distinto S., Wolber G., Langer T. Evaluation of the performance of 3D virtual screening protocols: RMSD comparisons, enrichment assessments, and decoy selection—what can we learn from earlier mistakes? J Comput Aided Mol Des. 2008;22(3-4):213–228. PubMed

Jacobsson M., Lidén P., Stjernschantz E., Boström H., Norinder U. Improving structure-based virtual screening by multivariate analysis of scoring data. J Med Chem. 2003;46:5781–5789. PubMed

Vuorinen A., Nashev L.G., Odermatt A., Rollinger J.M., Schuster D. Pharmacophore model refinement for 11β-hydroxysteroid dehydrogenase inhibitors: search for modulators of intracellular glucocorticoid concentrations. Mol Inform. 2014;33(1):15–25. PubMed

Kim K., Kwon H., Barinka C., Motlova L., Nam S., Choi D. Novel β- and γ-amino acid-derived inhibitors of prostate-specific membrane antigen. J Med Chem. 2020;63:3261–3273. PubMed

Schneider G., Fechner U. Computer-based de novo design of drug-like molecules. Nat Rev Drug Discov. 2005;4(8):649–663. PubMed

Huang Q., Li L.-L., Yang S.-Y. PhDD: A new pharmacophore-based de novo design method of drug-like molecules combined with assessment of synthetic accessibility. J Mol Graph Model. 2010;28(8):775–787. PubMed

Temml V., Garscha U., Romp E., Schubert G., Gerstmeier J., Kutil Z. Discovery of the first dual inhibitor of the 5-lipoxygenase-activating protein and soluble epoxide hydrolase using pharmacophore-based virtual screening. Sci Rep. 2017;7(1) doi: 10.1038/srep42751. PubMed DOI PMC

Meirer K, Steinhilber D, Proschak E. Inhibitors of the Arachidonic Acid Cascade: Interfering with Multiple Pathways; 2014. 114, 83–91. PubMed

Brunger A.T., Jin R., Breidenbach M.A. Highly specific interactions between botulinum neurotoxins and synaptic vesicle proteins. Cell Mol Life Sci. 2008;65:2296–2306. PubMed PMC

Burnett J.C., Wang C., Nuss J.E., Nguyen T.L., Hermone A.R., Schmidt J.J. Pharmacophore-guided lead optimization: the rational design of a non-zinc coordinating, sub-micromolar inhibitor of the botulinum neurotoxin serotype a metalloprotease. Bioorg Med Chem Lett. 2009;19(19):5811–5813. PubMed

Hermone A.R., Burnett J.C., Nuss J.E., Tressler L.E., Nguyen T.L., Solaja B.A. Three-dimensional database mining identifies a unique chemotype that unites structurally diverse botulinum neurotoxin serotype A inhibitors in a three-zone pharmacophore. Chem Med Chem. 2008;3:1905–1912. PubMed

Fu L., Wang S., Wang X., Wang P., Zheng Y., Yao D. Crystal structure-based discovery of a novel synthesized PARP1 inhibitor (OL-1) with apoptosis-inducing mechanisms in triple-negative breast cancer. Sci Rep. 2016;6(1) doi: 10.1038/s41598-016-0007-2. PubMed DOI PMC

Stjernschantz E., Marelius J., Medina C., Jacobsson M., Vermeulen N.P.E., Oostenbrink C. Are automated molecular dynamics simulations and binding free energy calculations realistic tools in lead optimization? an evaluation of the linear interaction energy (LIE) method. J Chem Inf Model. 2006;46(5):1972–1983. PubMed

Wieder M., Perricone U., Seidel T., Boresch S., Langer T. Comparing pharmacophore models derived from crystal structures and from molecular dynamics simulations. Monatsh Chem. 2016;147:553–563. PubMed PMC

Shan J., Zheng J.J. Optimizing Dvl PDZ domain inhibitor by exploring chemical space. J Comput Aided Mol Des. 2009;23:37–47. PubMed PMC

Shan J., Shi D.-L., Wang J., Zheng J. Identification of a specific inhibitor of the dishevelled PDZ domain. Biochemistry. 2005;44:15495–15503. PubMed

Masuzaki H., Yamamoto H., Kenyon C.J., Elmquist J.K., Morton N.M., Paterson J.M. Transgenic amplification of glucocorticoid action in adipose tissue causes high blood pressure in mice. J Clin Invest. 2003;112(1):83–90. PubMed PMC

Masuzaki H., Paterson J., Shinyama H., Morton N.M., Mullins J.J., Seckl J.R. A transgenic model of visceral obesity and the metabolic syndrome. Science. 2001;294:2166–2170. PubMed

Paterson J.M., Morton N.M., Fievet C., Kenyon C.J., Holmes M.C., Staels B. Metabolic syndrome without obesity: Hepatic overexpression of 11beta-hydroxysteroid dehydrogenase type 1 in transgenic mice. Proc Natl Acad Sci USA. 2004;101:7088–7093. PubMed PMC

Schuster D., Maurer E.M., Laggner C., Nashev L.G., Wilckens T., Langer T. The discovery of new 11β-hydroxysteroid dehydrogenase type 1 inhibitors by common feature pharmacophore modeling and virtual screening. J Med Chem. 2006;49:3454–3466. PubMed

Kratschmar D.V., Vuorinen A., Da Cunha T., Wolber G., Classen-Houben D., Doblhoff O. Characterization of activity and binding mode of glycyrrhetinic acid derivatives inhibiting 11β-hydroxysteroid dehydrogenase type 2. J Steroid Biochem Mol Biol. 2011;125:129–142. PubMed

Chiang Y.K., Kuo C.C., Wu Y.S., Chen C.T., Coumar M.S., Wu J.S. Generation of ligand-based pharmacophore model and virtual screening for identification of novel tubulin inhibitors with potent anticancer activity. J Med Chem. 2009;52:4221–4233. PubMed

Stanton R.A., Lu X., Detorio M., Montero C., Hammond E.T., Ehteshami M. Discovery, characterization, and lead optimization of 7-azaindole non-nucleoside HIV-1 reverse transcriptase inhibitors. Bioorg Med Chem Lett. 2016;26:4101–4105. PubMed PMC

Zhu K., Jiang C., Tao H., Liu J., Zhang H., Luo C. Identification of a novel selective small-molecule inhibitor of protein arginine methyltransferase 5 (PRMT5) by virtual screening, resynthesis and biological evaluations. Bioorg Med Chem Lett. 2018;28(9):1476–1483. PubMed

Yang L.L., Li G.B., Yan H.X., Sun Q.Z., Ma S., Ji P. Discovery of N6-phenyl-1H-pyrazolo[3,4-d]pyrimidine-3,6-diamine derivatives as novel CK1 inhibitors using common-feature pharmacophore model based virtual screening and hit-to-lead optimization. Eur J Med Chem. 2012;56:30–38. PubMed

Ranganathan A., Stoddart L.A., Hill S.J., Carlsson J. Fragment-based discovery of subtype-selective adenosine receptor ligands from homology models. J Med Chem. 2015;58:9578–9590. PubMed

Da Settimo F., Primofiore G., Taliani S., Marini A.M., La Motta C., Novellino E. 3-Aryl[1,2,4]triazino[4,3-a]benzimidazol-4(10H)-ones: a new class of selective A1 adenosine receptor antagonists. J Med Chem. 2001;44:316–327. PubMed

Huang P., Loew G.H., Funamizu H., Mimura M., Ishiyama N., Hayashida M. Rational design, discovery, and synthesis of a novel series of potent growth hormone secretagogues. J Med Chem. 2001;44:4082–4091. PubMed

Chhabria M.T., Brahmkshatriya P.S., Mahajan B.M., Darji U.B., Shah G.B. Discovery of novel acyl coenzyme a: cholesterol acyltransferase inhibitors: pharmacophore-based virtual screening, synthesis and pharmacology. Chem Biol Drug Des. 2012;80:106–113. PubMed

Choong I.C., Lew W., Lee D., Pham P., Burdett M.T., Lam J.W. Identification of potent and selective small-molecule inhibitors of caspase-3 through the use of extended tethering and structure-based drug design. J Med Chem. 2002;45:5005–5022. PubMed

Tian S., Wang X., Li L., Zhang X., Li Y., Zhu F. Discovery of novel and selective adenosine A2A receptor antagonists for treating Parkinson's disease through comparative structure-based virtual screening. J Chem Inf Model. 2017;57:1474–1487. PubMed

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