GPCRLigNet: rapid screening for GPCR active ligands using machine learning

Sriram K, Insel PAG Protein-Coupled Receptors as Targets for Approved Drugs: How Many Targets and How Many Drugs?Mol Pharmacol2018, 93 (4),251. https://doi.org/10.1124/mol.117.111062

Kratochwil NA, Malherbe P, Lindemann L, Ebeling M, Hoener MC, Mühlemann A, Porter RHP, Stahl M, Gerber PR An Automated System for the Analysis of G Protein-Coupled Receptor Transmembrane Binding Pockets: Alignment, Receptor-Based Pharmacophores, and Their Application.J. Chem. Inf. Model2005, 45 (5),1324–1336. https://doi.org/10.1021/ci050221u

Porter HP, Steward R, Kolczewski L, Panousis SG, Narquizian C, Hertel R, Grether C, Dehmlow U, Winnig H, Slack MP, Kratochwil JA, Malherbe N, Martin PE, Guba R, Green WG, Christ LD, Lindemann A, Hoener LC, Gatti-McArthur M S. G Protein-Coupled Receptor Transmembrane Binding Pockets and Their Applications in GPCR Research and Drug Discovery: A Survey.Current Topics in Medicinal Chemistry2011, 11 (15),1902–1924. https://doi.org/10.2174/156802611796391267

Mckay K, Hamilton NB, Remington JM, Schneebeli ST, Li J (2022) Essential Dynamics Ensemble Docking for Structure-Based GPCR Drug Discovery. Front. Mol. Bioscihttps://doi.org/fmolb.879212

Balakin KV, Tkachenko SE, Lang SA, Okun I, Ivashchenko AA, Savchuk NP Property-Based Design of GPCR-Targeted Library.J. Chem. Inf. Comput. Sci2002, 42 (6),1332–1342. https://doi.org/10.1021/ci025538y

Balakin KV, Lang SA, Skorenko AV, Tkachenko SE, Ivashchenko AA, Savchuk NP Structure-Based versus Property-Based Approaches in the Design of G-Protein-Coupled Receptor-Targeted Libraries.J. Chem. Inf. Comput. Sci2003, 43 (5),1553–1562. https://doi.org/10.1021/ci034114g

von Korff M, Steger M GPCR-Tailored Pharmacophore Pattern Recognition of Small Molecular Ligands.J. Chem. Inf. Comput. Sci2004, 44 (3),1137–1147. https://doi.org/10.1021/ci0303013

Lamb ML, Bradley EK, Beaton G, Bondy SS, Castellino AJ, Gibbons PA, Suto MJ, Grootenhuis PD J. Design of a Gene Family Screening Library Targeting G-Protein Coupled Receptors.Journal of Molecular Graphics and Modelling2004, 23 (1),15–21. https://doi.org/10.1016/j.jmgm.2004.03.001

Givehchi A, Schneider G Multi-Space Classification for Predicting GPCR-Ligands.Molecular Diversity2005, 9 (4),371–383. https://doi.org/10.1007/s11030-005-6293-4

Kelemen ÁA, Ferenczy GG, Keserű GM A Desirability Function-Based Scoring Scheme for Selecting Fragment-like Class A Aminergic GPCR Ligands.Journal of Computer-Aided Molecular Design2015, 29 (1),59–66. https://doi.org/10.1007/s10822-014-9804-5

van der Horst E, Okuno Y, Bender A, IJzerman AP Substructure Mining of GPCR Ligands Reveals Activity-Class Specific Functional Groups in an Unbiased Manner.J. Chem. Inf. Model2009, 49 (2),348–360. https://doi.org/10.1021/ci8003896

Seo S, Choi J, Ahn SK, Kim KW, Kim J, Choi J, Kim J, Ahn J Prediction of GPCR-Ligand Binding Using Machine Learning Algorithms. Comput Math Methods Med 2018, 2018, 6565241–6565241. https://doi.org/10.1155/2018/6565241

Raschka S, Kaufman B Machine Learning and AI-Based Approaches for Bioactive Ligand Discovery and GPCR-Ligand Recognition.Methods2020, 180,89–110. https://doi.org/10.1016/j.ymeth.2020.06.016

Chan WKB, Zhang H, Yang J, Brender JR, Hur J, Özgür A, Zhang YGLASS A Comprehensive Database for Experimentally Validated GPCR-Ligand Associations.Bioinformatics2015, 31 (18),3035–3042. https://doi.org/10.1093/bioinformatics/btv302

Mysinger MM, Carchia M, Irwin JJ, Shoichet BK Directory of Useful Decoys, Enhanced (DUD-E): Better Ligands and Decoys for Better Benchmarking.J. Med. Chem2012, 55 (14),6582–6594. https://doi.org/10.1021/jm300687e

NCI/CADD, group. Cactus Web Server (2021) https://cactus.nci.nih.gov/

RDKit Open-Source Cheminformatics. http://www.rdkit.org

Durant JL, Leland BA, Henry DR, Nourse JG Reoptimization of MDL Keys for Use in Drug Discovery.J. Chem. Inf. Comput. Sci2002, 42 (6),1273–1280. https://doi.org/10.1021/ci010132r

Rogers D, Hahn M, Extended-Connectivity, FingerprintsJ. Chem. Inf. Model2010, 50 (5),742–754. https://doi.org/10.1021/ci100050t

Abadi M, Barham P, Chen J, Chen Z, Davis A, Dean J, Devin M, Ghemawat S, Irving G, Isard M, Kudlur M, Levenberg J, Monga R, Moore S, Murray D, Steiner B, Tucker P, Vasudevan V, Warden P, Zhang X TensorFlow:A System for Large-Scale Machine Learning. 2016

Kipf TN, Welling M Semi-Supervised Classification with Graph Convolutional Networks. CoRR 2016, abs/1609.02907

Duvenaud D, Maclaurin D, Aguilera-Iparraguirre J, G\’mez-Bombarelli R, Hirzel T, Aspuru-Guzik A ’n; Adams, R. P. Convolutional Networks on Graphs for Learning Molecular Fingerprints. Nips’15 2015, 2224–2232

Kearnes S, McCloskey K, Berndl M, Pande V, Riley P Molecular Graph Convolutions: Moving beyond Fingerprints.Journal of computer-aided molecular design2016, 30 (8),595–608. https://doi.org/10.1007/s10822-016-9938-8

Xiong Z, Wang D, Liu X, Zhong F, Wan X, Li X, Li Z, Luo X, Chen K, Jiang H, Zheng M Pushing the Boundaries of Molecular Representation for Drug Discovery with the Graph Attention Mechanism.J. Med. Chem2020, 63 (16),8749–8760. https://doi.org/10.1021/acs.jmedchem.9b00959

Yang K, Swanson K, Jin W, Coley C, Eiden P, Gao H, Guzman-Perez A, Hopper T, Kelley B, Mathea M, Palmer A, Settels V, Jaakkola T, Jensen K, Barzilay R Analyzing Learned Molecular Representations for Property Prediction.J. Chem. Inf. Model2019, 59 (8),3370–3388. https://doi.org/10.1021/acs.jcim.9b00237

Visini R, Awale M, Reymond J-L, Fragment Database FDB-17J. Chem. Inf. Model2017, 57 (4),700–709. https://doi.org/10.1021/acs.jcim.7b00020

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 Deliv Rev2001, 46 (1–3),3–26. https://doi.org/10.1016/s0169-409x(00)00129-0

Veber DF, Johnson SR, Cheng H-Y, Smith BR, Ward KW, Kopple KD Molecular Properties That Influence the Oral Bioavailability of Drug Candidates.J Med Chem2002, 45 (12),2615–2623. https://doi.org/10.1021/jm020017n

Ghose AK, Viswanadhan VN, Wendoloski JJ A Knowledge-Based Approach in Designing Combinatorial or Medicinal Chemistry Libraries for Drug Discovery. 1. A Qualitative and Quantitative Characterization of Known Drug Databases.J Comb Chem1999, 1 (1),55–68. https://doi.org/10.1021/cc9800071

Bickerton GR, Paolini GV, Besnard J, Muresan S, Hopkins AL Quantifying the Chemical Beauty of Drugs.Nature Chemistry2012, 4 (2),90–98. https://doi.org/10.1038/nchem.1243

Heifetz A, Chudyk EI, Gleave L, Aldeghi M, Cherezov V, Fedorov DG, Biggin PC, Bodkin MJ The Fragment Molecular Orbital Method Reveals New Insight into the Chemical Nature of GPCR–Ligand Interactions.J. Chem. Inf. Model2016, 56 (1),159–172. https://doi.org/10.1021/acs.jcim.5b00644

Morao I, Fedorov DG, Robinson R, Southey M, Townsend-Nicholson A, Bodkin MJ, Heifetz A Rapid and Accurate Assessment of GPCR–Ligand Interactions Using the Fragment Molecular Orbital-Based Density-Functional Tight-Binding Method.Journal of Computational Chemistry2017, 38 (23),1987–1990. https://doi.org/10.1002/jcc.24850

Chudyk EI, Sarrat L, Aldeghi M, Fedorov DG, Bodkin MJ, James T, Southey M, Robinson R, Morao I, Heifetz A (2018) Exploring GPCR-Ligand Interactions with the Fragment Molecular Orbital (FMO) Method. In Computational Methods for GPCR Drug Discovery; Heifetz, A., Ed.; Springer New York: New York, NY, ; pp 179–195. https://doi.org/10.1007/978-1-4939-7465-8_8

Vistoli G, Pedretti A, Testa B Assessing Drug-Likeness–What Are We Missing?Drug Discov Today2008, 13 (7–8),285–294. https://doi.org/10.1016/j.drudis.2007.11.007

Liao C, de Molliens MP, Schneebeli ST, Brewer M, Song G, Chatenet D, Braas KM, May V, Li J Targeting the PAC1 Receptor for Neurological and Metabolic Disorders.Curr Top Med Chem2019, 19 (16),1399–1417. https://doi.org/10.2174/1568026619666190709092647

Liao C, Remington JM, May V, Li J Molecular Basis of Class B GPCR Selectivity for the Neuropeptides PACAP and VIP. Frontiers in Molecular Biosciences 2021, 8

Beebe X, Darczak D, Davis-Taber RA, Uchic ME, Scott VE, Jarvis MF, Stewart AO Discovery and SAR of Hydrazide Antagonists of the Pituitary Adenylate Cyclase-Activating Polypeptide (PACAP) Receptor Type 1 (PAC1-R).Bioorganic & Medicinal Chemistry Letters2008, 18 (6),2162–2166. https://doi.org/10.1016/j.bmcl.2008.01.052

留言 (0)

沒有登入
gif