| [1] |
Bradbury A, Hall S, Curtin N, et al. Targeting ATR as Cancer Therapy: a new era for synthetic lethality and synergistic combinations [J]? Pharmacol Ther, 2020, 207: 107450.
|
| [2] |
Zimmermann A, Dahmen H, Grombacher T, et al. Abstract 2588: M1774, a novel potent and selective ATR inhibitor, shows antitumor effects as monotherapy and in combination[J]. Cancer Res, 2022, 82(12_Suppl): 2588.
|
| [3] |
Yap Timothy A, Tolcher Anthony W, Ruth PE, et al. A first-in-human phase I study of ATR inhibitor M1774 in patients with solid tumors[J]. J Clin Oncol, 2021, 39(15_suppl): TPS3153.
|
| [4] |
Zenke FT, Zimmermann A, Dahmen H, et al. Antitumor activity of M4344, a potent and selective ATR inhibitor, in monotherapy and combination therapy [J]. Cancer Res, 2019, 79(13_Suppl): 369.
|
| [5] |
Fokas E, Prevo R, Pollard JR, et al. Targeting ATR in vivo using the novel inhibitor VE-822 results in selective sensitization of pancreatic tumors to radiation[J]. Cell Death Dis, 2012, 3(12): e441.
|
| [6] |
Knegtel R, Charrier JD, Durrant S, et al. Rational design of 5-(4-(isopropylsulfonyl) phenyl)-3-(3-(4-((methylamino) methyl) phenyl) isoxazol-5-yl) pyrazin-2-amine (VX-970, M6620): optimization of intra- and intermolecular polar interactions of a new ataxia telangiectasia mutated and Rad3-related (ATR) kinase inhibitor[J]. J Med Chem, 2019, 62(11): 5547-5561.
|
| [7] |
Foote KM, Nissink JWM, McGuire T, et al. Discovery and characterization of AZD6738, a potent inhibitor of ataxia telangiectasia mutated and Rad3 related (ATR) kinase with application as an anticancer agent[J]. J Med Chem, 2018, 61(22): 9889-9907.
|
| [8] |
Foote KM, Lau A. Drugging ATR: progress in the development of specific inhibitors for the treatment of cancer[J]. Future Med Chem, 2015, 7(7): 873-891.
|
| [9] |
Luecking U, Lefranc J, Wengner A, et al. Abstract 983: identification of potent, highly selective and orally available ATR inhibitor BAY 1895344 with favorable PK properties and promising efficacy in monotherapy and combination in preclinical tumor models[J]. Cancer Res, 2017, 77(13_Suppl): 983.
|
| [10] |
Wengner AM, Siemeister G, Lücking U, et al. The novel ATR inhibitor BAY 1895344 is efficacious as monotherapy and combined with DNA damage-inducing or repair-compromising therapies in preclinical cancer models[J]. Mol Cancer Ther, 2020, 19(1): 26-38.
|
| [11] |
Roulston A, Zimmermann M, Papp R, et al. RP-3500: a novel, potent, and selective ATR inhibitor that is effective in preclinical models as a monotherapy and in combination with PARP inhibitors[J]. Mol Cancer Ther, 2022, 21(2): 245-256.
|
| [12] |
Lipinski CA, Lombardo F, Dominy BW, et al. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings[J]. Adv Drug Deliv Rev, 2001, 46(1/2/3): 3-26.
|
| [13] |
Veber DF, Johnson SR, Cheng HY, et al. Molecular properties that influence the oral bioavailability of drug candidates[J]. J Med Chem, 2002, 45(12): 2615-2623.
|
| [14] |
Taylor RD, MacCoss M, Lawson AD. Rings in drugs: miniperspective [J]. J Med Chem, 2014, 57(14): 5845-5859.
|
| [15] |
Friedman JH. Greedy function approximation: a gradient boosting machine[J]. Ann Statist, 2001, 29(5): 1189-1232.
|
| [16] |
Chen TQ, Guestrin C. XGBoost: a scalable tree boosting system[C]//Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2016: 785-794.
|
| [17] |
Dorogush AV, Ershov V, Gulin A. CatBoost: gradient boosting with categorical features support[J]. arXiv, 2018: 1810.11363.
|
| [18] |
Tropsha A, Gramatica P, Gombar V. The importance of being earnest: validation is the absolute essential for successful application and interpretation of QSPR models[J]. QSAR Comb Sci, 2003, 22(1): 69-77.
|
| [19] |
Kar S, Roy K. First report on development of quantitative interspecies structure—carcinogenicity relationship models and exploring discriminatory features for rodent carcinogenicity of diverse organic chemicals using OECD guidelines[J]. Chemosphere, 2012, 87(4): 339-355.
|
| [20] |
Ojha P, Mitra I, Das R, et al. Further exploring rm2 metrics for validation of QSPR models[J]. Chemom Intell Lab Syst, 2011, 107: 194-205.
|
| [21] |
Roy PP, Leonard JT, Roy K. Exploring the impact of size of training sets for the development of predictive QSAR models[J]. Chemom Intell Lab Syst, 2008, 90(1): 31-42.
|
| [22] |
Pratim Roy P, Paul S, Mitra I, et al. On two novel parameters for validation of predictive QSAR models[J]. Molecules, 2009, 14(5): 1660-1701.
|
| [23] |
Mitra I, Roy PP, Kar S, et al. On further application of r as a metric for validation of QSAR models[J]. J Chemom, 2010, 24(1): 22-33.
|
| [24] |
Brian Houston J, Carlile DJ. Prediction of hepatic clearance from microsomes, hepatocytes, and liver slices[J]. Drug Metab Rev, 1997, 29(4): 891-922.
|
| [25] |
Tang HD, Hussain A, Leal M, et al. Interspecies prediction of human drug clearance based on scaling data from one or two animal species[J]. Drug Metab Dispos, 2007, 35(10): 1886-1893.
|
| [26] |
Friesner RA, Banks JL, Murphy RB, et al. Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy[J]. J Med Chem, 2004, 47(7): 1739-1749.
|
| [27] |
Friesner RA, Murphy RB, Repasky MP, et al. Extra precision glide: docking and scoring incorporating a model of hydrophobic enclosure for protein-ligand complexes[J]. J Med Chem, 2006, 49(21): 6177-6196.
|
| [28] |
Vilar S, Cozza G, Moro S. Medicinal chemistry and the molecular operating environment (MOE): application of QSAR and molecular docking to drug discovery[J]. Curr Top Med Chem, 2008, 8(18): 1555-1572.
|
| [29] |
Vass M, Kooistra AJ, Ritschel T, et al. Molecular interaction fingerprint approaches for GPCR drug discovery[J]. Curr Opin Pharmacol, 2016, 30: 59-68.
|
| [30] |
Kozakov D, Grove LE, Hall DR, et al. The FTMap family of web servers for determining and characterizing ligand-binding hot spots of proteins[J]. Nat Protoc, 2015, 10(5): 733-755.
|
| [31] |
Bemis GW, Murcko MA. The properties of known drugs. 1. molecular frameworks[J]. J Med Chem, 1996, 39(15): 2887-2893.
|
| [32] |
Polykovskiy D, Zhebrak A, Sanchez-Lengeling B, et al. Molecular sets (MOSES): a benchmarking platform for molecular generation models[J]. Front Pharmacol, 2020, 11: 565644.
|
| [33] |
Fearn T. Probabilistic principal component analysis[J]. NIR News, 2014, 25(3): 23.
|
| [34] |
Lu YP, Knapp M, Crawford K, et al. Rationally designed PI3Kα mutants to mimic ATR and their use to understand binding specificity of ATR inhibitors[J]. J Mol Biol, 2017, 429(11): 1684-1704.
|
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