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Items: 1 to 50 of 82

1.

Response to Comment on "Predicting reaction performance in C-N cross-coupling using machine learning".

Estrada JG, Ahneman DT, Sheridan RP, Dreher SD, Doyle AG.

Science. 2018 Nov 16;362(6416). pii: eaat8763. doi: 10.1126/science.aat8763.

PMID:
30442777
2.

Role of simple descriptors and applicability domain in predicting change in protein thermostability.

McGuinness KN, Pan W, Sheridan RP, Murphy G, Crespo A.

PLoS One. 2018 Sep 7;13(9):e0203819. doi: 10.1371/journal.pone.0203819. eCollection 2018.

3.

Mapping the dark space of chemical reactions with extended nanomole synthesis and MALDI-TOF MS.

Lin S, Dikler S, Blincoe WD, Ferguson RD, Sheridan RP, Peng Z, Conway DV, Zawatzky K, Wang H, Cernak T, Davies IW, DiRocco DA, Sheng H, Welch CJ, Dreher SD.

Science. 2018 Aug 10;361(6402). pii: eaar6236. doi: 10.1126/science.aar6236. Epub 2018 May 24.

PMID:
29794218
4.

CHEMGENIE: integration of chemogenomics data for applications in chemical biology.

Kutchukian PS, Chang C, Fox SJ, Cook E, Barnard R, Tellers D, Wang H, Pertusi D, Glick M, Sheridan RP, Wallace IM, Wassermann AM.

Drug Discov Today. 2018 Jan;23(1):151-160. doi: 10.1016/j.drudis.2017.09.004. Epub 2017 Sep 14. Review.

PMID:
28917822
5.

Demystifying Multitask Deep Neural Networks for Quantitative Structure-Activity Relationships.

Xu Y, Ma J, Liaw A, Sheridan RP, Svetnik V.

J Chem Inf Model. 2017 Oct 23;57(10):2490-2504. doi: 10.1021/acs.jcim.7b00087. Epub 2017 Oct 2.

PMID:
28872869
6.

Is Multitask Deep Learning Practical for Pharma?

Ramsundar B, Liu B, Wu Z, Verras A, Tudor M, Sheridan RP, Pande V.

J Chem Inf Model. 2017 Aug 28;57(8):2068-2076. doi: 10.1021/acs.jcim.7b00146. Epub 2017 Aug 1.

PMID:
28692267
7.

Informing the Selection of Screening Hit Series with in Silico Absorption, Distribution, Metabolism, Excretion, and Toxicity Profiles.

Sanders JM, Beshore DC, Culberson JC, Fells JI, Imbriglio JE, Gunaydin H, Haidle AM, Labroli M, Mattioni BE, Sciammetta N, Shipe WD, Sheridan RP, Suen LM, Verras A, Walji A, Joshi EM, Bueters T.

J Med Chem. 2017 Aug 24;60(16):6771-6780. doi: 10.1021/acs.jmedchem.6b01577. Epub 2017 May 5. Review.

PMID:
28418656
8.

Extreme Gradient Boosting as a Method for Quantitative Structure-Activity Relationships.

Sheridan RP, Wang WM, Liaw A, Ma J, Gifford EM.

J Chem Inf Model. 2016 Dec 27;56(12):2353-2360. doi: 10.1021/acs.jcim.6b00591. Epub 2016 Dec 13.

PMID:
27958738
9.

Debunking the Idea that Ligand Efficiency Indices Are Superior to pIC50 as QSAR Activities.

Sheridan RP.

J Chem Inf Model. 2016 Nov 28;56(11):2253-2262. Epub 2016 Oct 28.

PMID:
27766848
10.

Mining Chromatographic Enantioseparation Data Using Matched Molecular Pair Analysis.

Sheridan RP, Piras P, Sherer EC, Roussel C, Pirkle WH, Welch CJ.

Molecules. 2016 Sep 29;21(10). pii: E1297.

11.

The Relative Importance of Domain Applicability Metrics for Estimating Prediction Errors in QSAR Varies with Training Set Diversity.

Sheridan RP.

J Chem Inf Model. 2015 Jun 22;55(6):1098-107. doi: 10.1021/acs.jcim.5b00110. Epub 2015 Jun 4.

PMID:
25998559
12.

Deep neural nets as a method for quantitative structure-activity relationships.

Ma J, Sheridan RP, Liaw A, Dahl GE, Svetnik V.

J Chem Inf Model. 2015 Feb 23;55(2):263-74. doi: 10.1021/ci500747n. Epub 2015 Feb 17.

PMID:
25635324
13.

eCounterscreening: using QSAR predictions to prioritize testing for off-target activities and setting the balance between benefit and risk.

Sheridan RP, McMasters DR, Voigt JH, Wildey MJ.

J Chem Inf Model. 2015 Feb 23;55(2):231-8. doi: 10.1021/ci500666m. Epub 2015 Jan 20.

PMID:
25551659
14.

Modeling a crowdsourced definition of molecular complexity.

Sheridan RP, Zorn N, Sherer EC, Campeau LC, Chang CZ, Cumming J, Maddess ML, Nantermet PG, Sinz CJ, O'Shea PD.

J Chem Inf Model. 2014 Jun 23;54(6):1604-16. doi: 10.1021/ci5001778. Epub 2014 May 20.

PMID:
24802889
15.

Global quantitative structure-activity relationship models vs selected local models as predictors of off-target activities for project compounds.

Sheridan RP.

J Chem Inf Model. 2014 Apr 28;54(4):1083-92. doi: 10.1021/ci500084w. Epub 2014 Mar 26.

PMID:
24628044
16.

Using random forest to model the domain applicability of another random forest model.

Sheridan RP.

J Chem Inf Model. 2013 Nov 25;53(11):2837-50. doi: 10.1021/ci400482e. Epub 2013 Nov 5.

PMID:
24152204
17.

Time-split cross-validation as a method for estimating the goodness of prospective prediction.

Sheridan RP.

J Chem Inf Model. 2013 Apr 22;53(4):783-90. doi: 10.1021/ci400084k. Epub 2013 Apr 5.

PMID:
23521722
18.

QSAR Prediction of Passive Permeability in the LLC-PK1 Cell Line: Trends in Molecular Properties and Cross-Prediction of Caco-2 Permeabilities.

Sherer EC, Verras A, Madeira M, Hagmann WK, Sheridan RP, Roberts D, Bleasby K, Cornell WD.

Mol Inform. 2012 Apr;31(3-4):231-45. doi: 10.1002/minf.201100157. Epub 2012 Mar 12.

PMID:
27477094
19.

Three useful dimensions for domain applicability in QSAR models using random forest.

Sheridan RP.

J Chem Inf Model. 2012 Mar 26;52(3):814-23. doi: 10.1021/ci300004n. Epub 2012 Mar 9.

PMID:
22385389
20.

Comparison of random forest and Pipeline Pilot Naïve Bayes in prospective QSAR predictions.

Chen B, Sheridan RP, Hornak V, Voigt JH.

J Chem Inf Model. 2012 Mar 26;52(3):792-803. doi: 10.1021/ci200615h. Epub 2012 Mar 8.

PMID:
22360769
21.

Drug-like density: a method of quantifying the "bindability" of a protein target based on a very large set of pockets and drug-like ligands from the Protein Data Bank.

Sheridan RP, Maiorov VN, Holloway MK, Cornell WD, Gao YD.

J Chem Inf Model. 2010 Nov 22;50(11):2029-40. doi: 10.1021/ci100312t. Epub 2010 Oct 26.

PMID:
20977231
22.

Molecular shape and medicinal chemistry: a perspective.

Nicholls A, McGaughey GB, Sheridan RP, Good AC, Warren G, Mathieu M, Muchmore SW, Brown SP, Grant JA, Haigh JA, Nevins N, Jain AN, Kelley B.

J Med Chem. 2010 May 27;53(10):3862-86. doi: 10.1021/jm900818s.

23.

QSAR models for predicting the similarity in binding profiles for pairs of protein kinases and the variation of models between experimental data sets.

Sheridan RP, Nam K, Maiorov VN, McMasters DR, Cornell WD.

J Chem Inf Model. 2009 Aug;49(8):1974-85. doi: 10.1021/ci900176y.

PMID:
19639957
24.

Multiple protein structures and multiple ligands: effects on the apparent goodness of virtual screening results.

Sheridan RP, McGaughey GB, Cornell WD.

J Comput Aided Mol Des. 2008 Mar-Apr;22(3-4):257-65. doi: 10.1007/s10822-008-9168-9. Epub 2008 Feb 14.

PMID:
18273559
25.
26.

Mini review on molecular modeling of P-glycoprotein (Pgp).

Ha SN, Hochman J, Sheridan RP.

Curr Top Med Chem. 2007;7(15):1525-9. Review.

PMID:
17897039
27.

Comparison of topological, shape, and docking methods in virtual screening.

McGaughey GB, Sheridan RP, Bayly CI, Culberson JC, Kreatsoulas C, Lindsley S, Maiorov V, Truchon JF, Cornell WD.

J Chem Inf Model. 2007 Jul-Aug;47(4):1504-19. Epub 2007 Jun 26.

PMID:
17591764
28.

Empirical regioselectivity models for human cytochromes P450 3A4, 2D6, and 2C9.

Sheridan RP, Korzekwa KR, Torres RA, Walker MJ.

J Med Chem. 2007 Jul 12;50(14):3173-84. Epub 2007 Jun 19.

29.

Inhibition of recombinant cytochrome P450 isoforms 2D6 and 2C9 by diverse drug-like molecules.

McMasters DR, Torres RA, Crathern SJ, Dooney DL, Nachbar RB, Sheridan RP, Korzekwa KR.

J Med Chem. 2007 Jul 12;50(14):3205-13. Epub 2007 Jun 9.

30.

Modeling assisted rational design of novel, potent, and selective pyrrolopyrimidine DPP-4 inhibitors.

Gao YD, Feng D, Sheridan RP, Scapin G, Patel SB, Wu JK, Zhang X, Sinha-Roy R, Thornberry NA, Weber AE, Biftu T.

Bioorg Med Chem Lett. 2007 Jul 15;17(14):3877-9. Epub 2007 May 3.

PMID:
17502141
31.

Chemical similarity searches: when is complexity justified?

Sheridan RP.

Expert Opin Drug Discov. 2007 Apr;2(4):423-30. doi: 10.1517/17460441.2.4.423.

PMID:
23484752
32.

Molecular transformations as a way of finding and exploiting consistent local QSAR.

Sheridan RP, Hunt P, Culberson JC.

J Chem Inf Model. 2006 Jan-Feb;46(1):180-92.

PMID:
16426054
33.

Reagent Selector: using Synthon Analysis to visualize reagent properties and assist in combinatorial library design.

Mosley RT, Culberson JC, Kraker B, Feuston BP, Sheridan RP, Conway JF, Forbes JK, Chakravorty SJ, Kearsley SK.

J Chem Inf Model. 2005 Sep-Oct;45(5):1439-46.

PMID:
16180921
34.

Web enabling technology for the design, enumeration, optimization and tracking of compound libraries.

Feuston BP, Chakravorty SJ, Conway JF, Culberson JC, Forbes J, Kraker B, Lennon PA, Lindsley C, McGaughey GB, Mosley R, Sheridan RP, Valenciano M, Kearsley SK.

Curr Top Med Chem. 2005;5(8):773-83. Review.

PMID:
16101417
35.

Enhanced virtual screening by combined use of two docking methods: getting the most on a limited budget.

Maiorov V, Sheridan RP.

J Chem Inf Model. 2005 Jul-Aug;45(4):1017-23.

PMID:
16045296
36.

Boosting: an ensemble learning tool for compound classification and QSAR modeling.

Svetnik V, Wang T, Tong C, Liaw A, Sheridan RP, Song Q.

J Chem Inf Model. 2005 May-Jun;45(3):786-99.

PMID:
15921468
37.

Similarity to molecules in the training set is a good discriminator for prediction accuracy in QSAR.

Sheridan RP, Feuston BP, Maiorov VN, Kearsley SK.

J Chem Inf Comput Sci. 2004 Nov-Dec;44(6):1912-28.

PMID:
15554660
38.

Calculating similarities between biological activities in the MDL Drug Data Report database.

Sheridan RP, Shpungin J.

J Chem Inf Comput Sci. 2004 Mar-Apr;44(2):727-40.

PMID:
15032555
39.

Random forest: a classification and regression tool for compound classification and QSAR modeling.

Svetnik V, Liaw A, Tong C, Culberson JC, Sheridan RP, Feuston BP.

J Chem Inf Comput Sci. 2003 Nov-Dec;43(6):1947-58.

PMID:
14632445
40.

Finding multiactivity substructures by mining databases of drug-like compounds.

Sheridan RP.

J Chem Inf Comput Sci. 2003 May-Jun;43(3):1037-50.

PMID:
12767163
41.

A model for predicting likely sites of CYP3A4-mediated metabolism on drug-like molecules.

Singh SB, Shen LQ, Walker MJ, Sheridan RP.

J Med Chem. 2003 Apr 10;46(8):1330-6.

PMID:
12672233
42.

Why do we need so many chemical similarity search methods?

Sheridan RP, Kearsley SK.

Drug Discov Today. 2002 Sep 1;7(17):903-11. Review.

PMID:
12546933
43.

A simple method for visualizing the differences between related receptor sites.

Sheridan RP, Holloway MK, McGaughey G, Mosley RT, Singh SB.

J Mol Graph Model. 2002 Dec;21(3):217-25.

PMID:
12463640
44.

Amino acid substitution of arginine 80 in 17beta-hydroxysteroid dehydrogenase type 3 and its effect on NADPH cofactor binding and oxidation/reduction kinetics.

McKeever BM, Hawkins BK, Geissler WM, Wu L, Sheridan RP, Mosley RT, Andersson S.

Biochim Biophys Acta. 2002 Nov 19;1601(1):29-37.

PMID:
12429500
45.

A simple method for visualizing the differences between related receptor sites.

Sheridan RP, Holloway MK, McGaughey G, Mosley RT, Singh SB.

J Mol Graph Model. 2002 Aug;21(1):71-9. Corrected and republished in: J Mol Graph Model. 2002 Dec;21(3):217-25.

PMID:
12413033
46.

The most common chemical replacements in drug-like compounds.

Sheridan RP.

J Chem Inf Comput Sci. 2002 Jan-Feb;42(1):103-8.

PMID:
11855973
47.

Protocols for bridging the peptide to nonpeptide gap in topological similarity searches.

Sheridan RP, Singh SB, Fluder EM, Kearsley SK.

J Chem Inf Comput Sci. 2001 Sep-Oct;41(5):1395-406.

PMID:
11604041
48.
49.

Chemical similarity searches using latent semantic structural indexing (LaSSI) and comparison to TOPOSIM.

Hull RD, Fluder EM, Singh SB, Nachbar RB, Kearsley SK, Sheridan RP.

J Med Chem. 2001 Apr 12;44(8):1185-91.

PMID:
11312918
50.

Latent semantic structure indexing (LaSSI) for defining chemical similarity.

Hull RD, Singh SB, Nachbar RB, Sheridan RP, Kearsley SK, Fluder EM.

J Med Chem. 2001 Apr 12;44(8):1177-84.

PMID:
11312917

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