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Items: 16

1.

Effect of missing data on multitask prediction methods.

de la Vega de León A, Chen B, Gillet VJ.

J Cheminform. 2018 May 22;10(1):26. doi: 10.1186/s13321-018-0281-z.

2.

Design of chemical space networks incorporating compound distance relationships.

de la Vega de León A, Bajorath J.

Version 2. F1000Res. 2016 Nov 4 [revised 2016 Jan 1];5. pii: Chem Inf Sci-2634. doi: 10.12688/f1000research.10021.2. eCollection 2016.

3.

Predicting bioactive conformations and binding modes of macrocycles.

Anighoro A, de la Vega de León A, Bajorath J.

J Comput Aided Mol Des. 2016 Oct;30(10):841-849. Epub 2016 Sep 21.

PMID:
27655412
4.

Chemical space visualization: transforming multidimensional chemical spaces into similarity-based molecular networks.

de la Vega de León A, Bajorath J.

Future Med Chem. 2016 Sep;8(14):1769-78. doi: 10.4155/fmc-2016-0023. Epub 2016 Aug 30.

PMID:
27572425
5.

Prediction of Activity Cliffs Using Condensed Graphs of Reaction Representations, Descriptor Recombination, Support Vector Machine Classification, and Support Vector Regression.

Horvath D, Marcou G, Varnek A, Kayastha S, de la Vega de León A, Bajorath J.

J Chem Inf Model. 2016 Sep 26;56(9):1631-40. doi: 10.1021/acs.jcim.6b00359. Epub 2016 Aug 26.

PMID:
27564682
6.

Monitoring the Progression of Structure-Activity Relationship Information during Lead Optimization.

Shanmugasundaram V, Zhang L, Kayastha S, de la Vega de León A, Dimova D, Bajorath J.

J Med Chem. 2016 May 12;59(9):4235-44. doi: 10.1021/acs.jmedchem.5b01428. Epub 2015 Nov 16.

PMID:
26569348
7.

Visualization of multi-property landscapes for compound selection and optimization.

de la Vega de León A, Kayastha S, Dimova D, Schultz T, Bajorath J.

J Comput Aided Mol Des. 2015 Aug;29(8):695-705. doi: 10.1007/s10822-015-9862-3. Epub 2015 Aug 2.

PMID:
26233785
8.

Synthesis, biological evaluation and molecular docking of N-phenyl thiosemicarbazones as urease inhibitors.

Hameed A, Khan KM, Zehra ST, Ahmed R, Shafiq Z, Bakht SM, Yaqub M, Hussain M, de la Vega de León A, Furtmann N, Bajorath J, Shad HA, Tahir MN, Iqbal J.

Bioorg Chem. 2015 Aug;61:51-7. doi: 10.1016/j.bioorg.2015.06.004. Epub 2015 Jun 22.

PMID:
26119990
9.

Prediction of compound potency changes in matched molecular pairs using support vector regression.

de la Vega de León A, Bajorath J.

J Chem Inf Model. 2014 Oct 27;54(10):2654-63. doi: 10.1021/ci5003944. Epub 2014 Sep 17.

PMID:
25191787
10.

Advancing the activity cliff concept, part II.

Stumpfe D, de la Vega de León A, Dimova D, Bajorath J.

F1000Res. 2014 Mar 18;3:75. doi: 10.12688/f1000research.3788.1. eCollection 2014.

11.

Systematic Identification of Matching Molecular Series and Mapping of Screening Hits.

de la Vega de León A, Hu Y, Bajorath J.

Mol Inform. 2014 Apr;33(4):257-63. doi: 10.1002/minf.201400017. Epub 2014 Mar 13.

PMID:
27485771
12.

Matched molecular pair-based data sets for computer-aided medicinal chemistry.

Hu Y, de la Vega de León A, Zhang B, Bajorath J.

Version 2. F1000Res. 2014 Feb 4 [revised 2014 Jan 1];3:36. doi: 10.12688/f1000research.3-36.v2. eCollection 2014.

13.

Formation of activity cliffs is accompanied by systematic increases in ligand efficiency from lowly to highly potent compounds.

de la Vega de León A, Bajorath J.

AAPS J. 2014 Mar;16(2):335-41. doi: 10.1208/s12248-014-9567-x. Epub 2014 Jan 30.

14.

Draft Genome Sequence of the Steroid Degrader Rhodococcus ruber Strain Chol-4.

Fernández de Las Heras L, Alonso S, de la Vega de León A, Xavier D, Perera J, Navarro Llorens JM.

Genome Announc. 2013 May 16;1(3). pii: e00215-13. doi: 10.1128/genomeA.00215-13.

15.

Compound optimization through data set-dependent chemical transformations.

de la Vega de León A, Bajorath J.

J Chem Inf Model. 2013 Jun 24;53(6):1263-71. doi: 10.1021/ci400165a. Epub 2013 May 20.

PMID:
23654345
16.

Design of a three-dimensional multitarget activity landscape.

de la Vega de León A, Bajorath J.

J Chem Inf Model. 2012 Nov 26;52(11):2876-83. doi: 10.1021/ci300444p. Epub 2012 Nov 7.

PMID:
23113585

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