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Items: 15

1.

Exhaustive sampling of the fragment space associated to a molecule leading to the generation of conserved fragments.

Heikamp K, Zuccotto F, Kiczun M, Ray P, Gilbert IH.

Chem Biol Drug Des. 2018 Mar;91(3):655-667. doi: 10.1111/cbdd.13129. Epub 2017 Dec 12.

2.

Computational polypharmacology analysis of the heat shock protein 90 interactome.

Anighoro A, Stumpfe D, Heikamp K, Beebe K, Neckers LM, Bajorath J, Rastelli G.

J Chem Inf Model. 2015 Mar 23;55(3):676-86. doi: 10.1021/ci5006959. Epub 2015 Feb 23.

PMID:
25686391
3.

Prediction of compounds in different local structure-activity relationship environments using emerging chemical patterns.

Namasivayam V, Gupta-Ostermann D, Balfer J, Heikamp K, Bajorath J.

J Chem Inf Model. 2014 May 27;54(5):1301-10. doi: 10.1021/ci500147b. Epub 2014 May 15.

PMID:
24803014
4.

Modeling of compound profiling experiments using support vector machines.

Balfer J, Heikamp K, Laufer S, Bajorath J.

Chem Biol Drug Des. 2014 Jul;84(1):75-85. doi: 10.1111/cbdd.12294. Epub 2014 Mar 13.

PMID:
24472570
5.

Support vector machines for drug discovery.

Heikamp K, Bajorath J.

Expert Opin Drug Discov. 2014 Jan;9(1):93-104. doi: 10.1517/17460441.2014.866943. Epub 2013 Dec 5. Review.

PMID:
24304044
6.

Comparison of confirmed inactive and randomly selected compounds as negative training examples in support vector machine-based virtual screening.

Heikamp K, Bajorath J.

J Chem Inf Model. 2013 Jul 22;53(7):1595-601. doi: 10.1021/ci4002712. Epub 2013 Jul 3.

PMID:
23799269
7.

Compound pathway model to capture SAR progression: comparison of activity cliff-dependent and -independent pathways.

Stumpfe D, Dimova D, Heikamp K, Bajorath J.

J Chem Inf Model. 2013 May 24;53(5):1067-72. doi: 10.1021/ci400141w. Epub 2013 Apr 22.

PMID:
23581427
8.

Do medicinal chemists learn from activity cliffs? A systematic evaluation of cliff progression in evolving compound data sets.

Dimova D, Heikamp K, Stumpfe D, Bajorath J.

J Med Chem. 2013 Apr 25;56(8):3339-45. doi: 10.1021/jm400147j. Epub 2013 Apr 9.

PMID:
23527828
9.

Prediction of compounds with closely related activity profiles using weighted support vector machine linear combinations.

Heikamp K, Bajorath J.

J Chem Inf Model. 2013 Apr 22;53(4):791-801. doi: 10.1021/ci400090t. Epub 2013 Apr 8.

PMID:
23517241
10.

The future of virtual compound screening.

Heikamp K, Bajorath J.

Chem Biol Drug Des. 2013 Jan;81(1):33-40. doi: 10.1111/cbdd.12054. Review.

PMID:
23253129
11.

Fingerprint design and engineering strategies: rationalizing and improving similarity search performance.

Heikamp K, Bajorat J.

Future Med Chem. 2012 Oct;4(15):1945-59. doi: 10.4155/fmc.12.126. Review.

PMID:
23088275
12.

Prediction of activity cliffs using support vector machines.

Heikamp K, Hu X, Yan A, Bajorath J.

J Chem Inf Model. 2012 Sep 24;52(9):2354-65. Epub 2012 Aug 23.

PMID:
22894655
13.

How do 2D fingerprints detect structurally diverse active compounds? Revealing compound subset-specific fingerprint features through systematic selection.

Heikamp K, Bajorath J.

J Chem Inf Model. 2011 Sep 26;51(9):2254-65. doi: 10.1021/ci200275m. Epub 2011 Aug 8.

PMID:
21793563
14.

Large-scale similarity search profiling of ChEMBL compound data sets.

Heikamp K, Bajorath J.

J Chem Inf Model. 2011 Aug 22;51(8):1831-9. doi: 10.1021/ci200199u. Epub 2011 Jul 14.

PMID:
21728295
15.

Potency-directed similarity searching using support vector machines.

Wassermann AM, Heikamp K, Bajorath J.

Chem Biol Drug Des. 2011 Jan;77(1):30-8. doi: 10.1111/j.1747-0285.2010.01059.x. Epub 2010 Nov 29.

PMID:
21114788

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