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Items: 1 to 50 of 556

1.

Data structures for computational compound promiscuity analysis and exemplary applications to inhibitors of the human kinome.

Miljković F, Bajorath J.

J Comput Aided Mol Des. 2019 Dec 2. doi: 10.1007/s10822-019-00266-0. [Epub ahead of print]

PMID:
31792884
2.

Identifying Promiscuous Compounds with Activity against Different Target Classes.

Feldmann C, Miljković F, Yonchev D, Bajorath J.

Molecules. 2019 Nov 18;24(22). pii: E4185. doi: 10.3390/molecules24224185.

3.

Large-Scale Comparison of Alternative Similarity Search Strategies with Varying Chemical Information Contents.

Laufkötter O, Miyao T, Bajorath J.

ACS Omega. 2019 Sep 5;4(12):15304-15311. doi: 10.1021/acsomega.9b02470. eCollection 2019 Sep 17.

4.

Evolving Concept of Activity Cliffs.

Stumpfe D, Hu H, Bajorath J.

ACS Omega. 2019 Aug 26;4(11):14360-14368. doi: 10.1021/acsomega.9b02221. eCollection 2019 Sep 10. Review.

5.

Interpretation of Compound Activity Predictions from Complex Machine Learning Models Using Local Approximations and Shapley Values.

Rodríguez-Pérez R, Bajorath J.

J Med Chem. 2019 Sep 26. doi: 10.1021/acs.jmedchem.9b01101. [Epub ahead of print]

PMID:
31512867
6.

Identification of Compounds That Interfere with High-Throughput Screening Assay Technologies.

David L, Walsh J, Sturm N, Feierberg I, Nissink JWM, Chen H, Bajorath J, Engkvist O.

ChemMedChem. 2019 Oct 17;14(20):1795-1802. doi: 10.1002/cmdc.201900395. Epub 2019 Sep 19.

7.

Machine Learning Models for Accurate Prediction of Kinase Inhibitors with Different Binding Modes.

Miljković F, Rodríguez-Pérez R, Bajorath J.

J Med Chem. 2019 Aug 30. doi: 10.1021/acs.jmedchem.9b00867. [Epub ahead of print]

PMID:
31469557
8.

Method for Systematic Analogue Search Using the Mega SAR Matrix Database.

Yoshimori A, Horita Y, Tanoue T, Bajorath J.

J Chem Inf Model. 2019 Sep 23;59(9):3727-3734. doi: 10.1021/acs.jcim.9b00557. Epub 2019 Aug 30.

PMID:
31468964
9.

Promiscuous Ligands from Experimentally Determined Structures, Binding Conformations, and Protein Family-Dependent Interaction Hotspots.

Gilberg E, Gütschow M, Bajorath J.

ACS Omega. 2019 Jan 22;4(1):1729-1737. doi: 10.1021/acsomega.8b03481. eCollection 2019 Jan 31.

10.

Systematic Extraction of Analogue Series from Large Compound Collections Using a New Computational Compound-Core Relationship Method.

Naveja JJ, Vogt M, Stumpfe D, Medina-Franco JL, Bajorath J.

ACS Omega. 2019 Jan 14;4(1):1027-1032. doi: 10.1021/acsomega.8b03390. eCollection 2019 Jan 31.

11.

Rationalizing the Formation of Activity Cliffs in Different Compound Data Sets.

Hu H, Stumpfe D, Bajorath J.

ACS Omega. 2018 Jul 11;3(7):7736-7744. doi: 10.1021/acsomega.8b01188. eCollection 2018 Jul 31.

12.

Evaluation of different virtual screening strategies on the basis of compound sets with characteristic core distributions and dissimilarity relationships.

Miyao T, Jasial S, Bajorath J, Funatsu K.

J Comput Aided Mol Des. 2019 Aug;33(8):729-743. doi: 10.1007/s10822-019-00218-8. Epub 2019 Aug 21.

PMID:
31435894
13.
14.

Combining structural and bioactivity-based fingerprints improves prediction performance and scaffold hopping capability.

Laufkötter O, Sturm N, Bajorath J, Chen H, Engkvist O.

J Cheminform. 2019 Aug 8;11(1):54. doi: 10.1186/s13321-019-0376-1.

15.

Can Cysteine Protease Cross-Class Inhibitors Achieve Selectivity?

Cianni L, Feldmann CW, Gilberg E, Gütschow M, Juliano L, Leitão A, Bajorath J, Montanari CA.

J Med Chem. 2019 Aug 15. doi: 10.1021/acs.jmedchem.9b00683. [Epub ahead of print]

PMID:
31361135
16.

Repositioning the Chemical Information Science Gateway.

Bajorath J.

F1000Res. 2019 Jun 27;8. pii: Chem Inf Sci-976. doi: 10.12688/f1000research.19764.1. eCollection 2019.

17.

Introducing a new category of activity cliffs with chemical modifications at multiple sites and rationalizing contributions of individual substitutions.

Stumpfe D, Hu H, Bajorath J.

Bioorg Med Chem. 2019 Aug 15;27(16):3605-3612. doi: 10.1016/j.bmc.2019.06.045. Epub 2019 Jun 28.

PMID:
31272836
18.

The Future Is Now: Artificial Intelligence in Drug Discovery.

Bajorath J, Kearnes S, Walters WP, Georg GI, Wang S.

J Med Chem. 2019 Jun 13;62(11):5249. doi: 10.1021/acs.jmedchem.9b00805. Epub 2019 May 23. No abstract available.

PMID:
31120741
19.

Exploration of Target Synergy in Cancer Treatment by Cell-Based Screening Assay and Network Propagation Analysis.

Naveja JJ, Stumpfe D, Medina-Franco JL, Bajorath J.

J Chem Inf Model. 2019 Jun 24;59(6):3072-3079. doi: 10.1021/acs.jcim.9b00036. Epub 2019 May 1.

PMID:
31013082
20.

Systematic computational identification of promiscuity cliff pathways formed by inhibitors of the human kinome.

Miljković F, Vogt M, Bajorath J.

J Comput Aided Mol Des. 2019 Jun;33(6):559-572. doi: 10.1007/s10822-019-00198-9. Epub 2019 Mar 26.

PMID:
30915709
21.

Second-generation activity cliffs identified on the basis of target set-dependent potency difference criteria.

Hu H, Stumpfe D, Bajorath J.

Future Med Chem. 2019 Mar;11(5):379-394. doi: 10.4155/fmc-2018-0299. Epub 2019 Mar 19.

PMID:
30887828
22.

Duality of activity cliffs in drug discovery.

Bajorath J.

Expert Opin Drug Discov. 2019 Jun;14(6):517-520. doi: 10.1080/17460441.2019.1593371. Epub 2019 Mar 18. No abstract available.

PMID:
30882260
23.

Systematic identification of target set-dependent activity cliffs.

Hu H, Stumpfe D, Bajorath J.

Future Sci OA. 2019 Jan 18;5(2):FSO363. doi: 10.4155/fsoa-2018-0089. eCollection 2019 Feb.

24.

Pros and cons of virtual screening based on public "Big Data": In silico mining for new bromodomain inhibitors.

Casciuc I, Horvath D, Gryniukova A, Tolmachova KA, Vasylchenko OV, Borysko P, Moroz YS, Bajorath J, Varnek A.

Eur J Med Chem. 2019 Mar 1;165:258-272. doi: 10.1016/j.ejmech.2019.01.010. Epub 2019 Jan 9.

PMID:
30685526
25.

Virtual Screening with Generative Topographic Maps: How Many Maps Are Required?

Casciuc I, Zabolotna Y, Horvath D, Marcou G, Bajorath J, Varnek A.

J Chem Inf Model. 2019 Jan 28;59(1):564-572. doi: 10.1021/acs.jcim.8b00650. Epub 2018 Dec 31.

PMID:
30567430
26.

Computational Assessment of Chemical Saturation of Analogue Series under Varying Conditions.

Yonchev D, Vogt M, Stumpfe D, Kunimoto R, Miyao T, Bajorath J.

ACS Omega. 2018 Nov 30;3(11):15799-15808. doi: 10.1021/acsomega.8b02087. Epub 2018 Nov 20.

27.

Exploring Alternative Strategies for the Identification of Potent Compounds Using Support Vector Machine and Regression Modeling.

Miyao T, Funatsu K, Bajorath J.

J Chem Inf Model. 2019 Mar 25;59(3):983-992. doi: 10.1021/acs.jcim.8b00584. Epub 2018 Dec 14.

PMID:
30547580
28.

Computational chemical biology on the rise.

Brown JB, Bajorath J.

Future Med Chem. 2019 Jan;11(1):1-3. doi: 10.4155/fmc-2018-0282. Epub 2018 Dec 10. Review. No abstract available.

29.

Computational Method to Evaluate Progress in Lead Optimization.

Vogt M, Yonchev D, Bajorath J.

J Med Chem. 2018 Dec 13;61(23):10895-10900. doi: 10.1021/acs.jmedchem.8b01626. Epub 2018 Nov 30.

PMID:
30499667
30.

Three-Dimensional Activity Landscape Models of Different Design and Their Application to Compound Mapping and Potency Prediction.

Miyao T, Funatsu K, Bajorath J.

J Chem Inf Model. 2019 Mar 25;59(3):993-1004. doi: 10.1021/acs.jcim.8b00661. Epub 2018 Dec 12.

PMID:
30485091
31.

Cathepsin B: Active site mapping with peptidic substrates and inhibitors.

Schmitz J, Gilberg E, Löser R, Bajorath J, Bartz U, Gütschow M.

Bioorg Med Chem. 2019 Jan 1;27(1):1-15. doi: 10.1016/j.bmc.2018.10.017. Epub 2018 Oct 19. Review.

PMID:
30473362
32.

Machine Learning Distinguishes with High Accuracy between Pan-Assay Interference Compounds That Are Promiscuous or Represent Dark Chemical Matter.

Jasial S, Gilberg E, Blaschke T, Bajorath J.

J Med Chem. 2018 Nov 21;61(22):10255-10264. doi: 10.1021/acs.jmedchem.8b01404. Epub 2018 Nov 13.

PMID:
30422657
33.

SAR Matrix Method for Large-Scale Analysis of Compound Structure-Activity Relationships and Exploration of Multitarget Activity Spaces.

Hu Y, Bajorath J.

Methods Mol Biol. 2018;1825:339-352. doi: 10.1007/978-1-4939-8639-2_11.

PMID:
30334212
34.

Mapping Biological Activities to Different Types of Molecular Scaffolds: Exemplary Application to Protein Kinase Inhibitors.

Dimova D, Bajorath J.

Methods Mol Biol. 2018;1825:327-337. doi: 10.1007/978-1-4939-8639-2_10.

PMID:
30334211
35.
36.

Computationally derived compound profiling matrices.

Vogt M, Jasial S, Bajorath J.

Future Sci OA. 2018 Jul 24;4(8):FSO327. doi: 10.4155/fsoa-2018-0050. eCollection 2018 Sep.

37.

Foundations of data-driven medicinal chemistry.

Bajorath J.

Future Sci OA. 2018 Jun 28;4(8):FSO320. doi: 10.4155/fsoa-2018-0057. eCollection 2018 Sep. No abstract available.

38.

Data-Driven Exploration of Selectivity and Off-Target Activities of Designated Chemical Probes.

Miljković F, Bajorath J.

Molecules. 2018 Sep 23;23(10). pii: E2434. doi: 10.3390/molecules23102434.

39.

Exploring Selectivity of Multikinase Inhibitors across the Human Kinome.

Miljković F, Bajorath J.

ACS Omega. 2018 Jan 31;3(1):1147-1153. doi: 10.1021/acsomega.7b01960. Epub 2018 Jan 26.

40.

Dark chemical matter in public screening assays and derivation of target hypotheses.

Jasial S, Bajorath J.

Medchemcomm. 2017 Oct 26;8(11):2100-2104. doi: 10.1039/c7md00426e. eCollection 2017 Nov 1.

41.

A Hybrid Virtual Screening Protocol Based on Binding Mode Similarity.

Anighoro A, Bajorath J.

Methods Mol Biol. 2018;1824:165-175. doi: 10.1007/978-1-4939-8630-9_9.

PMID:
30039406
42.

Prediction of Compound Profiling Matrices Using Machine Learning.

Rodríguez-Pérez R, Miyao T, Jasial S, Vogt M, Bajorath J.

ACS Omega. 2018 Apr 30;3(4):4713-4723. doi: 10.1021/acsomega.8b00462.

43.

Extracting Compound Profiling Matrices from Screening Data.

Vogt M, Jasial S, Bajorath J.

ACS Omega. 2018 Apr 30;3(4):4706-4712. doi: 10.1021/acsomega.8b00461.

44.

Combining Similarity Searching and Network Analysis for the Identification of Active Compounds.

Kunimoto R, Bajorath J.

ACS Omega. 2018 Apr 30;3(4):3768-3777. doi: 10.1021/acsomega.8b00344. Epub 2018 Apr 3.

45.

Reconciling Selectivity Trends from a Comprehensive Kinase Inhibitor Profiling Campaign with Known Activity Data.

Miljković F, Bajorath J.

ACS Omega. 2018 Mar 31;3(3):3113-3119. doi: 10.1021/acsomega.8b00243. Epub 2018 Mar 14.

46.

X-ray-Structure-Based Identification of Compounds with Activity against Targets from Different Families and Generation of Templates for Multitarget Ligand Design.

Gilberg E, Stumpfe D, Bajorath J.

ACS Omega. 2018 Jan 31;3(1):106-111. doi: 10.1021/acsomega.7b01849. Epub 2018 Jan 5.

47.

Compound Ranking Based on Fuzzy Three-Dimensional Similarity Improves the Performance of Docking into Homology Models of G-Protein-Coupled Receptors.

Anighoro A, Bajorath J.

ACS Omega. 2017 Jun 30;2(6):2583-2592. doi: 10.1021/acsomega.7b00330. Epub 2017 Jun 8.

48.

Exploring Structural Relationships between Bioactive and Commercial Chemical Space and Developing Target Hypotheses for Compound Acquisition.

Cerchia C, Dimova D, Lavecchia A, Bajorath J.

ACS Omega. 2017 Nov 30;2(11):7760-7766. doi: 10.1021/acsomega.7b01338. Epub 2017 Nov 9.

49.

Support Vector Machine Classification and Regression Prioritize Different Structural Features for Binary Compound Activity and Potency Value Prediction.

Rodríguez-Pérez R, Vogt M, Bajorath J.

ACS Omega. 2017 Oct 31;2(10):6371-6379. doi: 10.1021/acsomega.7b01079. Epub 2017 Oct 4.

50.

Exploring ensembles of bioactive or virtual analogs of X-ray ligands for shape similarity searching.

Miyao T, Bajorath J.

J Comput Aided Mol Des. 2018 Jul;32(7):759-767. doi: 10.1007/s10822-018-0128-8. Epub 2018 Jul 2.

PMID:
29968097

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