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Best matches for "machine learning" and "scoring functions":

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Items: 1 to 20 of 73

1.

Empirical Scoring Functions for Structure-Based Virtual Screening: Applications, Critical Aspects, and Challenges.

Guedes IA, Pereira FSS, Dardenne LE.

Front Pharmacol. 2018 Sep 24;9:1089. doi: 10.3389/fphar.2018.01089. eCollection 2018. Review.

2.

Rescoring of docking poses under Occam's Razor: are there simpler solutions?

Zhenin M, Bahia MS, Marcou G, Varnek A, Senderowitz H, Horvath D.

J Comput Aided Mol Des. 2018 Sep;32(9):877-888. doi: 10.1007/s10822-018-0155-5. Epub 2018 Sep 1.

PMID:
30173397
3.

Convolutional neural network scoring and minimization in the D3R 2017 community challenge.

Sunseri J, King JE, Francoeur PG, Koes DR.

J Comput Aided Mol Des. 2018 Jul 10. doi: 10.1007/s10822-018-0133-y. [Epub ahead of print]

PMID:
29992528
4.

Visualizing convolutional neural network protein-ligand scoring.

Hochuli J, Helbling A, Skaist T, Ragoza M, Koes DR.

J Mol Graph Model. 2018 Sep;84:96-108. doi: 10.1016/j.jmgm.2018.06.005. Epub 2018 Jun 18.

PMID:
29940506
5.

Development of a machine-learning model to predict Gibbs free energy of binding for protein-ligand complexes.

Bitencourt-Ferreira G, de Azevedo WF.

Biophys Chem. 2018 Sep;240:63-69. doi: 10.1016/j.bpc.2018.05.010. Epub 2018 Jun 7.

PMID:
29906639
6.

Machine Learning Classification Models to Improve the Docking-based Screening: A Case of PI3K-Tankyrase Inhibitors.

Berishvili VP, Voronkov AE, Radchenko EV, Palyulin VA.

Mol Inform. 2018 Nov;37(11):e1800030. doi: 10.1002/minf.201800030. Epub 2018 Jun 14.

PMID:
29901257
7.

Development and evaluation of a deep learning model for protein-ligand binding affinity prediction.

Stepniewska-Dziubinska MM, Zielenkiewicz P, Siedlecki P.

Bioinformatics. 2018 Nov 1;34(21):3666-3674. doi: 10.1093/bioinformatics/bty374.

8.

The Impact of Protein Structure and Sequence Similarity on the Accuracy of Machine-Learning Scoring Functions for Binding Affinity Prediction.

Li H, Peng J, Leung Y, Leung KS, Wong MH, Lu G, Ballester PJ.

Biomolecules. 2018 Mar 14;8(1). pii: E12. doi: 10.3390/biom8010012.

9.

Boosted neural networks scoring functions for accurate ligand docking and ranking.

Ashtawy HM, Mahapatra NR.

J Bioinform Comput Biol. 2018 Apr;16(2):1850004. doi: 10.1142/S021972001850004X. Epub 2018 Feb 4.

PMID:
29495922
10.

Development of CDK-targeted scoring functions for prediction of binding affinity.

Levin NMB, Pintro VO, Bitencourt-Ferreira G, de Mattos BB, de Castro Silvério A, de Azevedo WF Jr.

Biophys Chem. 2018 Apr;235:1-8. doi: 10.1016/j.bpc.2018.01.004. Epub 2018 Feb 1.

PMID:
29407904
11.
12.

Task-Specific Scoring Functions for Predicting Ligand Binding Poses and Affinity and for Screening Enrichment.

Ashtawy HM, Mahapatra NR.

J Chem Inf Model. 2018 Jan 22;58(1):119-133. doi: 10.1021/acs.jcim.7b00309. Epub 2017 Dec 20.

PMID:
29190087
13.

Optimized Virtual Screening Workflow: Towards Target-Based Polynomial Scoring Functions for HIV-1 Protease.

Pintro VO, de Azevedo WF.

Comb Chem High Throughput Screen. 2017;20(9):820-827. doi: 10.2174/1386207320666171121110019.

PMID:
29165067
14.

Virtual screening approach to identifying influenza virus neuraminidase inhibitors using molecular docking combined with machine-learning-based scoring function.

Zhang L, Ai HX, Li SM, Qi MY, Zhao J, Zhao Q, Liu HS.

Oncotarget. 2017 Sep 15;8(47):83142-83154. doi: 10.18632/oncotarget.20915. eCollection 2017 Oct 10.

15.

Supervised machine learning techniques to predict binding affinity. A study for cyclin-dependent kinase 2.

de Ávila MB, Xavier MM, Pintro VO, de Azevedo WF Jr.

Biochem Biophys Res Commun. 2017 Dec 9;494(1-2):305-310. doi: 10.1016/j.bbrc.2017.10.035. Epub 2017 Oct 7.

PMID:
29017921
16.

Protein-Ligand Empirical Interaction Components for Virtual Screening.

Yan Y, Wang W, Sun Z, Zhang JZH, Ji C.

J Chem Inf Model. 2017 Aug 28;57(8):1793-1806. doi: 10.1021/acs.jcim.7b00017. Epub 2017 Jul 18.

PMID:
28678484
17.

Machine Learning Consensus Scoring Improves Performance Across Targets in Structure-Based Virtual Screening.

Ericksen SS, Wu H, Zhang H, Michael LA, Newton MA, Hoffmann FM, Wildman SA.

J Chem Inf Model. 2017 Jul 24;57(7):1579-1590. doi: 10.1021/acs.jcim.7b00153. Epub 2017 Jul 12.

18.

Supervised Machine Learning Methods Applied to Predict Ligand- Binding Affinity.

Heck GS, Pintro VO, Pereira RR, de Ávila MB, Levin NMB, de Azevedo WF.

Curr Med Chem. 2017;24(23):2459-2470. doi: 10.2174/0929867324666170623092503. Review.

PMID:
28641555
19.

Performance of machine-learning scoring functions in structure-based virtual screening.

Wójcikowski M, Ballester PJ, Siedlecki P.

Sci Rep. 2017 Apr 25;7:46710. doi: 10.1038/srep46710.

20.

Protein-Ligand Scoring with Convolutional Neural Networks.

Ragoza M, Hochuli J, Idrobo E, Sunseri J, Koes DR.

J Chem Inf Model. 2017 Apr 24;57(4):942-957. doi: 10.1021/acs.jcim.6b00740. Epub 2017 Apr 11.

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