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Best matches for "scoring functions" and "binding affinity":

Protein-protein interactions: scoring schemes and binding affinity. Gromiha MM et al. Curr Opin Struct Biol. (2017)

Machine-learning scoring functions to improve structure-based binding affinity prediction and virtual screening. Ain QU et al. Wiley Interdiscip Rev Comput Mol Sci. (2015)

Forging the Basis for Developing Protein-Ligand Interaction Scoring Functions. Liu Z et al. Acc Chem Res. (2017)

Search results

Items: 1 to 20 of 166

1.

Advancements in docking and molecular dynamics simulations towards ligand-receptor interactions and structure-function relationships.

Naqvi AAT, Mohammad T, Hasan GM, Hassan MI.

Curr Top Med Chem. 2018 Oct 25. doi: 10.2174/1568026618666181025114157. [Epub ahead of print]

PMID:
30360721
2.

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.

3.

Binding Affinity via Docking: Fact and Fiction.

Pantsar T, Poso A.

Molecules. 2018 Jul 30;23(8). pii: E1899. doi: 10.3390/molecules23081899.

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.

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.

7.

Improving classical scoring functions using random forest: The non-additivity of free energy terms' contributions in binding.

Afifi K, Al-Sadek AF.

Chem Biol Drug Des. 2018 Aug;92(2):1429-1434. doi: 10.1111/cbdd.13206. Epub 2018 Apr 27.

PMID:
29655201
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.

Multiple grid arrangement improves ligand docking with unknown binding sites: Application to the inverse docking problem.

Ban T, Ohue M, Akiyama Y.

Comput Biol Chem. 2018 Apr;73:139-146. doi: 10.1016/j.compbiolchem.2018.02.008. Epub 2018 Feb 15.

11.

Theoretical models of inhibitory activity for inhibitors of protein-protein interactions: targeting menin-mixed lineage leukemia with small molecules.

Jedwabny W, Kłossowski S, Purohit T, Cierpicki T, Grembecka J, Dyguda-Kazimierowicz E.

Medchemcomm. 2017 Dec 1;8(12):2216-2227. doi: 10.1039/c7md00170c. Epub 2017 Sep 12.

12.

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
13.

Binding affinity prediction of nanobody-protein complexes by scoring of molecular dynamics trajectories.

Soler MA, Fortuna S, de Marco A, Laio A.

Phys Chem Chem Phys. 2018 Jan 31;20(5):3438-3444. doi: 10.1039/c7cp08116b.

PMID:
29328338
14.

Vibrational entropy estimation can improve binding affinity prediction for non-obligatory protein complexes.

Škrbić T, Zamuner S, Hong R, Seno F, Laio A, Trovato A.

Proteins. 2018 Apr;86(4):393-404. doi: 10.1002/prot.25454. Epub 2018 Jan 21.

PMID:
29318668
15.

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
16.

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
17.

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
18.

Assisted Design of Antibody and Protein Therapeutics (ADAPT).

Vivcharuk V, Baardsnes J, Deprez C, Sulea T, Jaramillo M, Corbeil CR, Mullick A, Magoon J, Marcil A, Durocher Y, O'Connor-McCourt MD, Purisima EO.

PLoS One. 2017 Jul 27;12(7):e0181490. doi: 10.1371/journal.pone.0181490. eCollection 2017.

19.

Enhance the performance of current scoring functions with the aid of 3D protein-ligand interaction fingerprints.

Liu J, Su M, Liu Z, Li J, Li Y, Wang R.

BMC Bioinformatics. 2017 Jul 18;18(1):343. doi: 10.1186/s12859-017-1750-5.

20.

Application of a simple quantum chemical approach to ligand fragment scoring for Trypanosoma brucei pteridine reductase 1 inhibition.

Jedwabny W, Panecka-Hofman J, Dyguda-Kazimierowicz E, Wade RC, Sokalski WA.

J Comput Aided Mol Des. 2017 Aug;31(8):715-728. doi: 10.1007/s10822-017-0035-4. Epub 2017 Jul 7.

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