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A Brief Review of Computer-Assisted Approaches to Rational Design of Peptide Vaccines.

International journal of molecular sciences

Nandy A, Basak SC.
PMID: 27153063
Int J Mol Sci. 2016 May 04;17(5). doi: 10.3390/ijms17050666.

The growing incidences of new viral diseases and increasingly frequent viral epidemics have strained therapeutic and preventive measures; the high mutability of viral genes puts additional strains on developmental efforts. Given the high cost and time requirements for new...

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Nandy A, Basak SC. A Brief Review of Computer-Assisted Approaches to Rational Design of Peptide Vaccines. Int J Mol Sci. 2016;17(5)doi: 10.3390/ijms17050666.
Nandy, A., & Basak, S. C. (2016). A Brief Review of Computer-Assisted Approaches to Rational Design of Peptide Vaccines. International journal of molecular sciences, 17(5), . https://doi.org/10.3390/ijms17050666
Nandy, Ashesh, and Basak, Subhash C. "A Brief Review of Computer-Assisted Approaches to Rational Design of Peptide Vaccines." International journal of molecular sciences vol. 17,5 (2016). doi: https://doi.org/10.3390/ijms17050666
Nandy A, Basak SC. A Brief Review of Computer-Assisted Approaches to Rational Design of Peptide Vaccines. Int J Mol Sci. 2016 May 04;17(5). doi: 10.3390/ijms17050666. PMID: 27153063; PMCID: PMC4881492.
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Molecular dynamics: new advances in drug discovery.

European journal of medicinal chemistry

Milardi D, Pappalardo M.
PMID: 25466447
Eur J Med Chem. 2015 Feb 16;91:1-3. doi: 10.1016/j.ejmech.2014.10.078. Epub 2014 Nov 01.

No abstract available.

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Milardi D, Pappalardo M. Molecular dynamics: new advances in drug discovery. Eur J Med Chem. 2014;91:1-3doi: 10.1016/j.ejmech.2014.10.078.
Milardi, D., & Pappalardo, M. (2015). Molecular dynamics: new advances in drug discovery. European journal of medicinal chemistry, 911-3. https://doi.org/10.1016/j.ejmech.2014.10.078
Milardi, Danilo, and Pappalardo, Matteo. "Molecular dynamics: new advances in drug discovery." European journal of medicinal chemistry vol. 91 (2015): 1-3. doi: https://doi.org/10.1016/j.ejmech.2014.10.078
Milardi D, Pappalardo M. Molecular dynamics: new advances in drug discovery. Eur J Med Chem. 2015 Feb 16;91:1-3. doi: 10.1016/j.ejmech.2014.10.078. Epub 2014 Nov 01. PMID: 25466447.
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Find novel dual-agonist drugs for treating type 2 diabetes by means of cheminformatics.

Drug design, development and therapy

Liu L, Ma Y, Wang RL, Xu WR, Wang SQ, Chou KC.
PMID: 23630413
Drug Des Devel Ther. 2013 Apr 08;7:279-88. doi: 10.2147/DDDT.S42113. Print 2013.

The high prevalence of type 2 diabetes mellitus in the world as well as the increasing reports about the adverse side effects of the existing diabetes treatment drugs have made developing new and effective drugs against the disease a...

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Liu L, Ma Y, Wang RL, et al. Find novel dual-agonist drugs for treating type 2 diabetes by means of cheminformatics. Drug Des Devel Ther. 2013;7:279-88doi: 10.2147/DDDT.S42113.
Liu, L., Ma, Y., Wang, R. L., Xu, W. R., Wang, S. Q., & Chou, K. C. (2013). Find novel dual-agonist drugs for treating type 2 diabetes by means of cheminformatics. Drug design, development and therapy, 7279-88. https://doi.org/10.2147/DDDT.S42113
Liu, Lei, et al. "Find novel dual-agonist drugs for treating type 2 diabetes by means of cheminformatics." Drug design, development and therapy vol. 7 (2013): 279-88. doi: https://doi.org/10.2147/DDDT.S42113
Liu L, Ma Y, Wang RL, Xu WR, Wang SQ, Chou KC. Find novel dual-agonist drugs for treating type 2 diabetes by means of cheminformatics. Drug Des Devel Ther. 2013 Apr 08;7:279-88. doi: 10.2147/DDDT.S42113. Print 2013. PMID: 23630413; PMCID: PMC3623550.
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Score_set: a CAPRI benchmark for scoring protein complexes.

Proteins

Lensink MF, Wodak SJ.
PMID: 25179222
Proteins. 2014 Nov;82(11):3163-9. doi: 10.1002/prot.24678. Epub 2014 Sep 11.

Critical Assessment of PRedicted Interactions (CAPRI) has proven to be a catalyst for the development of docking algorithms. An essential step in docking is the scoring of predicted binding modes in order to identify stable complexes. In 2005, CAPRI...

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Lensink MF, Wodak SJ. Score_set: a CAPRI benchmark for scoring protein complexes. Proteins. 2014;82(11):3163-9doi: 10.1002/prot.24678.
Lensink, M. F., & Wodak, S. J. (2014). Score_set: a CAPRI benchmark for scoring protein complexes. Proteins, 82(11), 3163-9. https://doi.org/10.1002/prot.24678
Lensink, Marc F, and Wodak, Shoshana J. "Score_set: a CAPRI benchmark for scoring protein complexes." Proteins vol. 82,11 (2014): 3163-9. doi: https://doi.org/10.1002/prot.24678
Lensink MF, Wodak SJ. Score_set: a CAPRI benchmark for scoring protein complexes. Proteins. 2014 Nov;82(11):3163-9. doi: 10.1002/prot.24678. Epub 2014 Sep 11. PMID: 25179222.
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A selective method for optimizing ensemble docking-based experiments on an InhA Fully-Flexible receptor model.

BMC bioinformatics

De Paris R, Vahl Quevedo C, Ruiz DD, Gargano F, de Souza ON.
PMID: 29929475
BMC Bioinformatics. 2018 Jun 22;19(1):235. doi: 10.1186/s12859-018-2222-2.

BACKGROUND: In the rational drug design process, an ensemble of conformations obtained from a molecular dynamics simulation plays a crucial role in docking experiments. Some studies have found that Fully-Flexible Receptor (FFR) models predict realistic binding energy accurately and...

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De Paris R, Vahl Quevedo C, Ruiz DD, et al. A selective method for optimizing ensemble docking-based experiments on an InhA Fully-Flexible receptor model. BMC Bioinformatics. 2018;19(1):235doi: 10.1186/s12859-018-2222-2.
De Paris, R., Vahl Quevedo, C., Ruiz, D. D., Gargano, F., & de Souza, O. N. (2018). A selective method for optimizing ensemble docking-based experiments on an InhA Fully-Flexible receptor model. BMC bioinformatics, 19(1), 235. https://doi.org/10.1186/s12859-018-2222-2
De Paris, Renata, et al. "A selective method for optimizing ensemble docking-based experiments on an InhA Fully-Flexible receptor model." BMC bioinformatics vol. 19,1 (2018): 235. doi: https://doi.org/10.1186/s12859-018-2222-2
De Paris R, Vahl Quevedo C, Ruiz DD, Gargano F, de Souza ON. A selective method for optimizing ensemble docking-based experiments on an InhA Fully-Flexible receptor model. BMC Bioinformatics. 2018 Jun 22;19(1):235. doi: 10.1186/s12859-018-2222-2. PMID: 29929475; PMCID: PMC6013854.
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A Deep-Learning Approach toward Rational Molecular Docking Protocol Selection.

Molecules (Basel, Switzerland)

Jiménez-Luna J, Cuzzolin A, Bolcato G, Sturlese M, Moro S.
PMID: 32471211
Molecules. 2020 May 27;25(11). doi: 10.3390/molecules25112487.

While a plethora of different protein-ligand docking protocols have been developed over the past twenty years, their performances greatly depend on the provided input protein-ligand pair. In this study, we developed a machine-learning model that uses a combination of...

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Jiménez-Luna J, Cuzzolin A, Bolcato G, et al. A Deep-Learning Approach toward Rational Molecular Docking Protocol Selection. Molecules. 2020;25(11)doi: 10.3390/molecules25112487.
Jiménez-Luna, J., Cuzzolin, A., Bolcato, G., Sturlese, M., & Moro, S. (2020). A Deep-Learning Approach toward Rational Molecular Docking Protocol Selection. Molecules (Basel, Switzerland), 25(11), . https://doi.org/10.3390/molecules25112487
Jiménez-Luna, José, et al. "A Deep-Learning Approach toward Rational Molecular Docking Protocol Selection." Molecules (Basel, Switzerland) vol. 25,11 (2020). doi: https://doi.org/10.3390/molecules25112487
Jiménez-Luna J, Cuzzolin A, Bolcato G, Sturlese M, Moro S. A Deep-Learning Approach toward Rational Molecular Docking Protocol Selection. Molecules. 2020 May 27;25(11). doi: 10.3390/molecules25112487. PMID: 32471211; PMCID: PMC7321124.
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A high-throughput screening assay for dipeptidyl peptidase-IV inhibitors using human plasma.

Analytical methods : advancing methods and applications

Zhang J, Qian XK, Song PF, Li XD, Wang AQ, Huo H, Yao JC, Zhang GM, Zou LW.
PMID: 34036983
Anal Methods. 2021 Jun 24;13(24):2671-2678. doi: 10.1039/d1ay00415h.

Dipeptidyl peptidase-IV (DPP-IV) plays a critical role in glucose metabolism and has become an important target for type 2 diabetes mellitus. We previously reported a two-photon fluorescent probe glycyl-prolyl-N-butyl-4-amino-1,8-naphthalimide (GP-BAN) for DPP-IV detection with high specificity and sensitivity. In...

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Zhang J, Qian XK, Song PF, et al. A high-throughput screening assay for dipeptidyl peptidase-IV inhibitors using human plasma. Anal Methods. 2021;13(24):2671-2678doi: 10.1039/d1ay00415h.
Zhang, J., Qian, X. K., Song, P. F., Li, X. D., Wang, A. Q., Huo, H., Yao, J. C., Zhang, G. M., & Zou, L. W. (2021). A high-throughput screening assay for dipeptidyl peptidase-IV inhibitors using human plasma. Analytical methods : advancing methods and applications, 13(24), 2671-2678. https://doi.org/10.1039/d1ay00415h
Zhang, Jing, et al. "A high-throughput screening assay for dipeptidyl peptidase-IV inhibitors using human plasma." Analytical methods : advancing methods and applications vol. 13,24 (2021): 2671-2678. doi: https://doi.org/10.1039/d1ay00415h
Zhang J, Qian XK, Song PF, Li XD, Wang AQ, Huo H, Yao JC, Zhang GM, Zou LW. A high-throughput screening assay for dipeptidyl peptidase-IV inhibitors using human plasma. Anal Methods. 2021 Jun 24;13(24):2671-2678. doi: 10.1039/d1ay00415h. PMID: 34036983.
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Repurposing approved drugs as inhibitors of SARS-CoV-2 S-protein from molecular modeling and virtual screening.

Journal of biomolecular structure & dynamics

de Oliveira OV, Rocha GB, Paluch AS, Costa LT.
PMID: 32448085
J Biomol Struct Dyn. 2021 Jul;39(11):3924-3933. doi: 10.1080/07391102.2020.1772885. Epub 2020 Jun 02.

Herein, molecular modeling techniques were used with the main goal to obtain candidates from a drug database as potential targets to be used against SARS-CoV-2. This novel coronavirus, responsible by the COVID-19 outbreak since the end of 2019, became...

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de Oliveira OV, Rocha GB, Paluch AS, et al. Repurposing approved drugs as inhibitors of SARS-CoV-2 S-protein from molecular modeling and virtual screening. J Biomol Struct Dyn. 2020;39(11):3924-3933doi: 10.1080/07391102.2020.1772885.
de Oliveira, O. V., Rocha, G. B., Paluch, A. S., & Costa, L. T. (2021). Repurposing approved drugs as inhibitors of SARS-CoV-2 S-protein from molecular modeling and virtual screening. Journal of biomolecular structure & dynamics, 39(11), 3924-3933. https://doi.org/10.1080/07391102.2020.1772885
de Oliveira, Osmair Vital, et al. "Repurposing approved drugs as inhibitors of SARS-CoV-2 S-protein from molecular modeling and virtual screening." Journal of biomolecular structure & dynamics vol. 39,11 (2021): 3924-3933. doi: https://doi.org/10.1080/07391102.2020.1772885
de Oliveira OV, Rocha GB, Paluch AS, Costa LT. Repurposing approved drugs as inhibitors of SARS-CoV-2 S-protein from molecular modeling and virtual screening. J Biomol Struct Dyn. 2021 Jul;39(11):3924-3933. doi: 10.1080/07391102.2020.1772885. Epub 2020 Jun 02. PMID: 32448085; PMCID: PMC7284156.
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DTI-CDF: a cascade deep forest model towards the prediction of drug-target interactions based on hybrid features.

Briefings in bioinformatics

Chu Y, Kaushik AC, Wang X, Wang W, Zhang Y, Shan X, Salahub DR, Xiong Y, Wei DQ.
PMID: 31885041
Brief Bioinform. 2021 Jan 18;22(1):451-462. doi: 10.1093/bib/bbz152.

Drug-target interactions (DTIs) play a crucial role in target-based drug discovery and development. Computational prediction of DTIs can effectively complement experimental wet-lab techniques for the identification of DTIs, which are typically time- and resource-consuming. However, the performances of the...

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Chu Y, Kaushik AC, Wang X, et al. DTI-CDF: a cascade deep forest model towards the prediction of drug-target interactions based on hybrid features. Brief Bioinform. 2021;22(1):451-462doi: 10.1093/bib/bbz152.
Chu, Y., Kaushik, A. C., Wang, X., Wang, W., Zhang, Y., Shan, X., Salahub, D. R., Xiong, Y., & Wei, D. Q. (2021). DTI-CDF: a cascade deep forest model towards the prediction of drug-target interactions based on hybrid features. Briefings in bioinformatics, 22(1), 451-462. https://doi.org/10.1093/bib/bbz152
Chu, Yanyi, et al. "DTI-CDF: a cascade deep forest model towards the prediction of drug-target interactions based on hybrid features." Briefings in bioinformatics vol. 22,1 (2021): 451-462. doi: https://doi.org/10.1093/bib/bbz152
Chu Y, Kaushik AC, Wang X, Wang W, Zhang Y, Shan X, Salahub DR, Xiong Y, Wei DQ. DTI-CDF: a cascade deep forest model towards the prediction of drug-target interactions based on hybrid features. Brief Bioinform. 2021 Jan 18;22(1):451-462. doi: 10.1093/bib/bbz152. PMID: 31885041.
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Identification of potential CRAC channel inhibitors: Pharmacophore mapping, 3D-QSAR modelling, and molecular docking approach.

SAR and QSAR in environmental research

Bhuvaneshwari S, Sankaranarayanan K.
PMID: 30773908
SAR QSAR Environ Res. 2019 Feb;30(2):81-108. doi: 10.1080/1062936X.2019.1566172. Epub 2019 Feb 18.

Upregulation of store-operated Ca

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Bhuvaneshwari S, Sankaranarayanan K. Identification of potential CRAC channel inhibitors: Pharmacophore mapping, 3D-QSAR modelling, and molecular docking approach. SAR QSAR Environ Res. 2019;30(2):81-108doi: 10.1080/1062936X.2019.1566172.
Bhuvaneshwari, S., & Sankaranarayanan, K. (2019). Identification of potential CRAC channel inhibitors: Pharmacophore mapping, 3D-QSAR modelling, and molecular docking approach. SAR and QSAR in environmental research, 30(2), 81-108. https://doi.org/10.1080/1062936X.2019.1566172
Bhuvaneshwari, S, and Sankaranarayanan, K. "Identification of potential CRAC channel inhibitors: Pharmacophore mapping, 3D-QSAR modelling, and molecular docking approach." SAR and QSAR in environmental research vol. 30,2 (2019): 81-108. doi: https://doi.org/10.1080/1062936X.2019.1566172
Bhuvaneshwari S, Sankaranarayanan K. Identification of potential CRAC channel inhibitors: Pharmacophore mapping, 3D-QSAR modelling, and molecular docking approach. SAR QSAR Environ Res. 2019 Feb;30(2):81-108. doi: 10.1080/1062936X.2019.1566172. Epub 2019 Feb 18. PMID: 30773908.
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The Impact of Protein Structure and Sequence Similarity on the Accuracy of Machine-Learning Scoring Functions for Binding Affinity Prediction.

Biomolecules

Li H, Peng J, Leung Y, Leung KS, Wong MH, Lu G, Ballester PJ.
PMID: 29538331
Biomolecules. 2018 Mar 14;8(1). doi: 10.3390/biom8010012.

It has recently been claimed that the outstanding performance of machine-learning scoring functions (SFs) is exclusively due to the presence of training complexes with highly similar proteins to those in the test set. Here, we revisit this question using...

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Li H, Peng J, Leung Y, et al. The Impact of Protein Structure and Sequence Similarity on the Accuracy of Machine-Learning Scoring Functions for Binding Affinity Prediction. Biomolecules. 2018;8(1)doi: 10.3390/biom8010012.
Li, H., Peng, J., Leung, Y., Leung, K. S., Wong, M. H., Lu, G., & Ballester, P. J. (2018). The Impact of Protein Structure and Sequence Similarity on the Accuracy of Machine-Learning Scoring Functions for Binding Affinity Prediction. Biomolecules, 8(1), . https://doi.org/10.3390/biom8010012
Li, Hongjian, et al. "The Impact of Protein Structure and Sequence Similarity on the Accuracy of Machine-Learning Scoring Functions for Binding Affinity Prediction." Biomolecules vol. 8,1 (2018). doi: https://doi.org/10.3390/biom8010012
Li H, Peng J, Leung Y, Leung KS, Wong MH, Lu G, Ballester PJ. The Impact of Protein Structure and Sequence Similarity on the Accuracy of Machine-Learning Scoring Functions for Binding Affinity Prediction. Biomolecules. 2018 Mar 14;8(1). doi: 10.3390/biom8010012. PMID: 29538331; PMCID: PMC5871981.
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SAnDReS a Computational Tool for Statistical Analysis of Docking Results and Development of Scoring Functions.

Combinatorial chemistry & high throughput screening

Xavier MM, Heck GS, Avila MB, Levin NMB, Pintro VO, Carvalho NL, Azevedo WF.
PMID: 27686428
Comb Chem High Throughput Screen. 2016;19(10):801-812. doi: 10.2174/1386207319666160927111347.

BACKGROUND: Docking allows to predict ligand binding to proteins, since the 3D-structure for the target is available. Several docking studies have been carried out to identify potential ligands for drug targets. Many of these studies resulted in the leads...

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Xavier MM, Heck GS, Avila MB, et al. SAnDReS a Computational Tool for Statistical Analysis of Docking Results and Development of Scoring Functions. Comb Chem High Throughput Screen. 2016;19(10):801-812doi: 10.2174/1386207319666160927111347.
Xavier, M. M., Heck, G. S., Avila, M. B., Levin, N. M. B., Pintro, V. O., Carvalho, N. L., & Azevedo, W. F. (2016). SAnDReS a Computational Tool for Statistical Analysis of Docking Results and Development of Scoring Functions. Combinatorial chemistry & high throughput screening, 19(10), 801-812. https://doi.org/10.2174/1386207319666160927111347
Xavier, Mariana Morrone, et al. "SAnDReS a Computational Tool for Statistical Analysis of Docking Results and Development of Scoring Functions." Combinatorial chemistry & high throughput screening vol. 19,10 (2016): 801-812. doi: https://doi.org/10.2174/1386207319666160927111347
Xavier MM, Heck GS, Avila MB, Levin NMB, Pintro VO, Carvalho NL, Azevedo WF. SAnDReS a Computational Tool for Statistical Analysis of Docking Results and Development of Scoring Functions. Comb Chem High Throughput Screen. 2016;19(10):801-812. doi: 10.2174/1386207319666160927111347. PMID: 27686428.
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Showing 1 to 12 of 76 entries