Ponzoni I - Search Results

1

QSAR Modelling to Identify LRRK2 Inhibitors for Parkinson's Disease.

Sebastián-Pérez V, Martínez MJ, Gil C, Campillo NE, Martínez A, Ponzoni I. Sebastián-Pérez V, et al. Among authors: ponzoni i. J Integr Bioinform. 2019 Feb 14;16(1):20180063. doi: 10.1515/jib-2018-0063. J Integr Bioinform. 2019. PMID: 30763264 Free PMC article.

2

QSPR models for predicting log P(liver) values for volatile organic compounds combining statistical methods and domain knowledge.

Palomba D, Martínez MJ, Ponzoni I, Díaz MF, Vazquez GE, Soto AJ. Palomba D, et al. Among authors: ponzoni i. Molecules. 2012 Dec 17;17(12):14937-53. doi: 10.3390/molecules171214937. Molecules. 2012. PMID: 23247367 Free PMC article.

3

Visual analytics in cheminformatics: user-supervised descriptor selection for QSAR methods.

Martínez MJ, Ponzoni I, Díaz MF, Vazquez GE, Soto AJ. Martínez MJ, et al. Among authors: ponzoni i. J Cheminform. 2015 Aug 19;7:39. doi: 10.1186/s13321-015-0092-4. eCollection 2015. J Cheminform. 2015. PMID: 26300983 Free PMC article.

4

Feature Learning applied to the Estimation of Tensile Strength at Break in Polymeric Material Design.

Cravero F, Martínez MJ, Vazquez GE, Díaz MF, Ponzoni I. Cravero F, et al. Among authors: ponzoni i. J Integr Bioinform. 2016 Nov 27;13(2):286. doi: 10.2390/biecoll-jib-2016-286. J Integr Bioinform. 2016. PMID: 28187416

5

Hybridizing Feature Selection and Feature Learning Approaches in QSAR Modeling for Drug Discovery.

Ponzoni I, Sebastián-Pérez V, Requena-Triguero C, Roca C, Martínez MJ, Cravero F, Díaz MF, Páez JA, Arrayás RG, Adrio J, Campillo NE. Ponzoni I, et al. Sci Rep. 2017 May 25;7(1):2403. doi: 10.1038/s41598-017-02114-3. Sci Rep. 2017. PMID: 28546583 Free PMC article.

6

GeRNet: a gene regulatory network tool.

Dussaut JS, Gallo CA, Cravero F, Martínez MJ, Carballido JA, Ponzoni I. Dussaut JS, et al. Among authors: ponzoni i. Biosystems. 2017 Dec;162:1-11. doi: 10.1016/j.biosystems.2017.08.006. Epub 2017 Aug 30. Biosystems. 2017. PMID: 28860069

7

MoDeSuS: A Machine Learning Tool for Selection of Molecular Descriptors in QSAR Studies Applied to Molecular Informatics.

Martínez MJ, Razuc M, Ponzoni I. Martínez MJ, et al. Among authors: ponzoni i. Biomed Res Int. 2019 Feb 17;2019:2905203. doi: 10.1155/2019/2905203. eCollection 2019. Biomed Res Int. 2019. PMID: 30906770 Free PMC article.

8

QSAR Classification Models for Predicting the Activity of Inhibitors of Beta-Secretase (BACE1) Associated with Alzheimer's Disease.

Ponzoni I, Sebastián-Pérez V, Martínez MJ, Roca C, De la Cruz Pérez C, Cravero F, Vazquez GE, Páez JA, Díaz MF, Campillo NE. Ponzoni I, et al. Sci Rep. 2019 Jun 24;9(1):9102. doi: 10.1038/s41598-019-45522-3. Sci Rep. 2019. PMID: 31235739 Free PMC article.

9

Feature Selection for Polymer Informatics: Evaluating Scalability and Robustness of the FS4RV_DD Algorithm Using Synthetic Polydisperse Data Sets.

Cravero F, Schustik SA, Martínez MJ, Vázquez GE, Díaz MF, Ponzoni I. Cravero F, et al. Among authors: ponzoni i. J Chem Inf Model. 2020 Feb 24;60(2):592-603. doi: 10.1021/acs.jcim.9b00867. Epub 2019 Dec 18. J Chem Inf Model. 2020. PMID: 31790226

10

Using molecular embeddings in QSAR modeling: does it make a difference?

Sabando MV, Ponzoni I, Milios EE, Soto AJ. Sabando MV, et al. Among authors: ponzoni i. Brief Bioinform. 2022 Jan 17;23(1):bbab365. doi: 10.1093/bib/bbab365. Brief Bioinform. 2022. PMID: 34498670 Review.

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