BioChemTICs

Publications

• Crozes, T., Ulzurrun, E., Páez, J.A., Campillo, N.E., Soto, A.J., Ponzoni, I. “A Multimodal Sequence-to-Sequence Model for Automatic Assignment of ATC Codes in Drug Discovery and Repurposing”. Journal of Chemical Information and Modeling. American Chemical Society. Vol. 66(8), pp. 4472–4483 (2026). American Chemical Society. ISSN: 1549-960X. [doi: 10.1021/acs.jcim.6c00118]
• Cravero, F., Ponzoni, I., Díaz, M.F., Vazquez, G.E. “From prediction to understanding: A review of XAI applications and innovations in materials science”. Computational Materials Science, Vol. 267, Art. N° 114493 (2026). Elsevier. ISSN: 0927-0256. [doi: 10.1016/j.commatsci.2026.114493]. 
• Rojas, M.G., Olivera, A.C., Carballido, J.A., Vidal, J.P. “A New Micro-genetic Crossover Operator for Effective Training of Medical Neural Networks”. SN Computer Science, Vol. 6, 539 (2025). Springer Nature. ISSN: 2661-8907 [doi: 10.1007/s42979-025-04077-z]
• Cravero, F., Ponzoni, I., Díaz, M.F. “Can we gain insight about the ductile behavior of materials by using polymer informatics?”. Chemometrics and Intelligent Laboratory Systems, Vol. 244, 105025 (2024). Elsevier. ISSN: 0169-7439. [doi: 10.1016/j.chemolab.2023.105025]. 
• Ponzoni, I., Páez, J.A., Campillo, N.E. “Explainable artificial intelligence: A taxonomy and guidelines for its application to drug discovery”. Wiley Interdisciplinary Reviews: Computational Molecular Science, e1681 (2023). Wiley. ISSN: 1759-0884. [doi: 10.1002/wcms.1681]
• Rojas, M.G., Olivera, A.C., Carballido, J.A., Vidal, P.J. “Memetic micro-genetic algorithms for cancer data classification”. Intelligent Systems with Applications, Vol. 17, 200173 (2023). Elsevier. ISSN 2667-3053. [doi: 10.1016/j.iswa.2022.200173]
• Martínez, M.J., Naveiro, R., Soto, A.J., Talavante, P., Kim Lee, S.H., Gómez Arrayas, R., Franco, M., Mauleón, P., Lozano Ordóñez, H., Revilla López, G., Bernabei, M., Nuria E. Campillo, N.E, Ponzoni, I. “Design of new dispersants using machine learning and visual analytics”. Polymers, 15(5), 1324 (2023). MDPI. ISSN: 2073-4360. [doi: 10.3390/polym15051324]  
• Petrini, I., Cecchini, R.L., Mascaró, M., Ponzoni, I., Carballido, J.A. “Papillary Thyroid Carcinoma: A thorough Bioinformatic Analysis of Gene Expression and Clinical Data”. Genes, 14(6), 1250 (2023). MDPI. ISSN: 2073-4425. [doi: 10.3390/genes14061250]  
• Cardoso Schwindt, V., Coletto, M.M., Díaz, M.F., Ponzoni, I. “Could QSOR modelling and machine learning techniques be useful to predict wine aroma?”. Food and Bioprocess Technology, Vol. 16, 24–42 (2023). Springer. ISSN: 1935-5130. [doi: 10.1007/s11947-022-02836-x] 
• Martínez, M.J., Sabando, M.V., Soto, A.J., Roca, C., Requena-Triguero, C., Campillo,  N.E., Páez, J.A., Ponzoni, I. “Multitask Deep Neural Networks for Ames Mutagenicity Prediction”. Journal of Chemical Information and Modeling, 62(24), 6342-6351 (2022). American Chemical Society. ISSN: 1549-960X. [doi: 10.1021/acs.jcim.2c00532]
• Cravero, F., Díaz, M.F., Ponzoni, I. “Polymer informatics for QSPR prediction of tensile mechanical properties. Case study: Strength at break”. The Journal of Chemical Physics, Vol. 156, 204903 (2022). Special topic issue: Chemical Design by Artificial Intelligence. American Institute of Physics (AIP). ISSN: 0021-9606. [doi: 10.1063/5.0087392].
• Carballido, J.A., Ponzoni, I., Cecchini, R.L. “Filtering non-balanced data using an evolutionary approach”. Logic Journal of the IGPL, Vol. 30, jzac018 (2022). Oxford University Press. ISSN: 1367-0751. [doi: 10.1093/jigpal/jzac018].
• Cravero, F., Schustik, S., Martínez, M.J., Díaz, M.F., Ponzoni, I. “How can polydispersity information be integrated in the QSPR modeling of mechanical properties?”. Science and Technology of Advanced Materials: Methods, Vol. 2(1), pp. 1-13 (2022). Taylor & Francis. ISSN: 2766-0400. [doi: 10.1080/27660400.2021.2012540].
• Sabando, M.V., Ponzoni I., Milios E., Soto A.J. “Using Molecular Embeddings in QSAR Modeling: Does it Make a Difference?”. Briefings in Bioinformatics, Vol. 23 (1), bbab365 (2022), Oxford University Press. ISSN: 1467-5463. [doi: 10.1093/bib/bbab365]
• Schustik, S., Cravero, F., Ponzoni, I., Díaz, M.F. “Polymer informatics: Expert-in-the-loop in QSPR modeling of refractive index”. Computational Materials Science, Vol. 194, Art. N° 110460 (2021). Elsevier. ISSN: 0927-0256. [doi: 10.1016/j.commatsci.2021.110460] 
• Schustik, S., Cravero F., Martínez M.J., Ponzoni I., Díaz M. “PolyMaS: A new software to generate high molecular weight polymer macromolecules from repeating structural units”. Polimery, Vol. 66(5), pp. 293-297 (2021). Elsevier. ISSN: 0169-7439. [doi: 10.14314/POLIMERY.2021.5.2] 
• Sabando, M.V., Ulbrich, P., Selzer, M., J. Byska, J. Mican, Ponzoni I., Soto A.J., Ganuza, M.L., Barbora Kozlikova, B., ChemVA: Interactive Visual Analysis of Chemical Compound Similarity in Virtual Screening, IEEE Transactions on Visualization and Computer Graphics, Vol. 27(2), pp. 891-901 (2021). IEEE Computer Society. ISSN: 1077-2626. [doi: 10.1109/TVCG.2020.3030438].
• Cravero, F., Schustik, S., Martínez, M.J., Vazquez, E.G., Díaz, M.F., Ponzoni, I. “Feature Selection for Polymer Informatics: Evaluating Scalability and Robustness of the FS4RV_DD Algorithm using Synthetic Polydisperse Datasets”. Journal of Chemical Information and Modeling, Vol. 60(2), pp. 592-603 (2020). American Chemical Society. ISSN: 1549-960X. [doi: 10.1021/acs.jcim.9b00867]
• Cravero F., Martínez M.J., Ponzoni I., Díaz M. “Computational modelling of mechanical properties for new polymeric materials with high molecular weight”. Chemometrics and Intelligent Laboratory Systems, Vol. 193, 103851 (2019). Elsevier. ISSN: 0169-7439. [doi: 10.1016/j.chemolab.2019.103851]
• Sabando, M.V., Ponzoni I., Soto A.J. “Neural-based Approaches to Overcome Feature Selection and Applicability Domain in Drug-related Property Prediction”. Applied Soft Computing, Vol. 85, 105777 (2019). Elsevier. ISSN: 1568-4946. [doi: 10.1016/j.asoc.2019.105777]  
• Cravero, F., Schustik, S., Martínez, M.J., Barranco, C.D., Díaz, M.F., Ponzoni, I. “Computer-Aided Design of Polymeric Materials: Characterization of Databases for Prediction of Mechanical Properties under Polydispersity”. Chemometrics and Intelligent Laboratory Systems, Vol. 191, pp. 65-72 (2019). Elsevier. ISSN: 0169-7439. [doi: 10.1016/j.chemolab.2019.06.006]
• Ponzoni I., Sebastian-Pérez V., Martínez M.J., Roca C., De la Cruz, C., Cravero F., Vazquez, G.E., Páez J.A., Díaz M.F., Campillo N.E. “QSAR Classification Models for Predicting the Activity of Inhibitors of Beta-Secretase (BACE1) Associated with Alzheimer’s Disease”. Scientific Reports, Vol. 9:9102, (2019). Nature Pub. Group. ISSN: 2045-2322. [doi: 10.1038/s41598-019-45522-3]
• Díaz–Montaña, J.J., Díaz–Díaz, N., Barranco, C.D., Ponzoni, I. “Development and use of a Cytoscape app for GRNCOP2”, Computer Methods and Programs in Biomedicine. Vol. 177, pp. 211–218 (2019). Elsevier. ISSN: 0169-2607. [doi: 10.1016/j.cmpb.2019.05.030]
• Sebastián-Pérez, V., Martínez, M.J., Gil, C., Campillo, N.E., Martínez, A., Ponzoni, I. “Inference of QSAR Models for identifying LRRK2 inhibitors for the treatment of Parkinson’s Disease”, Journal of Integrative Bioinformatics. Vol. 16, Issue 1 (2019). De Gruyter. ISSN: 1613-4516. [doi: 10.1515/jib-2018-0063]
• Martínez, M.J., Razuc, M., Ponzoni I. “MoDeSuS: A Machine Learning Tool for Selection of Molecular Descriptors in QSAR Studies applied to Molecular Informatics”, BioMed International Research. Vol. 2019, Article number 2905203 (2019). Hindawi. ISSN: 2314-6133. [doi: 10.1155/2019/2905203]
• Cecchini, R.L., Lorenzetti, C.M., Maguitman, A.G., Ponzoni, I. “Topic Relevance and Diversity in Information Retrieval from Large Datasets: A Multi-Objective Evolutionary Algorithm Approach”, Applied Soft Computing. Vol. 69, pp. 749-770 (2018). Elsevier. ISSN: 1568-4946. [doi: 10.1016/j.asoc.2017.11.016]
• Martínez M.J., Dussaut J.S., Ponzoni, I. “Biclustering as Strategy for Improving Feature Selection in Consensus QSAR Modeling”, Electronic Notes in Discrete Mathematics, Vol. 69, pp. 117-124 (2018). Elsevier. ISSN: 1571-0653. [doi: 10.1016/j.endm.2018.07.016]
• Dussaut J.S., Cecchini, R.L., Gallo, C.A., Ponzoni, I., Carballido, J.A. “A Review of Software Tools for Pathway Crosstalk Inference”, Currents Bioinformatics, 13 (1), pp. 64-72 (2018). Bentham Science.ISSN: 1574-8936. [doi: 10.2174/1574893611666161123123204]
• Carballido, J.A., Latini M.A., Ponzoni, I., Cecchini, R.L. “An Evolutionary Algorithm for Automatic Recommendation of Clustering Methods and its Parameters”, Electronic Notes in Discrete Mathematics, Vol. 69, pp. 229-236 (2018). Elsevier. ISSN: 1571-0653. [doi: 10.1016/j.endm.2018.07.030]
• Dussaut J.S., Gallo, C.A., Martínez M.J., Cravero F., Carballido, J.A., Ponzoni, I. “GeRNet: A Gene Regulatory Network Tool”, Biosystems. Vol. 162, pp. 1-11, (2017). Elsevier. ISSN: 0303-2647. [doi: 10.1016/j.biosystems.2017.08.006]
• Ponzoni I., Sebastian-Pérez, Requena C., Roca C., Martínez M.J., Cravero F., Díaz M.F., Páez J.A., Gomez Arrayas R., Adrio J., Campillo N.E. “Hybridizing Feature Selection and Feature Learning Approaches in QSAR Modeling for Drug Discovery”, Scientific Reports. Vol. 7:2403, (2017). Nature Pub. Group. ISSN: 2045-2322. [doi: s41598-017-02114-3]
• Cravero F., Martínez M.J., Vázquez G., Díaz M., Ponzoni I. “Feature Learning applied to the Estimation of Tensile Strength at Break in Polymeric Material Design”, Journal of Integrative Bioinformatics. Vol. 13, No. 2, 286 (2016). De Gruyter. ISSN: 1613-4516. [doi: 10.2390/biecoll-jib-2016-286]
• Romero J.R., Carballido J.A., Garbus I., Echenique V.C., Ponzoni I. “A Bioinformatics Approach for Detecting Repetitive Nested Motifs using Pattern Matching”, Evolutionary Bioinformatics. Vol. 12, pp. 247-251, (2016). SAGE Publishing. ISSN: 1176-9343. [doi: 10.4137/EBO.S40138]
• Gallo, C.A., Cecchini, R.L., Carballido, J.A., Micheletto, S., Ponzoni, I. “Discretization of gene expression data revised”, Briefings in Bioinformatics. Vol. 17 (5), pp. 758-770, (2016). Oxford University Press. ISSN: 1467-5463. [doi: 10.1093/bib/bbv074]
• Dussaut J.S., Gallo, C.A., Cecchini, R.L., Carballido, J.A., Ponzoni, I. “Crosstalk Pathway Inference using Topological Information and Biclustering of Gene Expression Data”, Biosystems, Vol.150, pp. 1-12, (2016). Elsevier. ISSN: 0303-2647.[doi: 10.1016/j.biosystems.2016.08.002]
• Martínez, M.J., Ponzoni, I., Díaz, M.F., Vazquez, G.E., Soto, A.J. “Visual analytics in cheminformatics: user‑supervised descriptor selection for QSAR methods”, Journal of Cheminformatics, Vol. 7, Paper 39, (2015). Springer. ISSN 1758-2946. [doi: 10.1186/s13321-015-0092-4]
• Carballido, J.A., Gallo, C.A., Dussaut J.S., Ponzoni, I. “On Evolutionary Algorithms for Biclustering of Gene Expression Data”, Currents Bioinformatics, Vol. 10, No. 3, pp. 259-267, (2015). Bentham Science.ISSN: 1574-8936. [doi: 10.2174/1574893609666140829204739].
• Ponzoni I., Nueda M.J., Tarazona S., Götz S., Montaner D., Dussaut J.S., Dopazo J., Conesa A. “Pathway network inference from gene expression data”, BMC Systems Biology, Vol. 8, S7. Springer Nature, (2014).ISSN: 1752-0509.[doi: 10.1186/1752-0509-8-S2-S7]
• Romero, J.R., Roncallo, P.F., Akkiraju, P.C., Ponzoni, I., Echenique, V.C., Carballido, J.A. “Using classification algorithms for predicting durum wheat yield in the province of Buenos Aires”, Computers and Electronics in Agriculture, Vol. 96, pp. 173-179. Elsevier, (2013). ISSN: 0168-1699. [doi: 10.1016/j.compag.2013.05.006]
• Palomba, D., Martínez, M.J., Ponzoni, I., Díaz, M.F., Vazquez, G.E., Soto, A.J. “QSAR models for predicting log Pliver on volatile organic compounds combining statistical methods and domain knowledge”, Molecules, Vol. 17, No. 12, pp. 14937-14953. MDPI AG, (2012). ISSN 1420-3049. [doi: 10.3390/molecules171214937]
• Cecchini, R.L., Ponzoni, I., Carballido, J.A. “Multi-objective evolutionary approaches for intelligent design of sensor networks in the petrochemical industry”, Expert Systems with Applications, Vol. 39, pp. 2643-2649, Elsevier, (2012). ISSN: 0957-4174. [doi: 10.1016/j.eswa.2011.08.119]
• Soto, A.J., Vazquez, G.E., Strickert, M., Ponzoni, I. “Target-driven subspace mapping methods and their applicability domain estimation”, Molecular Informatics, Vol. 30, pp. 779–789, Wiley, (2011). ISSN: 1868-1751. [doi: 10.1002/minf.201100053]
• Gallo, C.A., Carballido, J.A., Ponzoni, I. “Discovering Time-Lagged Rules from Microarray Data using Gene Profile Classifiers”, BMC Bioinformatics. Vol. 12, paper 123, Springer Nature, (2011). ISSN: 1471-2105. [doi: 10.1186/1471-2105-12-123]
• Soto, A.J., Cecchini, R.J., Vazquez, G.E., Ponzoni, I. “Multi-Objective Feature Selection in QSAR/ QSPR using a Machine Learning Approach”, QSAR & Combinatorial Science. Vol. 28, No. 11-12, pp. 1509-1523. Wiley, (2009). ISSN: 1611-020X. [doi: 10.1002/qsar.200960053]
• Carballido, J.A., Ponzoni, I., Brignole, N.B. “SID-GA: an Evolutionary Approach for improving Observability and Redundancy Analysis in Structural Instrumentation Design”. Computers & Industrial Engineering. Vol. 56, No. 4, pp. 1419-1428, (2009). Elsevier. ISSN: 0360-8352. [doi: 10.1016/j.cie.2008.09.001]
• Domancich, A.O., Durante, M., Ferraro, S., Hoch, P., Brignole N.B., Ponzoni I. “How To Improve the Model Partitioning in a DSS for Instrumentation Design”, Industrial & Engineering Chemistry Research. Vol. 48, No. 7, pp. 3513-3525, (2009). American Chemical Society. ISSN: 0888-5885. [doi: 10.1021/ie800449t]
• Ponzoni, I., Azuaje, F.J., Augusto, J.C., Glass, D.H. “Inferring adaptive regulation thresholds and association rules from gene expression data through combinatorial optimization learning”. IEEE/ACM Transactions on Computational Biology and Bioinformatics. Vol. 4, No. 4, pp. 624-634 (2007). IEEE Computer Society. ISSN: 1545-5963. [doi: 10.1109/tcbb.2007.1049]
• Carballido, J.A., Ponzoni, I., Brignole, N.B. “CGD-GA: A Graph-based Genetic Algorithm for Sensor Network Design”. Information Sciences. Vol. 177, No. 22, pp. 5091–5102 (2007). Elsevier. ISSN: 0020-0255. [doi: 10.1016/j.ins.2007.05.036]