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SCIENTIFIC ARTICLES

ProtoQSAR team has published over 100 articles in high-impact scientific journals. Here is a list of some of our most representative previous articles. If you wish a copy of any of them or additional information on any of these works, please do not hesitate to, ¡contact us!

Talavera-Cortés, D., Carpio, L. E., Serrano-Candelas, P., Lafita, C., Marti, M. J. T., Baeza-Serrano, Á., Granell, P., Gozalbes, R. and Serrano-Candelas, E. “Computational Characterisation of Sulphate-Reducing Bacteria Inhibitors to Overcome Methanogenic Competence and Optimise Green Biogas Production.

Proaño-Pérez, E., Serrano-Candelas, E., Guerrero, M., Gómez-Peregrina, D., Llorens, C., Soriano, B., Gámez-Valero, A., Herrero-Lorenzo, M., Martí, E., Serrano, C. and Martin, M. “MITF regulates autophagy and extracellular vesicle cargo in gastrointestinal stromal tumors.

López-Pascual, E., Moreno-Torres, M., Moro, E., Rapisarda, A., Ortega-Vallbona, R., Serrano-Candelas, E., Gozalbes, R., Jover, R. and Castell, J.V. “Ontogeny of drug-induced fatty liver disease (DIFLD): from key initiating events to disease phenotypes.

Moreno-Torres, M., López-Pascual, E., Moro, E., Rapisarda, A., Lindeman, B., Verhoeven, A., Luechtefeld, T., Serrano-Candelas, E., Dirven, H., Vinken, M. and Castell, J.V. “Unravelling drug-induced hepatic steatosis: Clinical sub-phenotypes, outcome prediction, and identification of high-concern drugs and hazardous chemical attributes”.

Bissoli, G., Palomino-Schätzlein, M., Planes, MD., Renard, J., Krätschmer, T., Silva-Dias, C., González-Bermúdez, MR., Lozano-Juste, J., Liu, L., Wang, G., Bueso, E. “Prediction of Germination in Aged Seeds and Identification of New Seed Viability Biomarkers Using NMR Metabolomics”.

Ortega-Vallbona, R., Talavera-Cortés, D., Carpio, L.E., Palacio, J.C., Roncaglioni, A., De Lomana, M.G., Gadaleta, D., Benfenati, E., Gozalbes, R. and Serrano-Candelas, E. “DockTox: Targeting Molecular Initiating Events in Organ Toxicity through Molecular Docking”.

Vallés-Pardo, J. L., Serrano-Candelas, E., Goya-Jorge, A., Moncho, S., Crespo, M., Macmillan, D. S., & Gozalbes, R. “GenoITS: Implementation of an Integrated Testing Strategy workflow for genotoxicity using QSAR-based tools.

Lambert R, Serrano Candelas E, Aparicio P, Murphy A, Gozalbes R, Fearnhead HO. “Drug-induced cytotoxicity prediction in muscle cells, an application of the Cell Painting assay.

Moncho, S., Llobet-Mut, Á., Serrano-Candelas, E., Gozalbes, R. “Assessing the Toxicity of Quantum Dots in Healthy and Tumoral Cells with ProtoNANO, a Platform of Nano-QSAR Models to Predict the Toxicity of Inorganic Nanomaterials”.

Macmillan, D.S., Ambure, P., Aranda, V., Bayona, Y., Bonderovic, V., Dawick, J., Fabre, N., Fischer, S., Hodges, G., Llobet-Mut, Á. and Loisel-Joubert, S. “Addressing the challenges of acute fish toxicity hazard classification using a non-animal defined approach.

Ortega-Vallbona, R., Johansson, L., Carpio, L. E., Serrano-Candelas, E., Mahdizadeh, S. J., Fearnhead, H., Gozalbes, R., Eriksson, L. A. Computational Characterization of the Interaction of CARD Domains in the Apoptosome”.

Ortega-Vallbona, R.; Palomino-Schätzlein, M.; Tolosa, L.; Benfenati, E.; Ecker, G.F.; Gozalbes, R.; Serrano-Candelas, E. “Computational Strategies for Assessing Adverse Outcome Pathways: Hepatic Steatosis as a Case Study”.

Moncho S, Serrano-Candelas E, De Julián-Ortiz JV, Gozalbes R.“A review on the structural characterization of nanomaterials for nano-QSAR models”.

Carpio L E, Olivares M, Benítez-Paez A, Serrano-Candelas E, Barigye S J, Sanz Y, Gozalbes R “Comparative Binding Study of Gliptins to Bacterial DPP4-like Enzymes for the Treatment of Type 2 Diabetes Mellitus (T2DM)”.

Ortega-Vallbona R, Méndez R, Tolosa, L, Escher S E, Castell J V, Gozalbes R, Serrano Candelas, E. “Uncovering the toxicity mechanisms of a series of carboxylic acids in liver cells through computational and experimental approaches”.

Geci R, Gadaleta D, García de Lomana M, Ortega-Vallbona R, Colombo E, Serrano-Candelas E, Paini A, Kuepfer L, Schaller S. “Systematic evaluation of high-throughput PBK modelling strategies for the prediction of intravenous and oral pharmacokinetics in humans”.

Lamouroux A, Tournier M, Iaculli D, Caufriez A, Rusiecka OM, Martin C, Bes V, Carpio LE, Girardin Y, Loris R, Tabernilla A, Molica F, Gozalbes R, Mayán MD, Vinken M, Kwak BR, Ballet S. “Structure-Based Design and Synthesis of Stapled 10Panx1 Analogues for Use in Cardiovascular Inflammatory Diseases”.

Bhat-Ambure J, Ambure P, Serrano-Candelas E, Galiana-Roselló C, Gil-Martínez A, Guerrero M, Martin M, González-García J, García-España E, Gozalbes R “G4-QuadScreen: A Computational Tool for Identifying Multi-Target-Directed Anticancer Leads against G-Quadruplex DNA”.

Tolosa J, Serrano-Candelas E, Vallés-Pardo J L, Goya A, Moncho S, Gozalbes R, Palomino-Schätzlein M. “MicotoXilico: an interactive database to Predict Mutagenicity, Genotoxicity, and carcinogenicity of mycotoxins”

Serrano-Candelas E, Carpio L E, Gozalbes R. “Computational Modeling of DYRK1A Inhibitors as Potential Anti-Alzheimer Agents”

Gozalbes R, De Julián-Ortiz JV. “Applications of Chemoinformatics in Predictive Toxicology for Regulatory Purposes, Especially in the Context of the EU REACH Legislation”

Gómez-Ganau S, De Julián-Ortiz JV, Gozalbes R. “Recent advances in computational approaches for designing potential anti-alzheimer’s agents”. Springer Book “Computational Modeling of Drugs Against Alzheimer’s Disease”.  (Series: Neuromethods, Kunal Roy (ed.), Vol. 132, ISBN 978-1-4939-7404-7).

De Julián-Ortiz JV, Gozalbes R, Besalú E. “Discriminating drug-like compounds by partition trees with quantum similarity indices and graph invariants”

Goya-Jorge E, Rayar AM, Barigye SJ, Jorge Rodríguez ME, Sylla-Iyarreta Veitía M. Development of an in silico model of DPPH• Free radical scavenging capacity: prediction of antioxidant activity of coumarin type compounds”.

De Julián-Ortiz JV, Verdejo B, Polo V, Besalú E, García-España E. “Molecular rearrangement of an aza-scorpiand macrocycle induced by pH: a computational study”.

Herrero A, Pinto A, Colón-Bolea P, Casar B, Jones M, Agudo-Ibáñez L, Vidal R, Tenbaum SP, Nuciforo P, Valdizán EM, Horvath Z, Orfi L, Pineda-Lucena A, Bony E, Keri G, Rivas G, Pazos A, Gozalbes R, Palmer HG, Hurlstone A, Crespo P. “Small Molecule Inhibition of ERK Dimerization Prevents Tumorigenesis by RAS-ERK Pathway Oncogenes”

Cancer Cell. 2015, 28, 170-182

De Julian-Ortiz JV, Zanni R, Galvez-Llompart M, Garcia-Domenech R.  “The prediction of human intestinal absorption based on the molecular structure”.

Curr Drug Metab. 2014, 15, 380-388

Gozalbes R, Mosulén S, Carbajo RJ, Pineda-Lucena, A. “Hit identification of novel heparanase inhibitors by structure- and ligand-based approaches”. Bioorg Med Chem

2013, 21, 1944-1951

García-Domenech R, Zanni R, Galvez-Llompart M, de Julián-Ortiz JV. “Modeling anti-allergic natural compounds by molecular topology”. Comb Chem High Throughput Screen

2013, 16, 628-635

Bushuev Y, Sastre G, de Julián-Ortiz, Gálvez J. «Water-hydrophobic zeolite systems». J. Phys. Chem. C

2012, 116, 24916-24929

Gozalbes R, Pineda-Lucena A. «Small molecule databases and chemical descriptors useful in chemoinformatics: an overview». Comb Chem High Throughput Screen

2011, 14, 548-558

Gozalbes R, Jacewicz M, Annand R, Tsaioun K, Pineda-Lucena A. «QSAR-based permeability model for drug-like compounds». Bioorg Med Chem

2011, 19, 2615-2624

Gozalbes R, Pineda-Lucena A. «QSAR-based solubility model for drug-like compounds». Bioorg Med Chem

2010, 18, 7078-7084

Bushuev Y, Sastre G, de Julián-Ortiz JV. «The structural directing role of water and hydroxyl groups in the synthesis of beta zeolite polymorphs». J. Phys. Chem. C

2010, 114, 345–56

Valencia L, Bastida R, García-España E, de Julián-Ortiz JV, Llinares JM, Macias A, Pérez-Lourido, P. «Nitrate encapsulation within the cavity of polyazapyridinophane. Considerations on nitrate-pyridine interactions». Crystal Growth & Design

2010, 10, 3418–23

Gozalbes R, Carbajo RJ, Pineda-Lucena A. «Contributions of computational chemistry and biophysical techniques to fragment-based drug discovery». Curr Med Chem

2010, 17, 1769-1794

Gozalbes R, Mosulén S, Carbajo RJ, Pineda-Lucena A. «Development and NMR validation of minimal pharmacophore hypotheses for the generation of fragment libraries enriched in heparanase inhibitors». J Comput Aided Mol Des

2009, 23, 555-569

Gozalbes R, Barbosa F, Nicolaï E, Horvath D, Froloff N. «Development and validation of a pharmacophore-based QSAR model for the prediction of CNS activity». ChemMedChem

2009, 4, 204-209

García-Domenech R, Gálvez J, de Julián-Ortiz JV, Pogliani L.»Some new trends in chemical graph theory». Chem Rev

2008, 108, 1127-1169

Marrero-Ponce Y, Meneses-Marcel A, Rivera-Borroto OM, García-Domenech R, de Julián-Ortiz JV, Montero A, Escario JA, Barrio AG, Pereira DM, Nogal JJ, Grau R, Torrens F, Vogel C, Arán VJ. «Bond-based linear indices in QSAR: computational discovery of novel anti-trichomonal compounds». J Comput Aided Mol Des

2008, 22, 523-540

Gozalbes R, Simon L, Froloff N, Sartori E, Monteils C, Baudelle R. «Development and experimental validation of a docking strategy for the generation of kinase-targeted libraries»  J Med Chem

2008, 51, 3124-3132

Mahmoudi N, de Julián-Ortiz JV, Ciceron L, Gálvez J, Mazier D, Danis M, Derouin F, García-Domenech R. «Identification of new antimalarial drugs by linear discriminant analysis and topological virtual screening». J Antimicrob Chemother

2006, 57, 489-497

García-Domenech R, de Julián-Ortiz JV, Besalú E. «True prediction of lowest observed adverse effect levels». Mol Div

2006, 10, 159-168

Llácer M, Gálvez J, García-Domenech R, Gómez-Lechón MJ, Más-Arcas C, de Julián-Ortiz JV. «Topologicalvirtual screening and pharmacological test of novel cytostatic drugs». Internet Electron J Mol Des

2006, 5, 306-319

Anquetin G, Greiner J, Mahmoudi N, Santillana-Hayat M, Gozalbes R, Farhati K, Derouin F, Aubry A, Cambau E, Vierling P. «Design, synthesis and activity against Toxoplasma gondii, Plasmodium spp., and Mycobacterium tuberculosis of new 6-fluoroquinolones». Eur J Med Chem

2006, 41, 1478-1493

Mao B, Gozalbes R, Barbosa F, Migeon J, Merrick S, Kamm K, Wong E, Costales C, Shi W, Wu C, Froloff N. «QSAR modeling of in vitro inhibition of cytochrome P450 3A4». J Chem Inf Model

2006, 46, 2125-2134

De Julián-Ortiz JV, Besalú E. «Internal Test Sets Studies in a Group of Antimalarials». Int J. Mol Sci

2006, 456-468

Pla-Quintana A, Roglans A, de Julián-Ortiz JV, Moreno-Mañas M, Parella T, Benet-Buchholz J, Solans X. «Structural analysis of chiral complexes of palladium(o) with 15-membered triolefinic macrocyclic ligands». Chem – Eur J

2005, 11, 2689–2697

Rolland C, Gozalbes R, Nicolaï E, Paugam MF, Coussy L, Barbosa F, Horvath D, Revah F. «G-protein-coupledreceptor affinity prediction based on the use of a profiling dataset: QSAR design, synthesis, and experimental validation». J Med Chem

2005, 48, 6563-6574

Frequently asked questions

What services does ProtoQSAR offer?

ProtoQSAR is a company specialized in QSAR models and in silico toxicological predictions for regulatory support (REACH, CLP, ICH, K-REACH, etc.). We offer customized studies (QSAR, SAR, read-across, compound prioritization, etc.) and ProtoPRED®, our molecular prediction platform featuring more than 60 models covering toxicity, physicochemical properties, ecotoxicity, and ADME.

We work with companies in the pharmaceutical, chemical, cosmetic, agrochemical sectors, as well as regulatory consultancies. We also provide practical training courses in QSAR and regulatory frameworks.

Contact ProtoQSAR / Request a ProtoPRED demo

How long does it take to obtain results?

With ProtoPRED®, predictions are generated instantly. For tailor-made studies, timelines depend on the number of compounds and the scope of the study and typically range from a few weeks up to approximately two months. Upon completion, we deliver reports ready for submission to regulatory authorities (QPRF).

Tell us about your project.

What software does ProtoQSAR use to develop its models?

We use proprietary QSAR models integrated into ProtoPRED, our molecular prediction platform with more than 60 models for toxicity, physicochemical properties, ecotoxicity, and ADME.

For customized studies, we may also use third-party tools such as QSAR Toolbox and VEGA when no equivalent internal model is available in ProtoPRED, ensuring regulatory acceptability.

View list of models.

Are ProtoQSAR QSAR models accepted under REACH, CLP, ICH, or other regulations?

Yes. Our models follow OECD principles and are documented with QMRF and QPRF for each prediction, facilitating regulatory acceptance and reducing the need for experimental testing.

In addition, we have examples of regulatory acceptance of our models by different authorities.

Does ProtoQSAR participate in collaborative projects?

Yes. We collaborate in European consortia (HORIZON, IHI, etc.), national programs (Torres Quevedo, NANOTECH), and regional calls (IVACE, AVI, etc.).

At ProtoQSAR, together with Moldrug and ProtoQSAR Italy (our associated companies), we are actively seeking industrial and academic partners.

Do you have a proposal? Contact us at bd@protoqsar.com.

How much does a ProtoPRED prediction or a customized study cost?

ProtoPRED® operates on a token-based system with volume discounts: the more tokens you purchase, the lower the cost per prediction. We also offer annual licenses for teams that use the platform intensively; the license becomes cost-effective from approximately 200 predictions per year.

For customized studies, pricing depends on the number of compounds, the endpoints involved, and the level of regulatory documentation required.


Request ProtoPRED pricing options / Request a quote for a customized study

Do you provide scientific support and regulatory guidance?

Yes. In addition to generating QSAR models and predictions, we provide scientific and regulatory support, including in silico strategy design, validation and interpretation of results, and preparation/review of QMRF and QPRF so that predictions comply with OECD guidelines and/or applicable legal frameworks, meeting regulatory authority requirements.

Our regulatory team will be pleased to assist you. Contact us at regulatory@protoqsar.com.

How are confidentiality and data protection managed?

In ProtoPRED®, privacy and data protection are built into your account. From the user page (Reports data), you can activate Data Privacy to irreversibly delete all prediction results, retaining only the model used and the date.

We also offer the possibility of signing an NDA (Non-Disclosure Agreement) to ensure information security, privacy compliance, and data protection.

FAQs

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We are here to help you. Do not hesitate to contact us to receive personalized advice and resolve any questions you may have. Our team is available to offer you the best care.

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