ProtoQSAR is a company that conducts studies for its clients and collaborators, currently we do not sell molecular simulation programs, nor our QSAR models. We provide our services to groups and entities that for the most part do not have specific training in chemistry-computing and molecular modeling -or it is very limited- and therefore our expertise can represent a greater added value for them.
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!
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”.
Toxicology 2024, 504, 153764
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”
Toxins, 2023, 15, 355
Serrano-Candelas E, Carpio L E, Gozalbes R. “Computational Modeling of DYRK1A Inhibitors as Potential Anti-Alzheimer Agents”
Computational Modeling of Drugs Against Alzheimer’s Disease, Springer, 2023, 295-324
Gozalbes R, De Julián-Ortiz JV. “Applications of Chemoinformatics in Predictive Toxicology for Regulatory Purposes, Especially in the Context of the EU REACH Legislation”
Int. J. Quant. Structure-Property Relationships, 2018, 3. 1-24
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).
2018, Chapter 2, Pages 25-59
De Julián-Ortiz JV, Gozalbes R, Besalú E. “Discriminating drug-like compounds by partition trees with quantum similarity indices and graph invariants”
Curr Pharm Des. 2016, 22, 5179-5195
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.
Int J Mol Sci. 2016,17, pii: E881
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”.
Int J Mol Sci. 2016, 17, pii: E1131
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
How long does it take for a client to get the results of their project?
The projects in which we work are very different from each other, and prior to their execution we make an evaluation of the time necessary. The great advantage of computer studies compared to experimental tests is that we can obtain results in a much shorter time. For example, the prediction of the profile of a compound by QSAR methods is very fast (few days), since we already have models for this kind of evaluation. The virtual screening of a database to search for a candidate interacting with a particular target can represent 1 week to 2 months, depending on the number of chemicals filtered (a few thousands or hundreds of thousands).
Can I propose a collaborative project to ProtoQSAR? How?
At ProtoQSAR we are very interested in the development of new collaborative projects in which chemoinformatics and molecular modeling can provide an added value to our collaborators. If you have any idea to raise, please contact us explaining your proposal, and in a very short time we will give you an answer.
How much do ProtoQSAR services cost? How are the costs estimated?
Costs vary depending on the type of service we provide and the time we spend on a project. Since computing allows results to be obtained in a much shorter time than experimental tests, our services are also much cheaper.
What software does ProtoQSAR use to develop its models?
In ProtoQSAR we work with the whole palette of molecular modeling and chemoinformatics techniques, and therefore we have all the necessary programs for the execution of our projects. We systematically use standardized programs that are well referenced in the scientific literature, and sometimes we carry out our own software developments for specific needs. In any case, these developments remain the property of ProtoQSAR, and are not for sale.
What degree of confidence do the computer models have?
Computational studies are not an “exact science” and a certain degree of uncertainty is expected, as is the case with experimental tests (both in vitro and in vivo) to a certain extent. We conduct our studies according to scientifically recognized quality guidelines, and our extensive experience (demonstrable through our scientific and technical publications) makes our studies as accurate as possible. Before conducting a study, we inform our clients about the reasonable results’ expectations. At the time of providing the results, we also contribute our expert evaluation and inform the customer of the potential limitations of the obtained models.
How much time is needed to do a virtual screening? And how many compounds can be filtered?
Both questions are linked: the execution time of a virtual screening depends essentially on the number of compounds that are screened. To give a general idea, an already developed QSAR model allows the user to evaluate tens of thousands of compounds in a single day. Screening by docking (in a therapeutic target with known 3D structure) requires a longer period of time, but we can also evaluate thousands of compounds in a few days.
Can computational models be applied at the regulatory level?
Chemoinformatic models are recognized as a valid alternative at a the regulatory level, and the international regulations themselves increasingly encourage their use. For example, in the case of REACH, ECHA systematically requests companies to demonstrate that they have fully considered the use of alternative methods before concluding that a new test with vertebrates is necessary.
Can computer models replace the experimental tests required by legislation (e.g. REACH)?
Yes, in a lot of cases our models make it unnecessary to perform additional assays. This is particularly common when there is already some bibliographic information on the studied compounds, or when the products are of natural origin. In other cases the computational studies do not completely replace the experimental tests, but it is usually possible to reduce them (and therefore reduce time and costs). Another factor to consider is the credibility of the company in front of the authorities, which is clearly favored when presenting a complete dossier including the evaluation by alternative methods.
What type of model do I need for the registration/authorization of my substance: QSAR or read-across?
The type of model depends on the baseline information and on the substance to be evaluated. In principle, QSAR models are technically more robust and reliable, but they cannot always be used (for example, if the compound to be evaluated does not fall within the domain of applicability of the compounds that have been used to generate the models). In that case read-across is a good alternative, since this technique has the same validity at the regulatory level when properly executed by an experienced professional.
What is the “Applicability Domain”? How does it affect the prediction of my substance at the regulatory level?
Applicability domain is the “chemical space” to which a specific compound belongs in relation to the compounds that have served as the basis for developing a QSAR model. One of the parameters required by the OECD (and therefore necessary from a regulatory point of view) when determining whether a QSAR is valid on a given compound is the characterization of that space.
How are QSAR models validated?
All our models are subjected to a double validation, internal and external, and when possible we perform an experimental validation with independent structures belonging to the same chemical space or “domain of applicability”.
Contact us and our team will advise you on everything you need
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.
Address
ProtoQSAR SL - CEEI Valencia
Technological Park of Paterna (Valencia)
Avda. Benjamin Franklin 12, Desp. 28
46980 Paterna (Valencia)
