Mora, J. R.; Marquez, E. A.; Pérez-Pérez, N.; Contreras-Torres, E.; Perez-Castillo, Y.; Agüero-Chapin, G.; Martinez-Rios, F.; Marrero-Ponce, Y.; Barigye, S. J. Rethinking the Applicability Domain Analysis in QSAR Models. J. Comput. Aided Mol. Des. 2024, 38 (1). DOI: 10.1007/s10822-024-00550-8
Lasfar, R.; Tóth, G. The Difference of Model Robustness Assessment Using Cross‐validation and Bootstrap Methods. J. Chemom. 2024. DOI: 10.1002/cem.3530
Rodrigues, A. de P.; Luna, A. S.; Pinto, L. An Evaluation Strategy to Select and Discard Sampling Preprocessing Methods for Imbalanced Datasets: A Focus on Classification Models. Chemometr. Intell. Lab. Syst. 2023, 240 (104933), 104933. DOI: 10.1016/j.chemolab.2023.104933
Sosnowska, A.; Mudlaff, M.; Gorb, L.; Bulawska, N.; Zdybel, S.; Bakker, M.; Peijnenburg, W.; Puzyn, T. Expanding the Applicability Domain of QSPRs for Predicting Water Solubility and Vapor Pressure of PFAS. Chemosphere 2023, 340 (139965), 139965. DOI: 10.1016/j.chemosphere.2023.139965
Lowe, C. N.; Charest, N.; Ramsland, C.; Chang, D. T.; Martin, T. M.; Williams, A. J. Transparency in Modeling through Careful Application of OECD’s QSAR/QSPR Principles via a Curated Water Solubility Data Set. Chem. Res. Toxicol., 2023, 36, 465–478. DOI: 10.1021/acs.chemrestox.2c00379
Sosnowska, A.; Bulawska, N.; Kowalska, D.; Puzyn, T. Towards Higher Scientific Validity and Regulatory Acceptance of Predictive Models for PFAS. Green Chem., 2023, 25, 1261–1275. DOI: 10.1039/d2gc04341f
Gousiadou, C.; Doganis, P.; Sarimveis, H. Development of Artificial Neural Network Models to Predict the PAMPA Effective Permeability of New, Orally Administered Drugs Active against the Coronavirus SARS-CoV-2. Netw. Model. Anal. Health Inform. Bioinform., 2023, 12. DOI: 10.1007/s13721-023-00410-9
Király, P.; Kiss, R.; Kovács, D.; Ballaj, A.; Tóth, G. The Relevance of Goodness‐of‐fit, Robustness and Prediction Validation Categories of OECD‐QSAR Principles with Respect to Sample Size and Model Type. Mol. Inform., 2022, 41, 2200072. DOI: 10.1002/minf.202200072
Fliszkiewicz, B.; Sajdak, M. Fragments Quantum Descriptors in Classification of Bio-Accumulative Compounds, 2022. DOI: 10.26434/chemrxiv-2022-9h79w
Biancolillo, A.; Mennitti, L.; Foschi, M.; Marini, F. Advanced Analytical Tools for the Estimation of Gut Permeability of Compounds of Pharmaceutical Interest. Appl. Sci., 2022, 12, 1326. DOI: 10.3390/app12031326
Avdeef, A.; Kansy, M. Trends in PhysChem Properties of Newly Approved Drugs over the Last Six Years; Predicting Solubility of Drugs Approved in 2021. J. Solution Chem., 2022, 51, 1455–1481. DOI: 10.1007/s10953-022-01199-3
Avdeef, A.; Kansy, M. Predicting Solubility of Newly-Approved Drugs (2016–2020) with a Simple ABSOLV and GSE(Flexible-Acceptor) Consensus Model Outperforming Random Forest Regression. J. Solution Chem., 2022, 51, 1020–1055. DOI: 10.1007/s10953-022-01141-7
Manggara, A. B.; Ohkawa, K.; Sugimoto, M. Classifying Modes of Toxic Action of Molecules with Electronic-Structure Informatics. Application to Imbalanced Toxicity Data of Phenol Derivatives to Tetrahymena Pyriformis. Chem. Lett., 2021. DOI: 10.1246/cl.210453
Manggara, A. B.; Sugimoto, M. Extended Regression Modeling of the Toxicity of Phenol Derivatives to Tetrahymena Pyriformis Using the Electronic-Structure Informatics Descriptor. J. Comput. Aided Chem., 2021, 22, 17–22. DOI: 10.2751/jcac.22.17
Saçan, M. T.; Önlü, S.; Tugcu, G. Chemometric Modeling of Algal Toxicity. In: Roy, K. (Ed.), Chemometrics and Cheminformatics in Aquatic Toxicology, 2021, 275–291. DOI: 10.1002/9781119681397.ch14
Kovács, D.; Király, P.; Tóth, G. Sample-Size Dependence of Validation Parameters in Linear Regression Models and in QSAR. SAR QSAR Environ. Res., 2021, 1–22. DOI: 10.1080/1062936x.2021.1890208
Avdeef, A.; Kans, M. “Flexible-Acceptor” General Solubility Equation for beyond Rule of 5 Drugs. Mol. Pharm., 2020, 17(10), 3930-3940. DOI: 10.1021/acs.molpharmaceut.0c00689
Avdeef, A. Prediction of aqueous intrinsic solubility of druglike molecules using Random Forest regression trained with Wiki-pS0 database. ADMET and DMPK, 2020, 8(1), 29-77. 29-77. DOI: 10.5599/admet.766
Llinas, A.; Oprisiu, I.; Avdeef, A. Findings of the Second Challenge to Predict Aqueous Solubility. J. Chem. Inf. Model, 2020, 60, 4791–4803. DOI: 10.1021/acs.jcim.0c0070
Llinas, A.; Avdeef, A. Solubility Challenge Revisited after Ten Years, with Multilab Shake-Flask Data, Using Tight (SD ∼ 0.17 log) and Loose (SD ∼ 0.62 log) Test Sets. J. Chem. Inf. Model., 2019, 59, 3036-3040. DOI: 10.1021/acs.jcim.9b00345
Used QSAR model is cited in supplementary information
Kim, S.; Cho, K. PyQSAR: A Fast QSAR Modeling Platform Using Machine Learning and Jupyter Notebook. Bull. Korean Chem. Soc., 2019, 40, 39-44. DOI: 10.1002/bkcs.11638
Used QSAR models are not cited
Gómez-Jiménez, G.; Gonzalez-Ponce, K.; Castillo-Pazos, D. J.; Madariaga-Mazon, A.; Barroso-Flores, J.; Cortes-Guzman, F.; Martinez-Mayorga, K. The OECD Principles for (Q)SAR Models in the Context of Knowledge Discovery in Databases (KDD), In: Karabencheva-Christova, T.G.; Christov, C.Z. (Eds.), Advances in Protein Chemistry and Structural Biology, Academic Press, 2018, 85-117. DOI: 10.1016/bs.apcsb.2018.04.001
Kausar, S.; Falcao, A. O. An automated framework for QSAR model building. J. Cheminf., 2018, 10:1. DOI: 10.1186/s13321-017-0256-5
Önlü, S.; Saçan, M.T. Impact of geometry optimization methods on QSAR modelling: A case study for predicting human serum albumin binding affinity. SAR QSAR Environ. Res., 2017, 28:6, 491-509. DOI: 10.1080/1062936X.2017.1343253
Used QSAR model is cited in supplementary information
Alder, C. M.; Hayler, J. D.; Henderson, R. K.; Redman, A. M.; Shukla, L.; Shuster, L. E.; Sneddon, H. F. Updating and further expanding GSK's solvent sustainability guide. Green Chem., 2016, 18, 3879-3890. DOI: 10.1039/C6GC00611F
Used QSAR model is cited in supplementary information
Avdeef, A. Suggested Improvements for Measurement of Equilibrium Solubility-pH of Ionizable Drugs. ADMET & DMPK, 2015, 3(2), 84-109. DOI: 10.5599/admet.3.2.193
Avdeef, A. Solubility Temperature Dependence Predicted from 2D Structure. ADMET & DMPK, 2015, 3(4), 298-344. DOI: 10.5599/admet.3.4.259
QsarDB in the literature
2024
Kundu, S. A Mathematically Rigorous Algorithm to Define, Compute and Assess Relevance of the Probable Dissociation Constants in Characterizing a Biochemical Network. Sci. Rep. 2024, 14 (1). DOI: 10.1038/s41598-024-53231-9
Piir, G.; Sild, S.; Maran, U. Interpretable Machine Learning for the Identification of Estrogen Receptor Agonists, Antagonists, and Binders. Chemosphere 2024, 347 (140671), 140671. DOI: 10.1016/j.chemosphere.2023.140671
Kotli, M.; Piir, G.; Maran, U. Pesticide Effect on Earthworm Lethality via Interpretable Machine Learning. J. Hazard. Mater. 2024, 461 (132577), 132577. DOI: 10.1016/j.jhazmat.2023.132577
Roy, K.; Banerjee, A. Read-across and Quantitative Structure–Activity Relationships (QSAR) for Making Predictions and Data Gap-Filling. In SpringerBriefs in Molecular Science; Springer Nature Switzerland: Cham, 2024, pp 15–29. DOI: 10.1007/978-3-031-52057-0_2
Martinez-Mayorga, K.; Rosas-Jiménez, J. G.; Gonzalez-Ponce, K.; López-López, E.; Neme, A.; Medina-Franco, J. L. The Pursuit of Accurate Predictive Models of the Bioactivity of Small Molecules. Chem. Sci. 2024, 15 (6), 1938–1952. DOI: 10.1039/d3sc05534e
Lasfar, R.; Tóth, G. The Difference of Model Robustness Assessment Using Cross‐validation and Bootstrap Methods. J. Chemom. 2024. DOI: 10.1002/cem.3530
2023
Lasfar, R.; Tóth, G. Patch Seriation to Visualize Data and Model Parameters. J. Cheminform. 2023, 15 (1). DOI: 10.1186/s13321-023-00757-1
Rodrigues, A. de P.; Luna, A. S.; Pinto, L. An Evaluation Strategy to Select and Discard Sampling Preprocessing Methods for Imbalanced Datasets: A Focus on Classification Models. Chemometr. Intell. Lab. Syst. 2023, 240 (104933), 104933. DOI: 10.1016/j.chemolab.2023.104933
Fliszkiewicz, B.; Sajdak, M. Fragments Quantum Descriptors in Classification of Bio-Accumulative Compounds. J. Mol. Graph. Model. 2023, 125 (108584), 108584. DOI: 10.1016/j.jmgm.2023.108584
Venkatraman, V. FP-MAP: An Extensive Library of Fingerprint-Based Molecular Activity Prediction Tools. Front. Chem. 2023, 11. DOI: 10.3389/fchem.2023.1239467
Martens, M.; Stierum, R.; Schymanski, E. L.; Evelo, C. T.; Aalizadeh, R.; Aladjov, H.; Arturi, K.; Audouze, K.; Babica, P.; Berka, K.; Bessems, J.; Blaha, L.; Bolton, E. E.; Cases, M.; Damalas, D. Ε.; Dave, K.; Dilger, M.; Exner, T.; Geerke, D. P.; Grafström, R.; Gray, A.; Hancock, J. M.; Hollert, H.; Jeliazkova, N.; Jennen, D.; Jourdan, F.; Kahlem, P.; Klanova, J.; Kleinjans, J.; Kondic, T.; Kone, B.; Lynch, I.; Maran, U.; Martinez Cuesta, S.; Ménager, H.; Neumann, S.; Nymark, P.; Oberacher, H.; Ramirez, N.; Remy, S.; Rocca-Serra, P.; Salek, R. M.; Sallach, B.; Sansone, S.-A.; Sanz, F.; Sarimveis, H.; Sarntivijai, S.; Schulze, T.; Slobodnik, J.; Spjuth, O.; Tedds, J.; Thomaidis, N.; Weber, R. J. M.; van Westen, G. J. P.; Wheelock, C. E.; Williams, A. J.; Witters, H.; Zdrazil, B.; Županič, A.; Willighagen, E. L. ELIXIR and Toxicology: A Community in Development. F1000Res. 2023, 10, 1129. DOI: 10.12688/f1000research.74502.2
Cronin, M. T. D.; Belfield, S. J.; Briggs, K. A.; Enoch, S. J.; Firman, J. W.; Frericks, M.; Garrard, C.; Maccallum, P. H.; Madden, J. C.; Pastor, M.; Sanz, F.; Soininen, I.; Sousoni, D. Making in Silico Predictive Models for Toxicology FAIR. Regul. Toxicol. Pharmacol. 2023, 140 (105385), 105385. DOI: 10.1016/j.yrtph.2023.105385
Lowe, C. N.; Charest, N.; Ramsland, C.; Chang, D. T.; Martin, T. M.; Williams, A. J. Transparency in Modeling through Careful Application of OECD’s QSAR/QSPR Principles via a Curated Water Solubility Data Set. Chem. Res. Toxicol., 2023, 36, 465–478. DOI: 10.1021/acs.chemrestox.2c00379
Sosnowska, A.; Bulawska, N.; Kowalska, D.; Puzyn, T. Towards Higher Scientific Validity and Regulatory Acceptance of Predictive Models for PFAS. Green Chem., 2023, 25, 1261–1275. DOI: 10.1039/d2gc04341f
2022
Oja, M.; Sild, S.; Piir, G.; Maran, U. Intrinsic Aqueous Solubility: Mechanistically Transparent Data-Driven Modeling of Drug Substances. Pharmaceutics 2022, 14, 2248. DOI: 10.3390/pharmaceutics14102248
Tullius Scotti, M.; Herrera‐Acevedo, C.; Barros de Menezes, R. P.; Martin, H.; Muratov, E. N.; Ítalo de Souza Silva, Á.; Faustino Albuquerque, E.; Ferreira Calado, L.; Coy‐Barrera, E.; Scotti., L. MolPredictX: Online Biological Activity Predictions by Machine Learning Models. Mol. Inf., 2022, 41, 2200133. DOI: 10.1002/minf.202200133
Toots, K. M.; Sild, S.; Leis, J.; Acree, W. E., Jr.; Maran, U. Machine Learning Quantitative Structure–Property Relationships as a Function of Ionic Liquid Cations for the Gas-Ionic Liquid Partition Coefficient of Hydrocarbons. Int. J. Mol. Sci., 2022, 23, 7534. DOI: 10.3390/ijms23147534
Singh, D. P.; Kaushik, B. A Systematic Literature Review for the Prediction of Anticancer Drug Response Using Various Machine‐learning and Deep‐learning Techniques. Chemical Biology & Drug Design, 2022, 101, 175–194. DOI: 10.1111/cbdd.14164
Biancolillo, A.; Mennitti, L.; Foschi, M.; Marini, F. Advanced Analytical Tools for the Estimation of Gut Permeability of Compounds of Pharmaceutical Interest. Appl. Sci., 2022, 12, 1326. DOI: 10.3390/app12031326
Shao, J.; Gong, Q.; Yin, Z.; Pan, W.; Pandiyan, S.; Wang, L. S2DV: Converting SMILES to a Drug Vector for Predicting the Activity of Anti-HBV Small Molecules. Brief. Bioinform., 2022, 23. DOI: 10.1093/bib/bbab593
Bertato, L.; Chirico, N.; Papa, E. Predicting the Bioconcentration Factor in Fish from Molecular Structures. Toxics, 2022, 10, 581. DOI: 10.3390/toxics10100581
Király, P.; Kiss, R.; Kovács, D.; Ballaj, A.; Tóth, G. The Relevance of Goodness‐of‐fit, Robustness and Prediction Validation Categories of OECD‐QSAR Principles with Respect to Sample Size and Model Type. Mol. Inf., 2022, 41, 2200072. DOI: 10.1002/minf.202200072
Pavel, A.; Saarimäki, L. A.; Möbus, L.; Federico, A.; Serra, A.; Greco, D. The Potential of a Data Centred Approach & Knowledge Graph Data Representation in Chemical Safety and Drug Design. Comput. Struct. Biotechnol. J., 2022, 20, 4837–4849. DOI: 10.1016/j.csbj.2022.08.061
Bertato, L.; Taboureau, O.; Chirico, N.; Papa, E. Classification-Based QSARs for Predicting Dietary Biomagnification in Fish. SAR QSAR Environ. Res., 2022, 33, 259–271. DOI: 10.1080/1062936x.2022.2066174
Lombardo, A.; Manganaro, A.; Arning, J.; Benfenati, E. Development of New QSAR Models for Water, Sediment, and Soil Half-Life. Sci. Total Environ., 2022, 838, 156004. DOI: 10.1016/j.scitotenv.2022.156004
Mrani, S.A.; Arrousse, N.; Haldhar, R.; Lahcen, A.A.; Amine, A.; Saffaj, T.; Kim, S.-C.; Taleb, M. In Silico Approaches for Some Sulfa Drugs as Eco-Friendly Corrosion Inhibitors of Iron in Aqueous Medium. Lubricants 2022, 10, 43. DOI: 10.3390/lubricants10030043
Fliszkiewicz, B.; Sajdak, M. Fragments Quantum Descriptors in Classification of Bio-Accumulative Compounds, 2022. DOI: 10.26434/chemrxiv-2022-9h79w
Göller, A.H.; Kuhnke, L.; ter Laak, A.; Meier, K.; Hillisch, A. Machine Learning Applied to the Modeling of Pharmacological and ADMET Endpoints. In: Heifetz, A. (Ed.), Artificial Intelligence in Drug Design. Methods in Molecular Biology, vol 2390. Humana, 2022. DOI: 10.1007/978-1-0716-1787-8_2
Cronin, M. T. D.; Enoch, S. J.; Madden, J. C.; Rathman, J. F.; Richarz, A.-N.; Yang, C. A Review of in Silico Toxicology Approaches to Support the Safety Assessment of Cosmetics-Related Materials. Comput. Toxicol., 2022, 21, 100213. DOI: 10.1016/j.comtox.2022.100213
2021
Luxon, A. Information Architecture for a Chemical Modeling Knowledge Graph. VCU Theses and Dissertations, 2021. DOI: 10.25772/CG3N-FZ56
Lorenz, S.; Amsel, A.-K.; Puhlmann, N.; Reich, M.; Olsson, O.; Kümmerer, K. Toward Application and Implementation of in Silico Tools and Workflows within Benign by Design Approaches. ACS Sustainable Chemistry & Engineering, 2021, 9, 12461–12475. DOI: 10.1021/acssuschemeng.1c03070
Martens, M.; Stierum, R.; Schymanski, E. L.; Evelo, C. T.; Aalizadeh, R.; Aladjov, H.; Arturi, K.; Audouze, K.; Babica, P.; Berka, K.; Bessems, J.; Blaha, L.; Bolton, E. E.; Cases, M.; Damalas, D. Ε.; Dave, K.; Dilger, M.; Exner, T.; Geerke, D. P.; Grafström, R.; Gray, A.; Hancock, J. M.; Hollert, H.; Jeliazkova, N.; Jennen, D.; Jourdan, F.; Kahlem, P.; Klanova, J.; Kleinjans, J.; Kondic, T.; Kone, B.; Lynch, I.; Maran, U.; Martinez Cuesta, S.; Ménager, H.; Neumann, S.; Nymark, P.; Oberacher, H.; Ramirez, N.; Remy, S.; Rocca-Serra, P.; Salek, R. M.; Sallach, B.; Sansone, S.-A.; Sanz, F.; Sarimveis, H.; Sarntivijai, S.; Schulze, T.; Slobodnik, J.; Spjuth, O.; Tedds, J.; Thomaidis, N.; Weber, R. J. M.; van Westen, G. J. P.; Wheelock, C. E.; Williams, A. J.; Witters, H.; Zdrazil, B.; Županič, A.; Willighagen, E. L. ELIXIR and Toxicology: A Community in Development. F1000Res. 2021, 10, 1129. DOI: 10.12688/f1000research.74502.1
Toots, K. M.; Sild, S.; Leis, J.; Acree Jr., W. E.; Maran, U. The Quantitative Structure-Property Relationships for the Gas-Ionic Liquid Partition Coefficient of a Large Variety of Organic Compounds in Three Ionic Liquids. J. Mol. Liq., 2021, 343, 117573. DOI: 10.1016/j.molliq.2021.117573
Manggara, A. B.; Ohkawa, K.; Sugimoto, M. Classifying Modes of Toxic Action of Molecules with Electronic-Structure Informatics. Application to Imbalanced Toxicity Data of Phenol Derivatives to Tetrahymena Pyriformis. Chem. Lett., 2021. DOI: 10.1246/cl.210453
García-Sosa, A.T.; Maran, U. Combined Naïve Bayesian, Chemical Fingerprints and Molecular Docking Classifiers to Model and Predict Androgen Receptor Binding Data for Environmentally- and Health-Sensitive Substances. Int. J. Mol. Sci., 2021, 22, 6695. DOI: 10.3390/ijms22136695
Kovács, D.; Király, P.; Tóth, G. Sample-Size Dependence of Validation Parameters in Linear Regression Models and in QSAR. SAR QSAR Environ. Res., 2021, 32, 247–268. DOI: 10.1080/1062936x.2021.1890208
Jeddi, M.Z.; Hopf, N.B.; Viegas, S.; Price, A.B.; Paini, A.; van Thriel, C.; Benfenati, E.; Ndaw, S.; Bessems, J.; Behnisch, P.A.; Leng, G.; Duca, R.-C.; Verhagen, H.; Cubadda, F.; Brennan, L.; Ali, I.; David, A.; Mustieles, V.; Fernandez, M.F.; Louro, H.; Pasanen-Kase, R. Towards a systematic use of effect biomarkers in population and occupational biomonitoring. Environ. Int., 2021, 146, 106257. DOI: 10.1016/j.envint.2020.106257
Piir, G.; Sild, S.; Maran, U. Binary and multi-class classification for androgen receptor agonists, antagonists and binders. Chemosphere, 2021, 262:128313. DOI: 10.1016/j.chemosphere.2020.128313
Schaduangrat, N.; Lampa, S.; Simeon, S.; Gleeson, M. P.; Spjuth, O.; Nantasenamat, C. Towards Reproducible Computational Drug Discovery. J. Cheminformatics, 2020, 12.DOI: 10.1186/s13321-020-0408-x
Gressling, T. "79 QSAR: quantitative structure–activity relationship". In: Gressling, T., Data Science in Chemistry: Artificial Intelligence, Big Data, Chemometrics and Quantum Computing with Jupyter, Berlin, Boston: De Gruyter, 2020, 386-388. DOI: 10.1515/9783110629453-079
Fayet, G.; Rotureau, P. Chemoinformatics for the Safety of Energetic and Reactive Materials at Ineris. Mol. Inf., 2020, 39, 2000190. DOI: 10.1002/minf.202000190
Zukić, S.; Maran, U. Modelling of antiproliferative activity measured in HeLa
cervical cancer cells in a series of xanthene derivatives, SAR QSAR Environ. Res., 2020, 31:12, 905-921. DOI: 10.1080/1062936X.2020.1839131
Schaduangrat, N.; Lampa, S.; Simeon, S.; Gleeson, M.P.; Spjuth, O.; Nantasenamat, C. Towards reproducible computational drug discovery. J. Cheminform., 2020, 12, 9. DOI: 10.1186/s13321-020-0408-x
Gramatica, P. Principles of QSAR Modeling: Comments and Suggestions From Personal Experience. International Journal of Quantitative Structure-Property Relationships (IJQSPR), 2020, 5(3), 61-97. DOI: 10.4018/IJQSPR.20200701.oa1
Nantasenamat, C. Best Practices for Constructing Reproducible QSAR Models. In: Roy, K. (Ed.), Ecotoxicological QSARs. Methods in Pharmacology and Toxicology, Humana, 2020, 55-75. DOI: 10.1007/978-1-0716-0150-1_3
Nendza, M.; Ahlers, J.; Schwartz, D. Best Practices for Constructing Reproducible QSAR Models. In: Roy, K. (Ed.), Ecotoxicological QSARs. Methods in Pharmacology and Toxicology, Humana, 2020, 545-560. DOI: 10.1007/978-1-0716-0150-1_22
Sild, S.; Piir, G.; Neagu, D.; Maran, U. Storing and using quantitative and qualitative structure–activity relationships in the era of toxicological and chemical data expansion. In: Neagu, D.; Richarz, A. (Eds.), Big Data in Predictive Toxicology (series Issues in Toxicology), Royal Society of Chemistry, 2020, 185-213. DOI: 10.1039/9781782623656-00185
Käärik, M.; Arulepp, M.; Käärik, M.; Maran, U.; Leis, J. Characterization and prediction of double-layer capacitance of nanoporous carbon materials using the Quantitative nano-Structure-Property Relationship approach based on experimentally determined porosity descriptors. Carbon, 2020, 158, 494-504. DOI: 10.1016/j.carbon.2019.11.017
2019
Diukendjieva, A.; Tsakovska, I.; Alov, P.; Pencheva, T.; Pajeva, I.; Worth, A. P.; Madden, J. C.; Cronin, M. T. D. Advances in the Prediction of Gastrointestinal Absorption: Quantitative Structure-Activity Relationship (QSAR) Modelling of PAMPA Permeability. Computational Toxicology, 2019, 10, 51–59. DOI: 10.1016/j.comtox.2018.12.008
Benfenati, E.; Chaudhry, Q.; Gini, G.; Dorne, J. L. Integrating in Silico Models and Read-across Methods for Predicting Toxicity of Chemicals: A Step-Wise Strategy. Environment International, 2019, 131, 105060. DOI: 10.1016/j.envint.2019.105060
Vighi, M.; Barsi, A.; Focks, A.; Grisoni, F. Predictive models in ecotoxicology: Bridging the gap between scientific progress and regulatory applicability — Remarks and research needs. Integr. Environ. Assess. Manag., 2019, 15, 345-351. DOI: 10.1002/ieam.4136
Reference to the QsarDB article is with mistake in first author name
Tomonori, I.; Manami, N.; Yuta, H. Revisiting Formic Acid Decomposition by a Graph-Theoretical Approach. J. Phys. Chem. A, 2019, 123 (44), 9579-9586. DOI: 10.1021/acs.jpca.9b05994
Hernández-Alvarado, R.B.; Madariaga-Mazón, A.; Martinez-Mayorga, K. Prediction of toxicity of secondary metabolites. Phys. Sci. Rev., 2019, 4(11). DOI: 10.1515/psr-2018-0107
García-Sosa, A.T. Benford's law in medicinal chemistry: Implications for drug design. Future Med. Chem., 2019, 11:17, 2247-2253. DOI: 10.4155/fmc-2019-0006
Oja, M.; Sild, S.; Maran, U. Logistic Classification Models for pH–Permeability Profile: Predicting Permeability Classes for the Biopharmaceutical Classification System. J. Chem. Inf. Model., 2019, 59(5), 2442-2455. DOI: 10.1021/acs.jcim.8b00833
Kazmi, S.R.; Jun, R.; Yu, M.-S.; Jung, C.; Na, D. In silico approaches and tools for the prediction of drug metabolism and fate: A review. Comput. Biol. Med., 2019, 106, 54-64. DOI: 10.1016/j.compbiomed.2019.01.008
Richarz, A.-N.; Lamon, L.; Asturiol, D.; Worth, A.P. Current Developments and Recommendations in Computational Nanotoxicology in View of Regulatory Applications. In: Gajewicz, A.; Puzyn, T. (Eds.), Computational Nanotoxicology: Challenges and Perspectives. Jenny Stanford Publishing, 2019, 99-156. DOI: 10.1201/9780429341373
2018
Piir, G.; Kahn, I.; García-Sosa, A.T.; Sild, S.; Ahte, P.; Maran, U. Best Practices for QSAR Model Reporting: Physical and Chemical Properties, Ecotoxicity, Environmental Fate, Human Health, and Toxicokinetics Endpoints, Environ. Health Perspect., 2018, 126:12. DOI: 10.1289/EHP3264
Oja, M.; Maran, U. pH-permeability profiles for drug substances: Experimental detection, comparison with human intestinal absorption and modelling. Eur. J. Pharm. Sci., 2018, 123, 429-440. DOI: 10.1016/j.ejps.2018.07.014
Käärik, M.; Maran, U.; Arulepp, M.; Perkson, A.; Leis, J. Quantitative Nano-Structure–Property Relationships for the Nanoporous Carbon: Predicting the Performance of Energy Storage Materials. ACS Appl. Energy Mater., 2018, 1(8), 4016-4024. DOI: 10.1021/acsaem.8b00708
Gozalbes, R.; Vicente de Julián-Ortiz, J. Applications of Chemoinformatics in Predictive Toxicology for Regulatory Purposes, Especially in the Context of the EU REACH Legislation., 2018, 1(3), 1-24. DOI: 10.4018/ijqspr.2018010101
Nolte, T.M.; Pinto-Gil, K.; Hendriks, A.J.; Ragas, A.M.J.; Pastor, M. Quantitative structure–activity relationships for primary aerobic biodegradation of organic chemicals in pristine surface waters: starting points for predicting biodegradation under acclimatization. Environ. Sci.: Processes Impacts, 2018, 20, 157-170. DOI: 10.1039/c7em00375g
Patel, M.; Chilton, M.L.; Sartini, A.; Gibson, L.; Barber, C.; Covey-Crump, L.; Przybylak, K.R.; Cronin, M.T.D.; Madden, J.C. Assessment and Reproducibility of Quantitative Structure–Activity Relationship Models by the Nonexpert. J. Chem. Inf. Model., 2018 ,58, 673–682. DOI: 10.1021/acs.jcim.7b00523
Gramatica, P.; Papa, E.; Sangion A. QSAR modeling of cumulative environmental endpoints for the prioritization of hazardous chemicals.
Environ. Sci.: Processes Impacts, 2018, 20, 38–47.DOI: 10.1039/c7em00519a
Haase, A.; Klaessig, F. EU US Roadmap Nanoinformatics 2030, EU Nanosafety Cluster, 2018, November 15. DOI: 10.5281/zenodo.1486012
Andersson, N.; Arena, M.; Auteri, D.; Barmaz, S.; Grignard, E.; Kienzler, A.; Lepper, P.; Lostia. A. M.; Munn, S.; Parra Morte, J. M.; Pellizzato, F.; Tarazona, J.; Terron, A.; Van der Linden, S. Guidance for the identification of endocrine disruptors in the context of Regulations (EU) No 528/2012 and (EC) No 1107/2009. EFSA Journal, 2018, 16, 5311. DOI: 10.2903/j.efsa.2018.5311
Puzyn, T.; Jeliazkova, N.; Sarimveis, H.; Marchese Robinson, R.L.; Lobaskin, V.; Rallo, R.; Richarz, A.-N.; Gajewicz, A.; Papadopulos, M.G.; Hastings, J.; Cronin, M.T.D.; Benfenati, E.; Fernández, A. Perspectives from the NanoSafety Modelling Cluster on the validation criteria for (Q)SAR models used in nanotechnology. Food Chem. Toxicol., 2018, 112, 478-494. DOI: 10.1016/j.fct.2017.09.037
It should be pointed out that this article includes the following misconception while describing QsarDB: "It is implied that the nanoparticles for which descriptors are calculated can be represented using a CAS number, which is clearly inadequate for describing nanomaterials, as opposed to small molecule chemicals." In reality, QsarDB is not making such statement anywhere in the documentation nor in practical use. The CAS number is a completely optional attribute and QsarDB can use different representations to characterize structures whether small-molecular or in nano-scale. More QnSPR examples can be found in respective collection http://hdl.handle.net/10967/120. The article refers to the particular archive https://doi.org/10.15152/QDB.119, where the use of CAS was relevant (but not mandatory), because this work used monomeric forms and this helped to visualize structures on that level. QsarDB format is open and allows the adaption any suitable structure identification or representation conventions.
2017
Przybylak, K. R.; Madden, J. C.; Covey-Crump, E.; Gibson, L.; Barber, C.; Patel, M.; Cronin, M. T. D. Characterisation of data resources for in silico modelling: benchmark datasets for ADME properties. Expert Opin. Drug Metab. Toxicol., 2017, 14(2), 169–181. DOI: 10.1080/17425255.2017.1316449
Viira, B.; García-Sosa, A.T.; Maran, U. Chemical Structure and Correlation Analysis of HIV-1 NNRT and NRT Inhibitors and Database-Curated, Published Inhibition Constants with Chemical Structure in Diverse Datasets. J. Mol. Graph. Model., 2017, 76, 205–223. DOI: j.jmgm.2017.06.019
Tetko, I.V.; Maran, U.; Tropsha, A. Public (Q)SAR Services, Integrated Modeling Environments, and Model Repositories on the Web: State of the Art and Perspectives for Future Development. Mol. Inf., 2016, 36(3), 1600082., DOI: 10.1002/minf.201600082
Nolte, T.M.; Ragas, A.M.J. A Review of Quantitative Structure–Property Relationships for the Fate of Ionizable Organic Chemicals in Water Matrices and Identification of Knowledge Gaps. Environ. Sci.: Processes Impacts., 2017, 19 (3), 221–246. DOI: 10.1039/c7em00034k
Shoombuatong, W.; Prathipati, P.; Prachayasittikul, V.; Schaduangrat, N.; Malik, A.A.; Pratiwi, R.; Wanwimolruk, S.; S. Wikberg, J.E.; Gleeson, M.P.; Spjuth, O.; Nantasenamat, C. Towards Predicting the Cytochrome P450 Modulation: From QSAR to Proteochemometric Modeling. Curr. Drug Metab., 2017, 18(6), 540-555. DOI: 10.2174/1389200218666170320121932
Pham-The, H.; Nam, N.-H.; Nga, D.-V.; Hai, D. T.; Dieguez-Santana, K.; Marrero-Poncee, Y.; Castillo-Garit, J. A.; Casanola-Martin, G. M.; Le-Thi-Thu, H. Learning from Multiple Classifier Systems: Perspectives for Improving Decision Making of QSAR Models in Medicinal Chemistry. Curr. Top. Med. Chem., 2017, 17 (30), 3269–3288. DOI: 10.2174/1568026618666171212111018
Ekins, S.; Clark, A.M.; Southan, C.; Bunin, B.A.; Williams, A.J. Small-Molecule Bioactivity Databases. In: Bittker, J.A.; Ross, N.T. (Eds.), High Throughput Screening Methods: Evolution and Refinement, Royal Society of Chemistry, 2017, 344–371. DOI: 10.1039/9781782626770-00344
Willighagen, E.L.; Mayfield, J.W.; Alvarsson, J.; Berg A.; Carlsson, L.; Jeliazkova, N.; Kuhn, S.; Pluskal, T.; Rojas-Chertó, M.; Spjuth, O.; Torrance, G.; Evelo, C.T.; Guha, R.; Steinbeck, C. The Chemistry Development Kit (CDK) v2.0: atom typing, depiction, molecular formulas, and substructure searching. J. Cheminf., 2017, 9:33. DOI: 10.1186/s13321-017-0220-4
Worth, A.; Aschberger, K.; Asturiol Bofill, D.; Bessems, J.; Gerloff, K.; Graepel, R.; Joossens, E.; Lamon, L.; Palosaari, T.; Richarz, A. Evaluation of the availability and applicability of computational approaches in the safety assessment of nanomaterials., 2017, EUR 28617 EN, Publications Office of the European Union, Luxembourg, ISBN
978-92-79-68708-2, DOI: 10.2760/248139
Settivari, R.S.; Rowlands, J.C.; Wilson, D.M.; Arnold, S.M.; Spencer, P.J. Application of Evolving Computational and Biological Platforms for Chemical Safety Assessment. In: Faqi, A.S. (Ed.), A Comprehensive Guide to Toxicology in Nonclinical Drug Development, Academic Press, 2017, 843–873. DOI: 10.1016/B978-0-12-803620-4.00032-3
2016
Mangiatordi, G. F.; Alberga, D.; Altomare, C. D.; Carotti, A.; Catto, M.; Cellamare, S.; Gadaleta, D.; Lattanzi, G.; Leonetti, F.; Pisani, L.; Stefanachi, A.; Trisciuzzi, D.; Nicolotti, O. Mind the Gap! A Journey towards Computational Toxicology. Mol. Inf., 2016, 35, 294–308. DOI: 10.1002/minf.201501017
Rücker, C.; Mahmoud, W.M.M.; Kümmerer, K. REACH und QSAR: Ein Leitfaden für kleine und mittlere Unternehmen., 2015, Leuphana Universität Lüneburg.
Oja, M.; Maran, U. Quantitative Structure–Permeability Relationships at Various PH Values for Neutral and Amphoteric Drugs and Drug-like Compounds. SAR QSAR Environ. Res., 2016, 27 (10), 813–832. DOI: 10.1080/1062936x.2016.1238408
O'Hagan, S.; Kell, D. B. Understanding the foundations of the structural similarities between marketed drugs and endogenous human metabolites. Front. Pharmacol., 2015, 6:105. DOI: 10.3389/fphar.2015.00105
Oja, M.; Maran, U. Quantitative structure-permeability relationships at various pH values for acidic and basic drugs and drug-like compounds. SAR QSAR Environ. Res., 2015, 26, 701-719. DOI: 10.1080/1062936X.2015.1085896
Oja, M.; Maran, U. The Permeability of an Artificial Membrane for Wide Range of pH in Human Gastrointestinal Tract:
Experimental Measurements and Quantitative Structure-Activity Relationship. Mol. Inform., 2015, 34, 493-506. DOI: 10.1002/minf.201400147
Warr, W. A. Many InChIs and quite some feat. J. Comput. Aided Mol. Des., 2015, 29, 681-694. DOI: 10.1007/s10822-015-9854-3
Clark, A. M.; Dole, K.; Coulon-Spektor, A.; McNutt, A.; Grass, G.; Freundlich, J. S.; Reynolds, R. C.; Ekins, S. Open Source Bayesian Models. 1. Application to ADME/Tox and Drug Discovery Datasets. J. Chem. Inf. Model. 2015, 55, 1231-1245. DOI: 10.1021/acs.jcim.5b00143
Clark, A. M.; Ekins, S. Open Source Bayesian Models. 2. Mining a “Big Dataset” To Create and Validate Models with ChEMBL. J. Chem. Inf. Model., 2015, 55, 1246-1260. DOI: 10.1021/acs.jcim.5b00144
Ambure, P.; Aher, R. B.; Roy, K. Recent Advances in the Open Access Cheminformatics Toolkits, Software Tools, Workflow Environments, and Databases. In: Zhang, W. (Ed.), Computer-Aided Drug Discovery. Methods in Pharmacology and Toxicology, Humana Press, 2015, 1-40. DOI: 10.1007/7653_2014_35
Hewitt, M.; Ellison, C. M.; Cronin, M. T. D.; Pastor, M.; Steger-Hartmann, T.; Munoz-Muriendas, J.; Pognan, F.; Madden, J. C. Ensuring confidence in predictions: A scheme to assess the scientific validity of in silico models. Adv. Drug Deliv. Rev., 2015, 101-111. DOI: 10.1016/j.addr.2015.03.005
2014
Ekins, S.; Clark, A. M.; Swamidass, S. J.; Litterman, N.; Williams, A. J. Bigger data, collaborative tools and the future of predictive drug discovery. J. Comput. Aided Mol. Des., 2014, 28, 997–1008. DOI: 10.1007/s10822-014-9762-y
Piir, G.; Sild, S.; Maran, U. Classifying bio-concentration factor with random forest algorithm, influence of the bio-accumulative vs. non-bio-accumulative compound
ratio to modelling result, and applicability domain for random forest model. SAR QSAR Environ. Res., 2014, 25, 967-981. DOI: 10.1080/1062936X.2014.969310
First publication that used DOI® System identifier. Predictable models available at the repository
Aruoja, V.; Moosus, M.; Kahru, A.; Sihtmäe, M.; Maran, U. Measurement of baseline toxicity and QSAR analysis of 50 non-polar and 58 polar narcotic
chemicals for the alga Pseudokirchneriella subcapitata. Chemosphere, 2014, 96, 23-32. DOI: 10.1016/j.chemosphere.2013.06.088
First publication that includes unique persistent digital identifier by Handle System (HDL)
2013
Palczewska, A.; Fu, X.; Trundle, P.; Yang, L.; Neagu, D.; Ridley, M.; Travis, K. Towards model governance in predictive toxicology. Int. J. Inf. Manag., 2013, 33, 567–582. DOI: 10.1016/j.ijinfomgt.2013.02.005
Ruusmann, V.; Maran, U. From data point timelines to a well curated data set, data mining of experimental data and chemical
structure data from scientific articles, problems and possible solutions. J. Comput. Aided Mol. Des., 2013, 27, 583-603. DOI: 10.1007/s10822-013-9664-4
Jeliazkova, N. Web tools for predictive toxicology model building. Expert Opin. Drug Metab. Toxicol., 2012, 8, 791-801. DOI: 10.1517/17425255.2012.685158
Cronin, M.T.D. In Silico Tools for Toxicity Prediction. In: Wilson, A.G.E. (Ed.), New Horizons in Predictive Toxicology: Current Status and Application, The Royal Society of Chemistry, 2012, 9-25. DOI: 10.1039/9781849733045-00009
Mocák, J. Chemometrics in Medicine and Pharmacy. Nova Biotechnologica et Chimica, 2012, 11 (1), 11–26. DOI: 10.2478/v10296-012-0002-3
2010
Spjuth, O.; Willighagen, E. L.; Guha, R.; Eklund, M.; Wikberg, J. E. S. Towards interoperable and reproducible QSAR analyses: Exchange of datasets. J. Cheminf., 2010, 2:5. DOI: 10.1186/1758-2946-2-5