Here is a list of scientific publications citing Alvascience’s software solutions:
2021
Falcón-Cano, G., Molina, C., & Cabrera-Pérez, M. A. (2021). ADME prediction with KNIME: A retrospective contribution to the second “Solubility Challenge.” ADMET and DMPK, 9(3), 209–218. https://doi.org/10.5599/admet.979
Zhu, T., Chen, W., Jafvert, C. T., Fu, D., Cheng, H., Chen, M., & Wang, Y. (2021). Development of novel experimental and modelled low density polyethylene (LDPE)-water partition coefficients for a range of hydrophobic organic compounds. Environmental Pollution, 291(September), 118223. https://doi.org/10.1016/j.envpol.2021.118223
Khan, P. M., & Roy, K. (2021). QSPR modelling for investigation of different properties of aminoglycoside-derived polymers using 2D descriptors. SAR and QSAR in Environmental Research, 32(7), 595–614. https://doi.org/10.1080/1062936X.2021.1939150
Mamada, H., Nomura, Y., & Uesawa, Y. (2021). Prediction Model of Clearance by a Novel Quantitative Structure–Activity Relationship Approach, Combination DeepSnap-Deep Learning and Conventional Machine Learning. ACS Omega, 6(36), 23570–23577. https://doi.org/10.1021/acsomega.1c03689
Aher, R. B., & Sarkar, D. (2021). 2D-QSAR modeling and two-fold classification of 1,2,4-triazole derivatives for antitubercular potency against the dormant stage of Mycobacterium tuberculosis. Molecular Diversity, 0123456789. https://doi.org/10.1007/s11030-021-10254-y
Casanova-Alvarez, O., Morales-Helguera, A., Cabrera-Pérez, M. Á., Molina-Ruiz, R., & Molina, C. (2021). A Novel Automated Framework for QSAR Modeling of Highly Imbalanced Leishmania High-Throughput Screening Data. Journal of Chemical Information and Modeling, 61(7), 3213–3231. https://doi.org/10.1021/acs.jcim.0c01439
Tinkov, O. V., Grigorev, V. Y., & Grigoreva, L. D. (2021). QSAR analysis of the acute toxicity of avermectins towards Tetrahymena pyriformis. SAR and QSAR in Environmental Research, 32(7), 541–571. https://doi.org/10.1080/1062936X.2021.1932583
Alsenan, S., Al-Turaiki, I., & Hafez, A. (2021). A deep learning approach to predict blood-brain barrier permeability. PeerJ Computer Science, 7, e515. https://doi.org/10.7717/peerj-cs.515
Gu, L., Zhu, T., & Chen, M. (2021). Modeling polyurethane foam (PUF)-air partition coefficients for persistent organic pollutants using linear and non-linear chemometric methods. Journal of Environmental Chemical Engineering, 9(4), 105615. https://doi.org/10.1016/j.jece.2021.105615
Feng, J.-J., Sun, X.-F., & Zeng, E. Y. (2021). Measurement of octanol–air partition coefficients for liquid crystals based on gas chromatography-retention time and its implication in predicting long-range transport potential. Chemosphere, 282(May), 131109. https://doi.org/10.1016/j.chemosphere.2021.131109
Galvez-Llompart, M., Ocello, R., Rullo, L., Stamatakos, S., Alessandrini, I., Zanni, R., Tuñón, I., Cavalli, A., Candeletti, S., Masetti, M., Romualdi, P., & Recanatini, M. (2021). Targeting the JAK/STAT Pathway: A Combined Ligand- and Target-Based Approach. Journal of Chemical Information and Modeling, 61(6), 3091–3108. https://doi.org/10.1021/acs.jcim.0c01468
Kleandrova, V. V., Scotti, L., Bezerra Mendonça Junior, F. J., Muratov, E., Scotti, M. T., & Speck-Planche, A. (2021). QSAR Modeling for Multi-Target Drug Discovery: Designing Simultaneous Inhibitors of Proteins in Diverse Pathogenic Parasites. Frontiers in Chemistry, 9(March), 1–20. https://doi.org/10.3389/fchem.2021.634663
Yoshimori, A., Kawasaki, E., Murakami, R., & Kanai, C. (2021). Discovery of Novel eEF2K Inhibitors Using HTS Fingerprint Generated from Predicted Profiling of Compound-Protein Interactions. Medicines, 8(5), 23. https://doi.org/10.3390/medicines8050023
Tao, L., Chen, G., & Li, Y. (2021). Machine learning discovery of high-temperature polymers. Patterns, 2(4), 100225. https://doi.org/10.1016/j.patter.2021.100225
Moussaoui, M., Laidi, M., Hanini, S., Abdallah, A. E. H., & Hentabli, M. (2021). Critical Properties and Acentric Factors of Pure Compounds Modelling Based on QSPR-SVM with Dragonfly Algorithm. Kemija u Industriji, 70(7–8), 375–386. https://doi.org/10.15255/KUI.2020.063
Huang, P., Liu, S., Xu, Y., Wang, Y., & Wang, Z. (2021). Combined lethal toxicities of pesticides with similar structures to Caenorhabditis elegans are not necessarily concentration additives. Environmental Pollution, 286(November 2020), 117207. https://doi.org/10.1016/j.envpol.2021.117207
Zanni, R., Galvez-Llompart, M., & Galvez, J. (2021). Computational analysis of macrolides as SARS-CoV-2 main protease inhibitors: a pattern recognition study based on molecular topology and validated by molecular docking. New Journal of Chemistry, 45(19), 8654–8675. https://doi.org/10.1039/D0NJ05983H
Bierling, A. L., Croy, I., Hummel, T., Cuniberti, G., & Croy, A. (2021). Olfactory Perception in Relation to the Physicochemical Odor Space. Brain Sciences, 11(5), 563. https://doi.org/10.3390/brainsci11050563
Kumar, R., Khan, F. U., Sharma, A., Siddiqui, M. H., Aziz, I. B., Kamal, M. A., Ashraf, G. M., Alghamdi, B. S., & Uddin, M. S. (2021). A deep neural network–based approach for prediction of mutagenicity of compounds. Environmental Science and Pollution Research. https://doi.org/10.1007/s11356-021-14028-9
Galvez, J., Zanni, R., Galvez-Llompart, M., & Benlloch, J. M. (2021). Macrolides May Prevent Severe Acute Respiratory Syndrome Coronavirus 2 Entry into Cells: A Quantitative Structure Activity Relationship Study and Experimental Validation. Journal of Chemical Information and Modeling, 61(4), 2016–2025. https://doi.org/10.1021/acs.jcim.0c01394
Liu, Y., Zhang, D., Tang, Y., Zhang, Y., Gong, X., Xie, S., & Zheng, J. (2021). Machine Learning-Enabled Repurposing and Design of Antifouling Polymer Brushes. Chemical Engineering Journal, 420(P1), 129872. https://doi.org/10.1016/j.cej.2021.129872
Szucs, R., Brown, R., Brunelli, C., Heaton, J. C., & Hradski, J. (2021). Structure Driven Prediction of Chromatographic Retention Times: Applications to Pharmaceutical Analysis. International Journal of Molecular Sciences, 22(8), 3848. https://doi.org/10.3390/ijms22083848
Schmidt, S., Schindler, M., Faber, D., & Hager, J. (2021). Fish early life stage toxicity prediction from acute daphnid toxicity and quantum chemistry. SAR and QSAR in Environmental Research, 1–24. https://doi.org/10.1080/1062936X.2021.1874514
Li, J., Wilkinson, J. L., & Boxall, A. B. A. (2021). Use of a large dataset to develop new models for estimating the sorption of active pharmaceutical ingredients in soils and sediments. Journal of Hazardous Materials, 415, 125688. https://doi.org/10.1016/j.jhazmat.2021.125688
Jiménez-Luna, J., Grisoni, F., Weskamp, N., & Schneider, G. (2021). Artificial intelligence in drug discovery: recent advances and future perspectives. Expert Opinion on Drug Discovery. https://doi.org/10.1080/17460441.2021.1909567
Halder, A. K., & Cordeiro, M. N. D. S. (2021). AKT Inhibitors: The Road Ahead to Computational Modeling-Guided Discovery. International Journal of Molecular Sciences, 22(8), 3944. https://doi.org/10.3390/ijms22083944
Nedyalkova, M., & Simeonov, V. (2021). Partitioning pattern of natural products based on molecular properties descriptors representing drug-likeness. Symmetry, 13(4). https://doi.org/10.3390/sym13040546
Mathew, S., Tess, D., Burchett, W., Chang, G., Woody, N., Keefer, C., Orozco, C., Lin, J., Jordan, S., Yamazaki, S., Jones, R., & Di, L. (2021). Evaluation of Prediction Accuracy for Volume of Distribution in Rat and Human Using In Vitro, In Vivo, PBPK and QSAR Methods. Journal of Pharmaceutical Sciences, 110(4), 1799–1823. https://doi.org/10.1016/j.xphs.2020.12.005
Zhu, T., Cao, Z., Prasad, R., Cheng, H., & Chen, M. (2021). In silico prediction of polyethylene-aqueous and air partition coefficients of organic contaminants using linear and nonlinear approaches. Journal of Environmental Management, 289, 112437. https://doi.org/10.1016/j.jenvman.2021.112437
Raimundo e Silva, J. P., Acevedo, C. A. H., de Souza, T. A., de Menezes, R. P. B., Sessions, Z. L., Abreu, L. S., Cibulski, S. P., Scotti, L., da Silva, M. S., Muratov, E. N., Scotti, M. T., & Tavares, J. F. (2021). Natural Products as Potential Agents Against SARS-CoV and SARS-CoV-2. Current Medicinal Chemistry, 28, 1–16. https://doi.org/10.2174/0929867328666210125113938
Seo, M., Chae, C. H., Lee, Y., Kim, H. R., & Kim, J. (2021). Novel QSAR Models for Molecular Initiating Event Modeling in Two Intersecting Adverse Outcome Pathways Based Pulmonary Fibrosis Prediction for Biocidal Mixtures. Toxics, 9(3), 59. https://doi.org/10.3390/toxics9030059
Ermondi, G., Garcia-jimenez, D., & Caron, G. (2021). PROTACs and Building Blocks : The 2D Chemical Space in Very Early Drug Discovery. 26(3), 672. https://doi.org/https://doi.org/10.3390/molecules26030672
Feng, C., Xu, Q., Qiu, X., Jin, Y., Ji, J., Lin, Y., Le, S., She, J., Lu, D., & Wang, G. (2021). Evaluation and application of machine learning-based retention time prediction for suspect screening of pesticides and pesticide transformation products in LC-HRMS. Chemosphere, 271, 129447. https://doi.org/10.1016/j.chemosphere.2020.129447
Kumar, A., Loharch, S., Kumar, S., Ringe, R. P., & Parkesh, R. (2021). Exploiting cheminformatic and machine learning to navigate the available chemical space of potential small molecule inhibitors of SARS-CoV-2. Computational and Structural Biotechnology Journal, 19, 424–438. https://doi.org/10.1016/j.csbj.2020.12.028
Aldosari, M. N., Yalamanchi, K. K., Gao, X., & Sarathy, S. M. (2021). Predicting entropy and heat capacity of hydrocarbons using machine learning. Energy and AI, 4, 100054. https://doi.org/10.1016/j.egyai.2021.100054
Sabbah, D. A., Al-Azaideh, B. A., Talib, W. H., Hajjo, R., Sweidan, K., Al-Zuheiri, A. M., Sheikha, G. A., & Shraim, S. (2021). New derivatives of sulfonylhydrazone as potential antitumor agents: Design, synthesis and cheminformatics evaluation. Acta Pharmaceutica, 71(4), 545–565. https://doi.org/10.2478/acph-2021-0043
Chen, S. T., Kowalewski, J., & Ray, A. (2021). Prolonged activation of carbon dioxide-sensitive neurons in mosquitoes. Interface Focus, 11(2), 20200043. https://doi.org/10.1098/rsfs.2020.0043
Liu, Y., Zhang, D., Tang, Y., Zhang, Y., Chang, Y., & Zheng, J. (2021). Machine Learning-Enabled Design and Prediction of Protein Resistance on Self-Assembled Monolayers and Beyond. ACS Applied Materials & Interfaces, 13(9), 11306–11319. https://doi.org/10.1021/acsami.1c00642
Sedykh, A. Y., Shah, R. R., Kleinstreuer, N. C., Auerbach, S. S., & Gombar, V. K. (2021). SAAGar−A new, extensible set of molecular substructures for QSAR/ QSPR and read-across predictions. Chemical Research in Toxicology. https://doi.org/10.1021/acs.chemrestox.0c00464
2020
Mauri, A. (2020). alvaDesc: A tool to calculate and analyze molecular descriptors and fingerprints. In K. Roy (Ed.), Ecotoxicological QSARs (pp. 801–820). Humana Press Inc. https://doi.org/10.1007/978-1-0716-0150-1_32
Falcón-Cano, G., Molina, C., & Cabrera-Pérez, M. Á. (2020). ADME Prediction with KNIME: Development and Validation of a Publicly Available Workflow for the Prediction of Human Oral Bioavailability. Journal of Chemical Information and Modeling, 60(6), 2660–2667. https://doi.org/10.1021/acs.jcim.0c00019
Di Pizio, A., Behr, J., & Krautwurst, D. (2020). Toward the Digitalization of Olfaction. In The Senses: A Comprehensive Reference (Vol. 3, pp. 758–768). Elsevier. https://doi.org/10.1016/B978-0-12-809324-5.24147-3
Alsenan, S. A., Al-Turaiki, I. M., & Hafez, A. M. (2020). Feature Extraction Methods in Quantitative Structure–Activity Relationship Modeling: A Comparative Study. IEEE Access, 8, 78737–78752. https://doi.org/10.1109/access.2020.2990375
Guo, Z., Huang, S., Wang, J., & Feng, Y. (2020). Recent advances in non-targeted screening analysis using liquid chromatography – high resolution mass spectrometry to explore new biomarkers for human exposure. Talanta, 219(July), 121339. https://doi.org/10.1016/j.talanta.2020.121339
Stošić, B., Janković, R., Stošić, M., Marković, D., Stanković, D., Sokolović, D., & Veselinović, A. M. (2020). In silico development of anesthetics based on barbiturate and thiobarbiturate inhibition of GABAA. Computational Biology and Chemistry, 88(March), 107318. https://doi.org/10.1016/j.compbiolchem.2020.107318
Falcón-Cano, G., Molina, C., & Cabrera-Pérez, M. A. (2020). ADME Prediction with KNIME: In silico aqueous solubility models based on supervised recursive machine learning approaches. ADMET and DMPK, 8(3), 251–273. https://doi.org/10.5599/admet.852
Zhu, T., Gu, Y., Cheng, H., & Chen, M. (2020). Versatile modelling of polyoxymethylene-water partition coefficients for hydrophobic organic contaminants using linear and nonlinear approaches. Science of the Total Environment, 728, 138881. https://doi.org/10.1016/j.scitotenv.2020.138881
Li, J., Carter, L. J., & Boxall, A. B. A. (2020). Evaluation and development of models for estimating the sorption behaviour of pharmaceuticals in soils. Journal of Hazardous Materials, 392(October 2019), 122469. https://doi.org/10.1016/j.jhazmat.2020.122469
Kowalewski, J., & Ray, A. (2020). Predicting novel drugs for SARS-CoV-2 using machine learning from a >10 million chemical space. Heliyon, 6(8), e04639. https://doi.org/10.1016/j.heliyon.2020.e04639
Sun, X., Zhang, X., Muir, D. C. G., & Zeng, E. Y. (2020). Identification of Potential PBT/POP-Like Chemicals by a Deep Learning Approach Based on 2D Structural Features. Environmental Science & Technology, 54(13), 8221–8231. https://doi.org/10.1021/acs.est.0c01437
Zhu, X., Ho, C. H., & Wang, X. (2020). Application of Life Cycle Assessment and Machine Learning for High-Throughput Screening of Green Chemical Substitutes. ACS Sustainable Chemistry and Engineering. https://doi.org/10.1021/acssuschemeng.0c02211
Kessler, T., St. John, P. C., Zhu, J., McEnally, C. S., Pfefferle, L. D., & Mack, J. H. (2020). A comparison of computational models for predicting yield sooting index. Proceedings of the Combustion Institute, 000, 1–9. https://doi.org/10.1016/j.proci.2020.07.009
George, A., & John, M. (2020). Impact of Oversampling on the Classification of Readily Biodegradable Materials. 2020 International Conference on Data Analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI), 1–5. https://doi.org/10.1109/ICDABI51230.2020.9325621
Zhu, T., Chen, W., Singh, R. P., & Cui, Y. (2020). Versatile in silico modeling of partition coefficients of organic compounds in polydimethylsiloxane using linear and nonlinear methods. Journal of Hazardous Materials, 399(February), 123012. https://doi.org/10.1016/j.jhazmat.2020.123012
Kleandrova, V. V., Scotti, M. T., Scotti, L., Nayarisseri, A., & Speck-Planche, A. (2020). Cell-based multi-target QSAR model for design of virtual versatile inhibitors of liver cancer cell lines. SAR and QSAR in Environmental Research, 00(00), 1–22. https://doi.org/10.1080/1062936X.2020.1818617
Rojas, C., Aranda, J. F., Pacheco Jaramillo, E., Losilla, I., Tripaldi, P., Duchowicz, P. R., & Castro, E. A. (2020). Foodinformatic prediction of the retention time of pesticide residues detected in fruits and vegetables using UHPLC/ESI Q-Orbitrap. Food Chemistry, (October), 128354. https://doi.org/10.1016/j.foodchem.2020.128354
Alsenan, S., Al-Turaiki, I., & Hafez, A. (2020). A Recurrent Neural Network model to predict blood–brain barrier permeability. Computational Biology and Chemistry, 89, 107377. https://doi.org/10.1016/j.compbiolchem.2020.107377
Tinkov, O., Polishchuk, P., Matveieva, M., Grigorev, V., Grigoreva, L., & Porozov, Y. (2020). The Influence of Structural Patterns on Acute Aquatic Toxicity of Organic Compounds. Molecular Informatics, 2000209, 1–14. https://doi.org/10.1002/minf.202000209
Zhu, T., Gu, L., Chen, M., & Sun, F. (2020). Exploring QSPR models for predicting PUF-air partition coefficients of organic compounds with linear and nonlinear approaches. Chemosphere, 266, 128962. https://doi.org/10.1016/j.chemosphere.2020.128962
Liu, A. L., Venkatesh, R., McBride, M., Reichmanis, E., Meredith, J. C., & Grover, M. A. (2020). Small Data Machine Learning: Classification and Prediction of Poly(ethylene terephthalate) Stabilizers Using Molecular Descriptors. ACS Applied Polymer Materials. https://doi.org/10.1021/acsapm.0c00921
Li, J., Sun, X., Xu, J., Tan, H., Zeng, E. Y., & Chen, D. (2020). Transplacental Transfer of Environmental Chemicals: Roles of Molecular Descriptors and Placental Transporters. Environmental Science and Technology. https://doi.org/10.1021/acs.est.0c06778
Sun, A., Ashammakhi, N., & Dokmeci, M. R. (2020). Methacrylate coatings for titanium surfaces to optimize biocompatibility. Micromachines, 11(1). https://doi.org/10.3390/mi11010087
Sabbah, D. A., Haroon, R. A., Bardaweel, S. K., Hajjo, R., & Sweidan, K. (2020). N-phenyl-6-chloro-4-hydroxy-2-quinolone-3-carboxamides: Molecular Docking, Synthesis, and Biological Investigation as Anticancer Agents. Molecules, 26(1), 73. https://doi.org/10.3390/molecules26010073
Wang, S., Kind, T., Tantillo, D. J., & Fiehn, O. (2020). Predicting in silico electron ionization mass spectra using quantum chemistry. Journal of Cheminformatics, 12(1), 1–11. https://doi.org/10.1186/s13321-020-00470-3
Yalamanchi, K. K., Monge-Palacios, M., van Oudenhoven, V. C. O., Gao, X., & Sarathy, S. M. (2020). Data Science Approach to Estimate Enthalpy of Formation of Cyclic Hydrocarbons. The Journal of Physical Chemistry A. https://doi.org/10.1021/acs.jpca.0c02785
Meshref, S., Li, Y., & Feng, Y. L. (2020). Prediction of liquid chromatographic retention time using quantitative structure-retention relationships to assist non-targeted identification of unknown metabolites of phthalates in human urine with high-resolution mass spectrometry. Journal of Chromatography A, 1634. https://doi.org/10.1016/j.chroma.2020.461691
Sharma, A., Kumar, R., Semwal, R., Aier, I., Tyagi, P., & Varadwaj, P. (2020). DeepOlf: Deep neural network based architecture for predicting odorants and their interacting Olfactory Receptors. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 1–1. https://doi.org/10.1109/tcbb.2020.3002154
Rybińska-Fryca, A., Sosnowska, A., & Puzyn, T. (2020). Representation of the structure-A key point of building QSAR/QSPR models for ionic liquids. Materials, 13(11), 1–11. https://doi.org/10.3390/ma13112500
Nedyalkova, M., & Simeonov, V. (2020). Multivariate chemometrics as a strategy to predict the allergenic nature of food proteins. Symmetry, 12(10), 1–19. https://doi.org/10.3390/sym12101616
Cui, X., Yang, R., Li, S., Liu, J., Wu, Q., & Li, X. (2020). Modeling and insights into molecular basis of low molecular weight respiratory sensitizers. Molecular Diversity, 0123456789. https://doi.org/10.1007/s11030-020-10069-3
Bonini, P., Kind, T., Tsugawa, H., Barupal, D. K., & Fiehn, O. (2020). Retip: Retention Time Prediction for Compound Annotation in Untargeted Metabolomics. Analytical Chemistry, 92(11), 7515–7522. https://doi.org/10.1021/acs.analchem.9b05765
Martinez-Mayorga, K., Madariaga-Mazon, A., Medina-Franco, J. L., & Maggiora, G. (2020). The impact of chemoinformatics on drug discovery in the pharmaceutical industry. Expert Opinion on Drug Discovery, 15(3), 293–306. https://doi.org/10.1080/17460441.2020.1696307
Alsenan, S. A., Al-Turaiki, I., & Hafez, A. (2020). Chemoinformatics for Data Scientists. Proceedings of the 22nd International Conference on Information Integration and Web-Based Applications & Services, 456–461. https://doi.org/10.1145/3428757.3429147
2019
Halder, A. K., & Cordeiro, M. N. D. S. (2019). Development of multi-target chemometric models for the inhibition of class I PI3K enzyme isoforms: A case study using QSAR-Co tool. International Journal of Molecular Sciences, 20(17). https://doi.org/10.3390/ijms20174191
Ghosh, D., Tetko, I., Klebl, B., Nussbaumer, P., & Koch, U. (2019). Analysis and Modelling of False Positives in GPCR Assays. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11731, pp. 764–770). Springer International Publishing. https://doi.org/10.1007/978-3-030-30493-5_71
Feiler, C., Mei, D., Vaghefinazari, B., Würger, T., Meißner, R. H., Luthringer-Feyerabend, B. J. C., Winkler, D. A., Zheludkevich, M. L., & Lamaka, S. V. (2019). In silico Screening of Modulators of Magnesium Dissolution. Corrosion Science, September, 108245. https://doi.org/10.1016/j.corsci.2019.108245
Tetko, I. V., Karpov, P., Bruno, E., Kimber, T. B., & Godin, G. (2019). Augmentation Is What You Need! Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11731, 831–835. https://doi.org/10.1007/978-3-030-30493-5_79