Here is a list of scientific publications citing Alvascience’s software solutions:


Galvez-Llompart, M., Zanni, R., Garcia-Domenech, R., & Galvez, J. (2022). How Molecular Topology Can Help in Amyotrophic Lateral Sclerosis (ALS) Drug Development: A Revolutionary Paradigm for a Merciless Disease. Pharmaceuticals, 15(1), 94.

Mukherjee, R. K., Kumar, V., & Roy, K. (2022). Ecotoxicological QSTR and QSTTR Modeling for the Prediction of Acute Oral Toxicity of Pesticides against Multiple Avian Species. Environmental Science & Technology, 56(1), 335–348.


Kanai, C., Kawasaki, E., Murakami, R., Morita, Y., & Yoshimori, A. (2021). Computational Prediction of Compound–Protein Interactions for Orphan Targets Using CGBVS. Molecules, 26(17), 5131.

Garcia Jimenez, D., Rossi Sebastiano, M., Caron, G., & Ermondi, G. (2021). Are we ready to design oral PROTACs®? ADMET and DMPK, 9(4), 243–254.

Nedyalkova, M., Vasighi, M., Sappati, S., Kumar, A., Madurga, S., & Simeonov, V. (2021). Inhibition Ability of Natural Compounds on Receptor-Binding Domain of SARS-CoV2: An In Silico Approach. Pharmaceuticals, 14(12), 1328.

Schiessler, E. J., Würger, T., Lamaka, S. V, Meißner, R. H., Cyron, C. J., Zheludkevich, M. L., Feiler, C., & Aydin, R. C. (2021). Predicting the inhibition efficiencies of magnesium dissolution modulators using sparse machine learning models. Npj Computational Materials, 7(1), 193.

Tiihonen, A., Cox-Vazquez, S. J., Liang, Q., Ragab, M., Ren, Z., Hartono, N. T. P., Liu, Z., Sun, S., Zhou, C., Incandela, N. C., Limwongyut, J., Moreland, A. S., Jayavelu, S., Bazan, G. C., & Buonassisi, T. (2021). Predicting Antimicrobial Activity of Conjugated Oligoelectrolyte Molecules via Machine Learning. Journal of the American Chemical Society, 143(45), 18917–18931.

Laidi, M., Abdallah, E., Si-Moussa, C., Benkortebi, O., Hentabli, M., & Hanini, S. (2021). CMC of diverse Gemini surfactants modelling using a hybrid approach combining SVR-DA. Chemical Industry and Chemical Engineering Quarterly, 27(3), 299–312.

Halder, A. K., & Cordeiro, M. N. D. S. (2021). Multi-Target In Silico Prediction of Inhibitors for Mitogen-Activated Protein Kinase-Interacting Kinases. Biomolecules, 11(11), 1670.

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.

Ghosh, D., Koch, U., Hadian, K., Sattler, M., & Tetko, I. V. (2021). Highly Accurate Filters to Flag Frequent Hitters in AlphaScreen Assays by Suggesting their Mechanism. Molecular Informatics, 2100151, 2100151.

Zhu, T., Chen, W., Gu, Y., Jafvert, C. T., & Fu, D. (2021). Polyethylene-water partition coefficients for polychlorinated biphenyls: Application of QSPR predictions models with experimental validation. Water Research, 207(June), 117799.

Alsenan, S. A. (2021). An Empirical Comparison of Machine and Deep Learning Algorithms’ Performance on Chemical Data. The 23rd International Conference on Information Integration and Web Intelligence, 655–658.

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.

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.

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.

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.

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.

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.

Laidi, M., Abdallah, el, Si-Moussa, C., Benkortebi, O., Hentabli, M., & Hanini, S. (2021). CMC of diverse Gemini surfactants modelling using a hybrid approach combining SVR-DA. Chemical Industry and Chemical Engineering Quarterly, 27(3), 299–312.

Alsenan, S., Al-Turaiki, I., & Hafez, A. (2021). A deep learning approach to predict blood-brain barrier permeability. PeerJ Computer Science, 7, e515.

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.

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.

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.

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.

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.

Tao, L., Chen, G., & Li, Y. (2021). Machine learning discovery of high-temperature polymers. Patterns, 2(4), 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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Nedyalkova, M., & Simeonov, V. (2021). Partitioning pattern of natural products based on molecular properties descriptors representing drug-likeness. Symmetry, 13(4).

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.

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.

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.

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.

Ermondi, G., Garcia-jimenez, D., & Caron, G. (2021). PROTACs and Building Blocks : The 2D Chemical Space in Very Early Drug Discovery. 26(3), 672.

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.

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.

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.

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.

Chen, S. T., Kowalewski, J., & Ray, A. (2021). Prolonged activation of carbon dioxide-sensitive neurons in mosquitoes. Interface Focus, 11(2), 20200043.

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.

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.


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.

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.

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.

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.

Guo, Z., Huang, S., Wang, J., & Feng, Y. L. (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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Sun, A., Ashammakhi, N., & Dokmeci, M. R. (2020). Methacrylate coatings for titanium surfaces to optimize biocompatibility. Micromachines, 11(1).

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.

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.

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.

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.

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.

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.

Nedyalkova, M., & Simeonov, V. (2020). Multivariate chemometrics as a strategy to predict the allergenic nature of food proteins. Symmetry, 12(10), 1–19.

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.

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.

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.

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.


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).

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.

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.

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.