Here is a list of scientific publications citing Alvascience’s software solutions:
2024
Mauri, A., & Bertola, M. (2024). AlvaBuilder: A Software for De Novo Molecular Design. Journal of Chemical Information and Modeling, 64(7), 2136–2142. https://doi.org/10.1021/acs.jcim.3c00610
Bueso-Bordils, J. I., Antón-Fos, G. M., Martín-Algarra, R., & Alemán-López, P. A. (2024). Overview of Computational Toxicology Methods Applied in Drug and Green Chemical Discovery. Journal of Xenobiotics, 14(4), 1901–1918. https://doi.org/10.3390/jox14040101
Noviandy, T. R., Idroes, G. M., Maulana, A., Afidh, R. P. F., & Idroes, R. (2024). Optimizing Hepatitis C Virus Inhibitor Identification with LightGBM and Tree-structured Parzen Estimator Sampling. Engineering, Technology & Applied Science Research, 14(6), 18810–18817. https://doi.org/10.48084/etasr.8947
Mariwan Ahmed, S., Tugcu, G., & Köksal, M. (2024). A Computational Approach: Predicting iNOS Inhibition of Compounds for Alzheimer’s Disease Treatment Through QSAR Modeling. ChemistrySelect, 9(44). https://doi.org/10.1002/slct.202400091
Pandey, S. K., & Roy, K. (2024). Development of hybrid models by the integration of the read-across hypothesis with the QSAR framework for the assessment of developmental and reproductive toxicity (DART) tested according to OECD TG 414. Toxicology Reports, 13(November), 101822. https://doi.org/10.1016/j.toxrep.2024.101822
Jia, H., & Sosso, G. C. (2024). Transparent Machine Learning Model to Understand Drug Permeability through the Blood–Brain Barrier. Journal of Chemical Information and Modeling. https://doi.org/10.1021/acs.jcim.4c01217
Guzman, J., Dulay, A., & Orosco, F. (2024). Selective protease inhibitors from secondary metabolites of Philippine medicinal plants against porcine epidemic diarrhea virus: A computational veterinary drug discovery approach. Open Veterinary Journal, 14(9), 2192. https://doi.org/10.5455/OVJ.2024.v14.i9.8
Abdellatif, H., Laidi, M., Si-moussa, C., Amrane, A., Euldji, I., & Benmouloud, W. (2024). Contributions to the development of prediction models for the toxicity of ionic liquids. Structural Chemistry, (0123456789). https://doi.org/10.1007/s11224-024-02411-4
Zhao, Y., Mulder, R. J., Eyckens, D. J., Houshyar, S., & Le, T. C. (2024). Advancing antimicrobial polymer development: a novel database and accelerated design via machine learning. Polymer Chemistry, 15(40), 4063–4076. https://doi.org/10.1039/D4PY00736K
Zhu, T., Li, S., Tao, C., Chen, W., Chen, M., Zong, Z., … Yan, B. (2025). Understanding the mechanism of microplastic-associated antibiotic resistance genes in aquatic ecosystems: Insights from metagenomic analyses and machine learning. Water Research, 268(September 2024), 122570. https://doi.org/10.1016/j.watres.2024.122570
Díaz-Peralta, L., Fernandez-Zertuche, M., Guevara-Salazar, J. A., Moran-Diaz, J. R., Hernandez-Dominguez, L. E., & Razo-Hernández, R. S. (2024). 1,5-Disubstituted-1,2,3-Triazoles as GABA analogues: Synthesis, QSAR and biological evaluation as Pseudomonas fluorescens GABA-AT inhibitors. Tetrahedron, 168(July), 134300. https://doi.org/10.1016/j.tet.2024.134300
Morishita, Y., Yarimizu, M., Kaneko, M., & Muraoka, A. (2024). Machine learning approach for predicting high JSC donor molecules in fullerene-typed organic solar cells. Chemical Physics Letters, 857(October), 141719. https://doi.org/10.1016/j.cplett.2024.141719
Rezaee, P., Rezaee, S., Maaza, M., & Arab, S. S. (2024). Screening of BindingDB database ligands against EGFR, HER2, Estrogen, Progesterone and NF-B receptors based on machine learning and molecular docking. Computers in Biology and Medicine, 183(June), 109279. https://doi.org/10.1016/j.compbiomed.2024.109279
Martínez-Campos, Z., Palacios-Can, F. J., López-Cortina, S. T., Razo-Hernández, R. S., & Fernández-Zertuche, M. (2024). Design and synthesis of 3,5-disubstituted isoxazoles by Cu-mediated 1,3-dipolar cycloaddition and their in silico evaluation as potential GABAB receptor modulators. Tetrahedron, 168(October), 134336. https://doi.org/10.1016/j.tet.2024.134336
Bhattacharyya, P., Samanta, P., Kumar, A., Das, S., & Ojha, P. K. (2024). Quantitative read-across structure–property relationship (q-RASPR): a novel approach to estimate the bioaccumulative potential for diverse classes of industrial chemicals in aquatic organisms. Environmental Science: Processes & Impacts. https://doi.org/10.1039/D4EM00374H
Ancuceanu, R., Popovici, P. C., Drăgănescu, D., Busnatu, Ștefan, Lascu, B. E., & Dinu, M. (2024). QSAR Regression Models for Predicting HMG-CoA Reductase Inhibition. Pharmaceuticals, 17(11), 1448. https://doi.org/10.3390/ph17111448
Jafari, P., & Song, P. (2024). Predicting The Tensile And Flexural Strength Of Fire-Retardant Epoxy Materials Using A Data-Driven Approach. Nanotechnology Perceptions, 20(S13), 655–665. https://doi.org/10.62441/nano-ntp.v20iS13.40
Rozano, L., Abdul Jalal, M. I., Md Shahri, N. A. A., Mohamed-Hussein, Z.-A., Ab Mutalib, N. S., & Abdullah-Zawawi, M.-R. (2024). Molecular Fingerprints and Pharmacophores for Computational Drug Repurposing. In Reference Module in Life Sciences (pp. 1–11). Elsevier. https://doi.org/10.1016/B978-0-323-95502-7.00165-2
Souza, T. A. De, Pereira, L. H. A., Alves, A. F., Dourado, D., Lins, J. da S., Scotti, M. T., … Silva, M. S. (2024). Jatropha Diterpenes: An Updated Review Concerning Their Structural Diversity, Therapeutic Performance, and Future Pharmaceutical Applications. Pharmaceuticals, 17(10), 1399. https://doi.org/10.3390/ph17101399
Kleandrova, V. V, Cordeiro, M. N. D. S., & Speck-Planche, A. (2024). Perturbation Theory Machine Learning Model for Phenotypic Early Antineoplastic Drug Discovery: Design of Virtual Anti-Lung-Cancer Agents. Applied Sciences, 14(20), 9344. https://doi.org/10.3390/app14209344
Chebotaev, P. P., Buglak, A. A., Sheehan, A., & Filatov, M. A. (2024). Predicting fluorescence to singlet oxygen generation quantum yield ratio for BODIPY dyes using QSPR and machine learning. Physical Chemistry Chemical Physics, 26(38), 25131–25142. https://doi.org/10.1039/D4CP02471K
Nisterenko, W., Kułaga, D., Woziński, M., Singh, Y. R., Judzińska, B., Jagiello, K., … Ciura, K. (2024). Evaluation of Physicochemical Properties of Ipsapirone Derivatives Based on Chromatographic and Chemometric Approaches. Molecules, 29(8), 1862. https://doi.org/10.3390/molecules29081862
Samanta, P., Bhattacharyya, P., Samal, A., Kumar, A., Bhattacharjee, A., & Ojha, P. K. (2024). Ecotoxicological risk assessment of active pharmaceutical ingredients (APIs) against different aquatic species leveraging intelligent consensus prediction and i-QSTTR modeling. Journal of Hazardous Materials, 480(June), 136110. https://doi.org/10.1016/j.jhazmat.2024.136110
Mahini, R. A., Casanola-Martin, G., Ludwig, S. A., & Rasulev, B. (2024). MixtureMetrics: A comprehensive package to develop additive numerical features to describe complex materials for machine learning modeling. SoftwareX, 28(May), 101911. https://doi.org/10.1016/j.softx.2024.101911
Kowalska, D., Sosnowska, A., Zdybel, S., Stepnik, M., & Puzyn, T. (2024). Predicting bioconcentration factors (BCFs) for per- and polyfluoroalkyl substances (PFAS). Chemosphere, 364(August), 143146. https://doi.org/10.1016/j.chemosphere.2024.143146
Alharthi, A. M., Al-Thanoon, N. A., Al-Fakih, A. M., & Algamal, Z. Y. (2024). QSAR modelling of enzyme inhibition toxicity of ionic liquid based on chaotic spotted hyena optimization algorithm. SAR and QSAR in Environmental Research, 35(9), 757–770. https://doi.org/10.1080/1062936X.2024.2404853
Pore, S., Pelloux, A., Chatterjee, M., Banerjee, A., & Roy, K. (2024). Machine learning-based q-RASAR predictions of the bioconcentration factor of organic molecules estimated following the organisation for economic co-operation and development guideline 305. Journal of Hazardous Materials, 479(August), 135725. https://doi.org/10.1016/j.jhazmat.2024.135725
Luo, X., Ding, Y., Cao, Y., Liu, Z., Zhang, W., Zeng, S., … Shi, P. (2024). Few-shot meta-learning applied to whole brain activity maps improves systems neuropharmacology and drug discovery. IScience, 27(10), 110875. https://doi.org/10.1016/j.isci.2024.110875
Li, D., Yang, F., Wang, X., Zhang, H., Pan, Y., Wang, N., & Chen, S. (2024). Innovative molecular descriptors in QSPR modeling: Integrating Carnahan-Starling EoS for predicting diffusion coefficients in hydrocarbons and mixtures. Journal of Molecular Liquids, 413(August), 125994. https://doi.org/10.1016/j.molliq.2024.125994
Kumar, A., Ojha, P. K., & Roy, K. (2024). First report on regression-based QSAR addressing pesticide dissipation half-life in plants: A step towards sustainable public health. Science of The Total Environment, 954(August), 176175. https://doi.org/10.1016/j.scitotenv.2024.176175
Pandey, S. K., & Roy, K. (2024). Predictive cheminformatics modeling of reorganization energy (RE) for p-type organic semiconductors: Integration of quantitative read-across structure-property relationship (q-RASPR) and stacking regression analysis. Materials Today Communications, 41(July), 110430. https://doi.org/10.1016/j.mtcomm.2024.110430
López-Pérez, K., Avellaneda-Tamayo, J. F., Chen, L., López-López, E., Juárez-Mercado, K. E., Medina-Franco, J. L., & Miranda-Quintana, R. A. (2024). Molecular similarity: Theory, applications, and perspectives. Artificial Intelligence Chemistry, 2(2), 100077. https://doi.org/10.1016/j.aichem.2024.100077
Banerjee, A., & Roy, K. (2024). The application of chemical similarity measures in an unconventional modeling framework c-RASAR along with dimensionality reduction techniques to a representative hepatotoxicity dataset. Scientific Reports, 14(1), 20812. https://doi.org/10.1038/s41598-024-71892-4
Paraschiv, C., Gosav, S., Burlacu, C. M., & Praisler, M. (2024). Exploring the Inhibitory Efficacy of Resokaempferol and Tectochrysin on PI3Kα Protein by Combining DFT and Molecular Docking against Wild-Type and H1047R Mutant Forms. Inventions, 9(5), 96. https://doi.org/10.3390/inventions9050096
Caron, G., Garcia Jimenez, D., Vallaro, M., Vitagliano, L., Lopez Lopez, L., Apprato, G., & Ermondi, G. (2024). Molecular properties, including chameleonicity, as essential tools for designing the next generation of oral beyond rule of five drugs. ADMET and DMPK, 00(0), 1–16. https://doi.org/10.5599/admet.2334
Singh, S., Kaur, N., & Gehlot, A. (2024). Application of artificial intelligence in drug design: A review. Computers in Biology and Medicine, 179(June), 108810. https://doi.org/10.1016/j.compbiomed.2024.108810
Kazemi-Khasragh, E., González, C., & Haranczyk, M. (2024). Toward diverse polymer property prediction using transfer learning. Computational Materials Science, 244(40), 113206. https://doi.org/10.1016/j.commatsci.2024.113206
Kaito, S., Takeshita, J., Iwata, M., Sasaki, T., Hosaka, T., Shizu, R., & Yoshinari, K. (2024). Utility of human cytochrome P450 inhibition data in the assessment of drug-induced liver injury. Xenobiotica, 54(7), 1–30. https://doi.org/10.1080/00498254.2024.2312505
Dulay, A. N. G., de Guzman, J. C. C., Marquez, Z. Y. D., Santana, E. S. D., Arce, J., & Orosco, F. L. (2024). The potential of Chlorella spp. as antiviral source against African swine fever virus through a virtual screening pipeline. Journal of Molecular Graphics and Modelling, 132(June), 108846. https://doi.org/10.1016/j.jmgm.2024.108846
Mishra, P., Nandi, S., Chatterjee, A., Nayek, T., Basak, S., Halder, K., & Mukherjee, A. (2024). Development of 2D and 3D QSAR models of pyrazole derivatives as acetylcholine esterase inhibitors. Journal of the Serbian Chemical Society, 89(7–8), 981–995. https://doi.org/10.2298/JSC230221039M
Ramajo, G., García, C., Gil, A., & Otero, A. (2024). Training Deep Learning Neural Networks for Predicting CCS Using the METLIN-CCS Dataset. In IFMBE Proceedings (Vol. 41, pp. 225–236). https://doi.org/10.1007/978-3-031-64636-2_17
Pore, S., & Roy, K. (2024). Insights into pharmacokinetic properties for exposure chemicals: predictive modelling of human plasma fraction unbound ( f u ) and hepatocyte intrinsic clearance (Cl int ) data using machine learning. Digital Discovery. https://doi.org/10.1039/D4DD00082J
Zheng, J.-J., Li, Q.-Z., Wang, Z., Wang, X., Zhao, Y., & Gao, X. (2024). Computer-aided nanodrug discovery: recent progress and future prospects. Chemical Society Reviews, 47, 1098–1131. https://doi.org/10.1039/D3CS00575E
Mitra, S., Halder, A. K., Koley, A., Ghosh, N., Panda, P., Mandal, S. C., & Cordeiro, M. N. D. S. (2024). Unveiling structural determinants for FXR antagonism in 1,3,4-trisubstituted-Pyrazol amide derivatives: A multi-scale in silico modelling approach. Computers in Biology and Medicine, 180(April), 108991. https://doi.org/10.1016/j.compbiomed.2024.108991
Bardaweel, S. K., AlOmari, R., & Hajjo, R. (2024). Integrating computational and experimental chemical biology revealed variable anticancer activities of phosphodiesterase isoenzyme 5 inhibitors (PDE5i) in lung cancer. RSC Medicinal Chemistry. https://doi.org/10.1039/D4MD00364K
Nath, A., Ojha, P. K., & Roy, K. (2024). Modelling lethality and teratogenicity of zebrafish ( Danio rerio ) due to β-lactam antibiotics employing the QSTR approach. SAR and QSAR in Environmental Research, 1–25. https://doi.org/10.1080/1062936X.2024.2378797
Hossain, M. M., Banerjee, A., Chatterjee, M., Roy, K., & Cronin, M. T. D. (2024). QSPR and q-RASPR predictions of the adsorption capacity of polyethylene, polypropylene and polystyrene microplastics for various organic pollutants in diverse aqueous environments. Environmental Science: Nano, 3. https://doi.org/10.1039/D4EN00311J
Prado-Romero, D. L., Saldívar-González, F. I., López-Mata, I., Laurel-García, P. A., Durán-Vargas, A., García-Hernández, E., … Medina-Franco, J. L. (2024). De Novo Design of Inhibitors of DNA Methyltransferase 1: A Critical Comparison of Ligand- and Structure-Based Approaches. Biomolecules, 14(7), 775. https://doi.org/10.3390/biom14070775
Kapustina, O., Burmakina, P., Gubina, N., Serov, N., & Vinogradov, V. (2024). User-friendly and industry-integrated AI for medicinal chemists and pharmaceuticals. Artificial Intelligence Chemistry, 2(2), 100072. https://doi.org/10.1016/j.aichem.2024.100072
Su, W., Li, P., Zhong, L., Liang, W., Li, T., Liu, J., … Jiang, G. (2024). Occurrence and Distribution of Antibacterial Quaternary Ammonium Compounds in Chinese Estuaries Revealed by Machine Learning-Assisted Mass Spectrometric Analysis. Environmental Science & Technology, 58(26), 11707–11717. https://doi.org/10.1021/acs.est.4c02380
Kumar, A., Ojha, P. K., & Roy, K. (2024). Safer and greener chemicals for the aquatic ecosystem: Chemometric modeling of the prolonged and chronic aquatic toxicity of chemicals on Oryzias latipes. Aquatic Toxicology, 273(June), 106985. https://doi.org/10.1016/j.aquatox.2024.106985
Laganà Vinci, R., Arena, K., Rigano, F., Cacciola, F., Dugo, P., & Mondello, L. (2024). Prediction of retention data of phenolic compounds by quantitative structure retention relationship models under reverse-phase liquid chromatography. Journal of Chromatography A, 1730(April), 465146. https://doi.org/10.1016/j.chroma.2024.465146
Tinkov, O. V., Osipov, V. N., Kolotaev, A. V., Khachatryan, D. S., & Grigorev, V. Y. (2024). HT_PREDICT: a machine learning-based computational open-source tool for screening HDAC6 inhibitors. SAR and QSAR in Environmental Research, 35(6), 505–530. https://doi.org/10.1080/1062936X.2024.2371155
Bera, D., Kumar, A., Roy, J., & Roy, K. (2024). Intelligent Consensus Predictions of the Retention Index of Flavor and Fragrance Compounds Using 2D Descriptors. Chromatographia, (0123456789). https://doi.org/10.1007/s10337-024-04349-5
Shah, S. K., Chaple, D. D., Masand, V. H., Jawarkar, R. D., Chaudhari, S., Abiramasundari, A., … Al-Hussain, S. A. (2024). Multi-Target In-Silico modeling strategies to discover novel angiotensin converting enzyme and neprilysin dual inhibitors. Scientific Reports, 14(1), 15991. https://doi.org/10.1038/s41598-024-66230-7
de Cripan, S. M., Arora, T., Olomí, A., Canela, N., Siuzdak, G., & Domingo-Almenara, X. (2024). Predicting the Predicted: A Comparison of Machine Learning-Based Collision Cross-Section Prediction Models for Small Molecules. Analytical Chemistry, 96(22), 9088–9096. https://doi.org/10.1021/acs.analchem.4c00630
de Sousa, N. F., Duarte, G. D., Moraes, C. B., Barbosa, C. G., Martin, H., Muratov, N. N., … Scotti, M. T. (2024). In Silico and In Vitro Studies of Terpenes from the Fabaceae Family Using the Phenotypic Screening Model against the SARS-CoV-2 Virus. Pharmaceutics, 16(7), 912. https://doi.org/10.3390/pharmaceutics16070912
Bandini, E., Castellano Ontiveros, R., Kajtazi, A., Eghbali, H., & Lynen, F. (2024). Physicochemical modelling of the retention mechanism of temperature-responsive polymeric columns for HPLC through machine learning algorithms. Journal of Cheminformatics, 16(1), 72. https://doi.org/10.1186/s13321-024-00873-6
Gao, H., Li, S., Lan, Z., Pan, D., Naidu, G. S., Peer, D., … Santos, H. A. (2024). Comparative optimization of polysaccharide-based nanoformulations for cardiac RNAi therapy. Nature Communications, 15(1), 5398. https://doi.org/10.1038/s41467-024-49804-x
Khan, A. U., Porta, G. M., Riva, M., & Guadagnini, A. (2024). In-silico mechanistic analysis of adsorption of Iodinated Contrast Media agents on graphene surface. Ecotoxicology and Environmental Safety, 280(January), 116506. https://doi.org/10.1016/j.ecoenv.2024.116506
Ullah, A., Shaheryar, M., & Lim, H. (2024). Machine Learning Approach for the Estimation of Henry’s Law Constant Based on Molecular Descriptors. Atmosphere, 15(6), 706. https://doi.org/10.3390/atmos15060706
Ait Lahcen, N., Liman, W., Oubahmane, M., Hdoufane, I., Habibi, Y., Alanazi, A. S., … Cherqaoui, D. (2024). Drug design of new anti-EBOV inhibitors: QSAR, homology modeling, molecular docking and molecular dynamics studies. Arabian Journal of Chemistry, 17(9), 105870. https://doi.org/10.1016/j.arabjc.2024.105870
Daghighi, A., Casanola-Martin, G. M., Iduoku, K., Kusic, H., González-Díaz, H., & Rasulev, B. (2024). Multi-Endpoint Acute Toxicity Assessment of Organic Compounds Using Large-Scale Machine Learning Modeling. Environmental Science & Technology, 58(23), 10116–10127. https://doi.org/10.1021/acs.est.4c01017
Acuña-Guzman, V., Montoya-Alfaro, M. E., Negrón-Ballarte, L. P., & Solis-Calero, C. (2024). A Machine Learning Approach for Predicting Caco-2 Cell Permeability in Natural Products from the Biodiversity in Peru. Pharmaceuticals, 17(6), 750. https://doi.org/10.3390/ph17060750
Pandey, S. K., & Roy, K. (2024). Predicting the performance and stability parameters of energetic materials (EMs) using a machine learning-based q-RASPR approach. Energy Advances. https://doi.org/10.1039/D4YA00215F
Balraadjsing, S., J.G.M. Peijnenburg, W., & Vijver, M. G. (2024). Building species trait-specific nano-QSARs: Model stacking, navigating model uncertainties and limitations, and the effect of dataset size. Environment International, 188(May), 108764. https://doi.org/10.1016/j.envint.2024.108764
Das, S., Samal, A., Kumar, A., Ghosh, V., Kar, S., & Ojha, P. K. (2024). Comprehensive ecotoxicological assessment of pesticides on multiple avian species: Employing quantitative structure-toxicity relationship (QSTR) modeling and read-across. Process Safety and Environmental Protection, 188(May), 39–52. https://doi.org/10.1016/j.psep.2024.05.095
Erickson, M., Casañola-Martin, G., Han, Y., Rasulev, B., & Kilin, D. (2024). Relationships between the Photodegradation Reaction Rate and Structural Properties of Polymer Systems. The Journal of Physical Chemistry B, 128(9), 2190–2200. https://doi.org/10.1021/acs.jpcb.3c06854
Obradović, D., Stavrianidi, A., Fedorova, E., Bogojević, A., Shpigun, O., Buryak, A., & Lazović, S. (2024). A comparative study of the predictive performance of different descriptor calculation tools: Molecular-based elution order modeling and interpretation of retention mechanism for isomeric compounds from METLIN database. Journal of Chromatography A, 1719(February), 464731. https://doi.org/10.1016/j.chroma.2024.464731
Niu, H., Zhang, Y., Jia, Q., Wang, Q., & Yan, F. (2024). Property estimation of organic compounds based on QSPR models with norm indices. Chemical Engineering Science, 288(February), 119835. https://doi.org/10.1016/j.ces.2024.119835
Nakatani, K., Izumi, Y., Umakoshi, H., Yokomoto-Umakoshi, M., Nakaji, T., Kaneko, H., … Bamba, T. (2024). Wide-scope targeted analysis of bioactive lipids in human plasma by LC/MS/MS. Journal of Lipid Research, 65(1), 100492. https://doi.org/10.1016/j.jlr.2023.100492
Gutkin, E., Gusev, F., Gentile, F., Ban, F., Koby, S. B., Narangoda, C., … Kurnikova, M. G. (2024). In silico screening of LRRK2 WDR domain inhibitors using deep docking and free energy simulations. Chemical Science. https://doi.org/10.1039/D3SC06880C
Bhattacharjee, A., Kar, S., & Ojha, P. K. (2024). Unveiling G-protein coupled receptor kinase-5 inhibitors for chronic degenerative diseases: Multilayered prioritization employing explainable machine learning-driven multi-class QSAR, ligand-based pharmacophore and free energy-inspired molecular simulatio. International Journal of Biological Macromolecules, 269(P1), 131784. https://doi.org/10.1016/j.ijbiomac.2024.131784
Vigna, V., Cova, T. F. G. G., Nunes, S. C. C., Pais, A. A. C. C., & Sicilia, E. (2024). Machine Learning-Based Prediction of Reduction Potentials for Pt IV Complexes. Journal of Chemical Information and Modeling, 64(9), 3733–3743. https://doi.org/10.1021/acs.jcim.4c00315
Tran, T. T. Van, Tayara, H., & Chong, K. T. (2024). AMPred-CNN: Ames mutagenicity prediction model based on convolutional neural networks. Computers in Biology and Medicine, 176(May), 108560. https://doi.org/10.1016/j.compbiomed.2024.108560
Ghosh, S., & Roy, K. (2024). Quantitative read-across structure-activity relationship (q-RASAR): A novel approach to estimate the subchronic oral safety (NOAEL) of diverse organic chemicals in rats. Toxicology, 505(May), 153824. https://doi.org/10.1016/j.tox.2024.153824
Kumar, V., Banerjee, A., & Roy, K. (2024). Innovative strategies for the quantitative modeling of blood–brain barrier (BBB) permeability: harnessing the power of machine learning-based q-RASAR approach. Molecular Systems Design & Engineering, (Ml). https://doi.org/10.1039/D4ME00056K
Dhanalakshmi, M., Sruthi, D., Das, K., Iqbal, M., Mohanan, V. P., Dave, S., & Muthulakshmi Andal, N. (2024). Graph theoretical descriptors differentiate d-Mannose isomers in the principal component proposed feature space: A computational approach. Carbohydrate Research, 541(May), 109147. https://doi.org/10.1016/j.carres.2024.109147
Pang, W., Chen, M., & Qin, Y. (2024). Prediction of anticancer drug sensitivity using an interpretable model guided by deep learning. BMC Bioinformatics, 25(1), 182. https://doi.org/10.1186/s12859-024-05669-x
Kumar, V., Banerjee, A., & Roy, K. (2024). Breaking the Barriers: Machine-Learning-Based c-RASAR Approach for Accurate Blood–Brain Barrier Permeability Prediction. Journal of Chemical Information and Modeling. https://doi.org/10.1021/acs.jcim.4c00433
Galvez-Llompart, M., Hierrezuelo, J., Blasco, M., Zanni, R., Galvez, J., de Vicente, A., Pérez-García, A., & Romero, D. (2024). Targeting bacterial growth in biofilm conditions: rational design of novel inhibitors to mitigate clinical and food contamination using QSAR. Journal of Enzyme Inhibition and Medicinal Chemistry, 39(1), 1–18. https://doi.org/10.1080/14756366.2024.2330907
Pore, S., Banerjee, A., & Roy, K. (2024). Application of machine learning‐based read‐across structure‐property relationship (RASPR) as a new tool for predictive modelling: Prediction of power conversion efficiency (PCE) for selected classes of organic dyes in dye‐sensitized solar cells (DSSCs). Molecular Informatics, 43(4), 1–21. https://doi.org/10.1002/minf.202300210
Das, S., Samal, A., & Ojha, P. K. (2024). Chemometrics-driven prediction and prioritization of diverse pesticides on chickens for addressing hazardous effects on public health. Journal of Hazardous Materials, 471(April), 134326. https://doi.org/10.1016/j.jhazmat.2024.134326
Godlewska, K., Białk-Bielińska, A., Mazierski, P., Zdybel, S., Sosnowska, A., Górzyński, D., Puzyn, T., Zaleska-Medynska, A., Klimczuk, T., & Paszkiewicz, M. (2024). Assessment of the application of selected metal-organic frameworks as advanced sorbents in passive extraction used in the monitoring of contaminants of emerging concern in surface waters. Science of The Total Environment, 927(March), 172215. https://doi.org/10.1016/j.scitotenv.2024.172215
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Ghosh, S., Chatterjee, M., & Roy, K. (2023). Predictive Quantitative Read-Across Structure–Property Relationship Modeling of the Retention Time (Log t R ) of Pesticide Residues Present in Foods and Vegetables. Journal of Agricultural and Food Chemistry, 71(24), 9538–9548. https://doi.org/10.1021/acs.jafc.3c01438
Kumar, A., Kumar, V., Podder, T., & Ojha, P. K. (2023). First report on ecotoxicological QSTR and i-QSTR modeling for the prediction of acute ecotoxicity of diverse organic chemicals against three protozoan species. Chemosphere, 335(May), 139066. https://doi.org/10.1016/j.chemosphere.2023.139066
Mohammadi, N., Abedanzadeh, S., Fereidonnejad, R., Mahdavinia, M., & Fereidoonnezhad, M. (2023). Effects of diphosphine ligands on the anticancer behavior of cycloplatinated(II) complexes of 2,2´-bipyridine N oxide: In vitro cytotoxicity, apoptosis, genotoxicity, and molecular docking studies. Journal of Organometallic Chemistry, 996, 122759. https://doi.org/10.1016/j.jorganchem.2023.122759
Chatterjee, M., & Roy, K. (2023). “Data fusion” quantitative read-across structure-activity-activity relationships (q-RASAARs) for the prediction of toxicities of binary and ternary antibiotic mixtures toward three bacterial species. Journal of Hazardous Materials, 459(May), 132129. https://doi.org/10.1016/j.jhazmat.2023.132129
Vatiwutipong, P., Vachmanus, S., Noraset, T., & Tuarob, S. (2023). Artificial Intelligence in Cosmetic Dermatology: A Systematic Literature Review. IEEE Access, 11(2), 71407–71425. https://doi.org/10.1109/ACCESS.2023.3295001
Zhang, R.; Wang, B.; Li, L.; Li, S.; Guo, H.; Zhang, P.; Hua, Y.; Cui, X.; Li, Y.; Mu, Y.; Huang, X.; Li, X. Modeling and Insights into the Structural Characteristics of Endocrine-Disrupting Chemicals. Ecotoxicol. Environ. Saf. 2023, 263 (January), 115251. https://doi.org/10.1016/j.ecoenv.2023.115251
Zhao, Y.; Mulder, R. J.; Houshyar, S.; Le, T. C. A Review on the Application of Molecular Descriptors and Machine Learning in Polymer Design. Polym. Chem. 2023. https://doi.org/10.1039/D3PY00395G
Gallagher, A.; Kar, S.; Sepúlveda, M. S. Computational Modeling of Human Serum Albumin Binding of Per- and Polyfluoroalkyl Substances Employing QSAR, Read-Across, and Docking. Molecules 2023, 28 (14), 5375. https://doi.org/10.3390/molecules28145375
dos Santos, B. R., Ramos, A. B. da S. B., de Menezes, R. P. B., Scotti, M. T., Colombo, F. A., Marques, M. J., & Reimão, J. Q. (2023). Repurposing the Medicines for Malaria Venture’s COVID Box to discover potent inhibitors of Toxoplasma gondii, and in vivo efficacy evaluation of almitrine bismesylate (MMV1804175) in chronically infected mice. PLOS ONE, 18(7), e0288335. https://doi.org/10.1371/journal.pone.0288335
Kajtazi, A., Russo, G., Wicht, K., Eghbali, H., & Lynen, F. (2023). Facilitating structural elucidation of small environmental solutes in RPLC-HRMS by retention index prediction. Chemosphere, 337(March), 139361. https://doi.org/10.1016/j.chemosphere.2023.139361
Xu, Y., Hu, Y., Ding, T., Wang, Z., Zhou, C., Zhu, Q., … Jiang, G. (2023). Novel macromolecular synthetic phenolic antioxidants in sludge on a national scale in China: Their distribution, potential transformation products, and ecological risk. Science of The Total Environment, 894(April), 164928. https://doi.org/10.1016/j.scitotenv.2023.164928
Zheng, H., Lv, W., Wang, Y., Feng, Y., & Yang, H. (2023). Molecular kinematic viscosity prediction of natural ester insulating oil based on sparse Machine learning models. Journal of Molecular Liquids, 385(March), 122355. https://doi.org/10.1016/j.molliq.2023.122355
Huang, P., Liu, S., Wang, Z., Ding, T., Tao, M., & Gu, Z. (2023). Study on the characterization of pesticide modes of action similarity and the multi-endpoint combined toxicity of pesticide mixtures to Caenorhabditis elegans. Science of The Total Environment, 893(February), 164918. https://doi.org/10.1016/j.scitotenv.2023.164918
Kumar, S., Jayan, J., Manoharan, A., Benny, F., Abdelgawad, M. A., Ghoneim, M. M., … Mathew, B. (2023). Discerning of isatin-based monoamine oxidase (MAO) inhibitors for neurodegenerative disorders by exploiting 2D, 3D-QSAR modelling and molecular dynamics simulation. Journal of Biomolecular Structure and Dynamics, 1–13. https://doi.org/10.1080/07391102.2023.2214216
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Chang, J., Zou, J., Lou, C., Ye, J., Feng, R., Li, Z., & Hu, G. (2023). Gas‐to‐ionic liquid partition: QSPR modeling and mechanistic interpretation. Molecular Informatics, (September 2022), 1–16. https://doi.org/10.1002/minf.202200223
Banerjee, A., & Roy, K. (2023). Machine-learning-based similarity meets traditional QSAR: “q-RASAR” for the enhancement of the external predictivity and detection of prediction confidence outliers in an hERG toxicity dataset. Chemometrics and Intelligent Laboratory Systems, 237(February), 104829. https://doi.org/10.1016/j.chemolab.2023.104829
Tripathi, M. K., Bhardwaj, B., Waiker, D. K., Tripathi, A., & Shrivastava, S. K. (2023). Discovery of novel dual acetylcholinesterase and butyrylcholinesterase inhibitors using machine learning and structure-based drug design. Journal of Molecular Structure, 1286(March), 135517. https://doi.org/10.1016/j.molstruc.2023.135517
SubLaban, A., Kessler, T. J., Van Dam, N., & Mack, J. H. (2023). Artificial Neural Network Models for Octane Number and Octane Sensitivity: A Quantitative Structure Property Relationship Approach to Fuel Design. Journal of Energy Resources Technology, 145(10). https://doi.org/10.1115/1.4062189
De Gauquier, P., Peeters, J., Vanommeslaeghe, K., Vander Heyden, Y., & Mangelings, D. (2023). Modelling the enantiorecognition of structurally diverse pharmaceuticals on O-substituted polysaccharide-based stationary phases. Talanta, 259(March), 124497. https://doi.org/10.1016/j.talanta.2023.124497
Bennett, S., & Jelfs, K. E. (2023). Porous Molecular Materials. In AI‐Guided Design and Property Prediction for Zeolites and Nanoporous Materials (pp. 251–282). Wiley. https://doi.org/10.1002/9781119819783.ch10
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Paul, R., Roy, J., & Roy, K. (2023). Prediction of soil ecotoxicity against Folsomia candida using acute and chronic endpoints. SAR and QSAR in Environmental Research, 34(4), 321–340. https://doi.org/10.1080/1062936X.2023.2211350
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Gálvez‐Llompart, M., & Sastre, G. (2023). Machine Learning Search for Suitable Structure Directing Agents for the Synthesis of Beta (BEA) Zeolite Using Molecular Topology and Monte Carlo Techniques. In AI‐Guided Design and Property Prediction for Zeolites and Nanoporous Materials (pp. 61–80). Wiley. https://doi.org/10.1002/9781119819783.ch3
Nath, A., Ojha, P. K., & Roy, K. (2023). Computational modeling of aquatic toxicity of polychlorinated naphthalenes (PCNs) employing 2D-QSAR and chemical read-across. Aquatic Toxicology, 257(September 2022), 106429. https://doi.org/10.1016/j.aquatox.2023.106429
Desai, S. A., Deepak, M. S., Khandare, R. S., Sahu, A. R., Patel, V. K., Patel, A. S., … Patel, J. (2023). A QSAR STUDY AND MODEL DEVELOPMENT FOR TYROSINE KINASE INHIBITORS. Journal of Data Acquisition and Processing, 38(2), 3453–3467. https://doi.org/10.5281/zenodo.777218
Oubahmane, M., Hdoufane, I., Delaite, C., Sayede, A., Cherqaoui, D., & El Allali, A. (2023). Design of Potent Inhibitors Targeting the Main Protease of SARS-CoV-2 Using QSAR Modeling, Molecular Docking, and Molecular Dynamics Simulations. Pharmaceuticals, 16(4), 608. https://doi.org/10.3390/ph16040608
De, P., & Roy, K. (2023). Computational modeling of PET imaging agents for vesicular acetylcholine transporter (VAChT) protein binding affinity: application of 2D-QSAR modeling and molecular docking techniques. In Silico Pharmacology, 11(1), 9. https://doi.org/10.1007/s40203-023-00146-4
Idrovo‐Encalada, A. M., Rojas, A. M., Fissore, E. N., Tripaldi, P., Pis Diez, R., & Rojas, C. (2023). Chemoinformatic modelling of the antioxidant activity of phenolic compounds. Journal of the Science of Food and Agriculture, (January). https://doi.org/10.1002/jsfa.12561
Schieferdecker, S., & Vock, E. (2023). Development of Pharmacophore Models for the Important Off-Target 5-HT 2B Receptor. Journal of Medicinal Chemistry, 66(2), 1509–1521. https://doi.org/10.1021/acs.jmedchem.2c01679
Ciura, K., Fryca, I., & Gromelski, M. (2023). Prediction of the retention factor in cetyltrimethylammonium bromide modified micellar electrokinetic chromatography using a machine learning approach. Microchemical Journal, 187, 108393. https://doi.org/10.1016/j.microc.2023.108393
Apprato, G., D’Agostini, G., Rossetti, P., Ermondi, G., & Caron, G. (2023). In Silico Tools to Extract the Drug Design Information Content of Degradation Data: The Case of PROTACs Targeting the Androgen Receptor. Molecules, 28(3), 1206. https://doi.org/10.3390/molecules28031206
Kowalska, D., Sosnowska, A., Bulawska, N., Stępnik, M., Besselink, H., Behnisch, P., & Puzyn, T. (2023). How the Structure of Per- and Polyfluoroalkyl Substances (PFAS) Influences Their Binding Potency to the Peroxisome Proliferator-Activated and Thyroid Hormone Receptors—An In Silico Screening Study. Molecules, 28(2), 479. https://doi.org/10.3390/molecules28020479
Kumar, S., Manoharan, A., J, J., Abdelgawad, M. A., Mahdi, W. A., Alshehri, S., Ghoneim, M. M., Pappachen, L. K., Zachariah, S. M., Aneesh, T. P., & Mathew, B. (2023). Exploiting butyrylcholinesterase inhibitors through a combined 3-D pharmacophore modeling, QSAR, molecular docking, and molecular dynamics investigation. RSC Advances, 13(14), 9513–9529. https://doi.org/10.1039/D3RA00526G
Ksenofontov, A., Isaev, Y., Lukanov, M., Makarov, D. M., Eventova, V., Khodov, I., & Berezin, M. B. (2023). Accurate prediction of 11B NMR chemical shift of BODIPYs via machine learning. Physical Chemistry Chemical Physics, 19. https://doi.org/10.1039/D3CP00253E
Castillo-Garit, J. A., Cañizares-Carmenate, Y., Pham-The, H., Pérez-Doñate, V., Torrens, F., & Pérez-Giménez, F. (2023). A Review of Computational Approaches Targeting SARS-CoV-2 Main Protease to the Discovery of New Potential Antiviral Compounds. Current Topics in Medicinal Chemistry, 23(1), 3–16. https://doi.org/10.2174/2667387816666220426133555
Kumar, V., Saha, A., & Roy, K. (2023). Multi-target QSAR modeling for the identification of novel inhibitors against Alzheimer’s disease. Chemometrics and Intelligent Laboratory Systems, 233(August 2022), 104734. https://doi.org/10.1016/j.chemolab.2022.104734
Kumar, A., Podder, T., Kumar, V., & Ojha, P. K. (2023). Risk assessment of aromatic organic chemicals to T. pyriformis in environmental protection using regression-based QSTR and Read-Across algorithm. Process Safety and Environmental Protection, 170(September 2022), 842–854. https://doi.org/10.1016/j.psep.2022.12.067
Tao, L., He, J., Arbaugh, T., McCutcheon, J. R., & Li, Y. (2023). Machine learning prediction on the fractional free volume of polymer membranes. Journal of Membrane Science, 665(October 2022), 121131. https://doi.org/10.1016/j.memsci.2022.121131
Zhu, T., Chen, Y., & Tao, C. (2023). Multiple machine learning algorithms assisted QSPR models for aqueous solubility: Comprehensive assessment with CRITIC-TOPSIS. Science of The Total Environment, 857(September 2022), 159448. https://doi.org/10.1016/j.scitotenv.2022.159448
Gurumayum, S., Bharadwaj, S., Sheikh, Y., Barge, S. R., Saikia, K., Swargiary, D., Ahmed, S. A., Thakur, D., & Borah, J. C. (2023). Taxifolin-3-O-glucoside from Osbeckia nepalensis Hook. mediates antihyperglycemic activity in CC1 hepatocytes and in diabetic Wistar rats via regulating AMPK/G6Pase/PEPCK signaling axis. Journal of Ethnopharmacology, 303(September 2022), 115936. https://doi.org/10.1016/j.jep.2022.115936
Desai, S., Patel, V. K., Patel, A. S., & Patel, J. (2023). Development and Validation of an Easily Interpretable QSAR Model for Inhibitory Activity Prediction Against Dihydrofolate Reductase from Candida Albicans. Biological Forum – An International Journal, 15(1), 505–513.
Zhu, T., Yu, Y., & Tao, T. (2023). A comprehensive evaluation of liposome/water partition coefficient prediction models based on the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method: Challenges from different descriptor dimension reduction methods and machi. Journal of Hazardous Materials, 443(PA), 130181. https://doi.org/10.1016/j.jhazmat.2022.130181
Mitra, S., Halder, A. K., Ghosh, N., Mandal, S. C., & Cordeiro, M. N. D. S. (2023). Multi-model in silico characterization of 3-benzamidobenzoic acid derivatives as partial agonists of Farnesoid X receptor in the management of NAFLD. Computers in Biology and Medicine, 157(December 2022), 106789. https://doi.org/10.1016/j.compbiomed.2023.106789
2022
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Sharma, A., Kumar, R., & Varadwaj, P. K. (2022). Decoding Seven Basic Odors by Investigating Pharmacophores and Molecular Features of Odorants. Current Bioinformatics, 17(8), 759–774. https://doi.org/10.2174/1574893617666220519111254
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Maloney, E. M., Villeneuve, D. L., Blackwell, B. R., Vitense, K., Corsi, S. R., Pronschinske, M. A., … Ankley, G. T. (2022). A framework for prioritizing contaminants in retrospective ecological assessments: Application in the Milwaukee Estuary (Milwaukee, WI). Integrated Environmental Assessment and Management, 00(00), 1–21. https://doi.org/10.1002/ieam.4725
Darie, I., & Praisler, M. (2022). Principal Component Analysis Assessing the Potential Clustering of 2C-x and DOx Amphetamines. 2022 E-Health and Bioengineering Conference (EHB), 01–04. https://doi.org/10.1109/EHB55594.2022.9991592
García Jiménez, D., Rossi Sebastiano, M., Vallaro, M., Mileo, V., Pizzirani, D., Moretti, E., Ermondi, G., & Caron, G. (2022). Designing Soluble PROTACs: Strategies and Preliminary Guidelines. Journal of Medicinal Chemistry. https://doi.org/10.1021/acs.jmedchem.2c00201
Chen, Z., Yang, B., Song, N., Chen, T., Zhang, Q., Li, C., Jiang, J., Chen, T., Yu, Y., & Liu, L. X. (2022). Machine learning-guided design of organic phosphorus-containing flame retardants to improve the limiting oxygen index of epoxy resins. Chemical Engineering Journal, July, 140547. https://doi.org/10.1016/j.cej.2022.140547
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Miller, K. J., Thorpe, C., Eggenberger, A. L., Lee, K., Kang, M., Liu, F., Wang, K., & Jiang, S. (2022). Identifying Factors that Determine Effectiveness of Delivery Agents in Biolistic Delivery Using a Library of Amine-Containing Molecules. ACS Applied Bio Materials, 5(10), 4972–4980. https://doi.org/10.1021/acsabm.2c00689
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Shah, S., Arora, S., Chaple, D., Badne, P., Yende, S., Khonde, S., & Deshmukh, S. (2022). 2D-QSAR Modeling of Chalcone Analogues as Angiotensin Converting Enzyme Inhibitor. Biointerface Research in Applied Chemistry, 13(4), 370. https://doi.org/10.33263/BRIAC134.370
Desai, S. A. (2022). QSAR Regression Models for Predicting the Activity of Inhibitors of Staphylococcus Epidermidis. International Journal of Quantitative Structure-Property Relationships, 7(1), 1–17. https://doi.org/10.4018/IJQSPR.313712
Huoyu, R., Zhiqiang, Z., Guofang, J., Zhanggao, L., & Zhenzhen, X. (2022). Quantitative Structure-Property Relationship for Critical Temperature of Alkenes with Quantum-Сhemical and Topological Indices. Russian Journal of Physical Chemistry A, 96(11), 2329–2334. https://doi.org/10.1134/S0036024422110267
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Würger, T., Wang, L., Snihirova, D., Deng, M., Lamaka, S. V., Winkler, D. A., Höche, D., Zheludkevich, M. L., Meißner, R. H., & Feiler, C. (2022). Data-driven selection of electrolyte additives for aqueous magnesium batteries. Journal of Materials Chemistry A, 10(40), 21672–21682. https://doi.org/10.1039/D2TA04538A
Makarov, D. M., Fadeeva, Y. A., Safonova, E. A., & Shmukler, L. E. (2022). Predictive modeling of antibacterial activity of ionic liquids by machine learning methods. Computational Biology and Chemistry, 101(July), 107775. https://doi.org/10.1016/j.compbiolchem.2022.107775
Desai, S., & Meshram, D. (2022). Development of Interpretable QSAR Model for Quick Screening of Inhibitors against Tyrosine Protein Kinase JAK-2. Chemical Science & Engineering Research, 4(100), 46–53. https://doi.org/10.36686/Ariviyal.CSER.2022.04.10.056
Banerjee, A., De, P., Kumar, V., Kar, S., & Roy, K. (2022). Quick and efficient quantitative predictions of androgen receptor binding affinity for screening Endocrine Disruptor Chemicals using 2D-QSAR and Chemical Read-Across. Chemosphere, 309(P1), 136579. https://doi.org/10.1016/j.chemosphere.2022.136579
Speck-Planche, A., & Kleandrova, V. V. (2022). The latest guidance on the simultaneous design of virtually active and non-hemolytic peptides. Expert Opinion on Drug Discovery, 1–3. https://doi.org/10.1080/17460441.2022.2128756
Ghosh, A., Panda, P., Halder, A. K., & Cordeiro, M. N. D. S. (2022). In silico characterization of aryl benzoyl hydrazide derivatives as potential inhibitors of RdRp enzyme of H5N1 influenza virus. Frontiers in Pharmacology, 13(September), 1–16. https://doi.org/10.3389/fphar.2022.1004255
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Rossi Sebastiano, M., Garcia Jimenez, D., Vallaro, M., Caron, G., & Ermondi, G. (2022). Refinement of Computational Access to Molecular Physicochemical Properties: From Ro5 to bRo5. Journal of Medicinal Chemistry, 65(18), 12068–12083. https://doi.org/10.1021/acs.jmedchem.2c00774
Chatterjee, M., & Roy, K. (2022). Chemical similarity and machine learning-based approaches for the prediction of aquatic toxicity of binary and multicomponent pharmaceutical and pesticide mixtures against Aliivibrio fischeri. Chemosphere, 308(P3), 136463. https://doi.org/10.1016/j.chemosphere.2022.136463
Schindler, K., Cortat, Y., Nedyalkova, M., Crochet, A., Lattuada, M., Pavic, A., & Zobi, F. (2022). Antimicrobial Activity of Rhenium Di- and Tricarbonyl Diimine Complexes: Insights on Membrane-Bound S. aureus Protein Binding. Pharmaceuticals, 15(9), 1107. https://doi.org/10.3390/ph15091107
Chen, J., Zhu, F., Qin, H., Song, Z., Qi, Z., & Sundmacher, K. (2022). Rational eutectic solvent design by linking regular solution theory with QSAR modelling. Chemical Engineering Science, 262, 118042. https://doi.org/10.1016/j.ces.2022.118042
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Piekuś-Słomka, N., Zapadka, M., & Kupcewicz, B. (2022). Methoxy and methylthio-substituted trans-stilbene derivatives as CYP1B1 inhibitors – QSAR study with detailed interpretation of molecular descriptors. Arabian Journal of Chemistry, 15(11), 104204. https://doi.org/10.1016/j.arabjc.2022.104204
Kelleci Çelik, F., & Karaduman, G. (2022). In silico QSAR modeling to predict the safe use of antibiotics during pregnancy. Drug and Chemical Toxicology, 1–10. https://doi.org/10.1080/01480545.2022.2113888
Zhu, T., Tao, C., Cheng, H., & Cong, H. (2022). Versatile in silico modelling of microplastics adsorption capacity in aqueous environment based on molecular descriptor and machine learning. Science of The Total Environment, 846(May), 157455. https://doi.org/10.1016/j.scitotenv.2022.157455
Baba, H., Urano, R., Nagai, T., & Okazaki, S. (2022). Prediction of self‐diffusion coefficients of chemically diverse pure liquids by all‐atom molecular dynamics simulations. Journal of Computational Chemistry, July, 1–9. https://doi.org/10.1002/jcc.26975
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Pal, S., Ghosh Dastidar, U., Ghosh, T., Ganguly, D., & Talukdar, A. (2022). Integration of Ligand-Based and Structure-Based Methods for the Design of Small-Molecule TLR7 Antagonists. Molecules, 27(13), 4026. https://doi.org/10.3390/molecules27134026
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Mukherjee, R. K., Kumar, V., & Roy, K. (2022). Chemometric modeling of plant protection products ( PPPs ) for the prediction of acute contact toxicity against honey bees ( A . mellifera ): A 2D-QSAR approach. Journal of Hazardous Materials, 423(PB), 127230. https://doi.org/10.1016/j.jhazmat.2021.127230
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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
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