3
How to cite this paper
APA: Ouabane, M., Dichane, Z., Alaqarbeh, M., Alnajjar, R., Sekkate, C., Lakhlifi, T & Bouachrine, M. (2025). The use of combined machine learning and in-silico molecular approaches for the study and the prediction of anti-HIV activity. Current Chemistry Letters, 14(1), 205-232.
Chicago/Turabian: Ouabane, M., Dichane, Z., Alaqarbeh, M., Alnajjar, R., Sekkate, C., Lakhlifi, T & Bouachrine, M. 2025. "The use of combined machine learning and in-silico molecular approaches for the study and the prediction of anti-HIV activity." Current Chemistry Letters 14, no. 1 (2025): 205-232.
AMA: Ouabane, M., Dichane, Z., Alaqarbeh, M., Alnajjar, R., Sekkate, C., Lakhlifi, T & Bouachrine, M. The use of combined machine learning and in-silico molecular approaches for the study and the prediction of anti-HIV activity. Current Chemistry Letters. 2025;14(1):205-232.
References
[1] Halder AK., Jha T. (2010) Validated predictive QSAR modeling of N-aryl-oxazolidinone-5-carboxamides for anti-HIV protease activity. Bioorg Med Chem Lett., 20 (20) 6082–7.
[2] Awi NJ, Teow S. (2018) Antibody-Mediated Therapy against HIV/AIDS: Where Are We Standing Now?. J Pathog., (20)1–9.
[3] Su Q, Xu X, Zhou L. (2008) QSAR model of triterpene derivatives as potent anti-HIV agents. Mol Simul., 34 (7) 651–9.
[4] Sierawska O., Małkowska P., Taskin C., Hrynkiewicz R., Mertowska P., Grywalska E., Korzeniowski T., Torres K., Surowiecka A., Niedźwiedzka-Rystwej P., Strużyna J. (2022) Innate Immune System Response to Burn Damage—Focus on Cytokine Alteration. Int J Mol Sci., 23 (2) 117.
[5] Vang R., Shih I. M., Kurman R. J. (2013) Fallopian tube precursors of ovarian low- and high-grade serous neoplasms. Histopathology., 62 (1) 44–58.
[6] Mcarthur J. C., Steiner J., Sacktor N., Nath A. (2010) Human Immunodeficiency Virus- Associated Neurocognitive Disorders Mind the Gap., Ann. Neurol. 67 (6) 699–714.
[7] Watkins C. C., Treisman G. J. (2015) Cognitive impairment in patients with Aids – Prevalence and severity. Dove Med Press., 7 35–47.
[8] Karkhur S, Hasanreisoglu M, Vigil E, Halim MS, Hassan M, Plaza C., Nguyen N. V., Afridi R., Tran A. T., Do D. V., Sepah Y. J., Nguyen Q. D. (2019) Interleukin-6 inhibition in the management of non-infectious uveitis and beyond. J. Ophthal. Inflamm. Infect. 9 (1) 17.
[9] Garcia J. C. (2015) Review of Radiologic Infectious and Non-infectious Pulmonary Complications in Human Immunodeficiency Virus Patients. J. Pulm. Respir Med. 5 (3) 1000260.
[10] Kumar S., Singh S., Luthra K. (2023) An Overview of Human Anti-HIV-1 Neutralizing Antibodies against Diverse Epitopes of HIV-1. ACS Omega. 8 (8) 7252–61.
[11] Bloch M., John M., Smith D., Rasmussen T.A. (2020) Wright E. Managing HIV-associated in fl ammation and ageing in the era of modern ART. HIV Med. 21 2–16.
[12] Ensoli B, Moretti S, Borsetti A, Teresa M, Stefano M, Picconi O, Tripiciano A., Sgadar C., Monini P., Cafaro A. (2021) New insights into pathogenesis point to HIV 1 Tat as a key vaccine target. Arch Virol. 166 2955–74.
[13] Zhang F., Wang Z., Peijnenburg W. J. G. M., Vijver M. G. (2023) Machine learning-driven QSAR models for predicting the mixture toxicity of nanoparticles. Environ Int. 177 108025.
[14] Durrant J. D., Amaro R. E. (2015) Machine-learning techniques applied to antibacterial drug discovery. Chem Biol Drug Des. 85 14–21.
[15] Chen M., Yang X., Lai X., Gao Y. (2015) 2D and 3D QSAR models for identifying diphenylpyridylethanamine based inhibitors against cholesteryl ester transfer protein. Bioorg Med Chem Lett. 25 4487–95.
[16] Maltarollo V. G., Gertrudes J.C., Oliveira P. R., Honorio K. M. (2015) Applying machine learning techniques for ADME-Tox prediction: A review. Expert Opin Drug Metab Toxicol. 11 259–71.
[17] Nongonierma A. B., Fitzgerald R. J. (2016) Learnings from quantitative structure-activity relationship (QSAR) studies with respect to food protein-derived bioactive peptides: A review. RSC Adv. 6 75400–13.
[18] Sarfaraz k. N., Zamara M. (2023) Recent Advances in Machine-Learning-Based Chemoinformatics : A Comprehensive Review. Int. J. Mol. Sci. 24(14) 11488
[19] Ghafourian T., Cronin M. T. D. The impact of variable selection on the modelling of oestrogenicity. SAR QSAR Environ. Res. 16 171–90.
[20] Shahlaei M., Madadkar-Sobhani A., Saghaie L., Fassihi A. (2012) Application of an expert system based on Genetic Algorithm-Adaptive Neuro-Fuzzy Inference System (GA-ANFIS) in QSAR of cathepsin K inhibitors. Expert. Syst. Appl. 39 (6) 6182-6191.
[21] Tirelli T., Pessani D. (2011) Importance of feature selection in decision-tree and artificial-neural-network ecological applications. Alburnus alburnus alborella: A practical example. Ecol. Inform. 6 (5) 309–315.
[22] Izza Y., Ignatiev A., Silva J. M. (2021) On Explaining Decision Trees To cite this version: HAL Id: hal-03312480 pp:0–21.
[23] Pandya, P. N., Kumar, S. P., Bhadresha, K., Patel, C. N., Patel, S. K., Rawal, R. M., & Mankad, A. U. (2020) Identification of promising compounds from curry tree with cyclooxygenase inhibitory potential using a combination of machine learning, molecular docking, dynamics simulations and binding free energy calculations. Mol. Simul. 46 (11) 812–822.
[24] Qi A., Zhao D., Yu F., Asghar A., Wu Z., Cai Z., Alenezi F., Mansour R. F., Chen H., Chen M. (2022) Directional mutation and crossover boosted ant colony optimization with application to COVID-19 X-ray image segmentation. Comput. Biol. Med. 148 105810.
[25] Yadav A. K., Singh T. R. (2021) Novel inhibitors design through structural investigations and simulation studies for human PKMTs (SMYD2) involved in cancer. Mol. Simul. 47 (14) 1149–1158.
[26] Toropov A. A., Toropova A. P., Carnesecchi E., Benfenati E., Dorne J. L. (2020) The index of ideality of correlation and the variety of molecular rings as a base to improve model of HIV-1 protease inhibitors activity. Struct. Chem. 31 1441–8.
[27] Liu Y., Liu Y., Wang S., Zhu X. (2023) LBCE-XGB: A XGBoost Model for Predicting Linear B-Cell Epitopes Based on BERT Embeddings. Interdiscip. Sci. 15 293–305.
[28] Chen T., Guestrin C. (2016) XGBoost: A scalable tree boosting system. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 13 (17) 785–94.
[29] Sikander R., Ghulam A., Ali F. (2022) XGB-DrugPred: computational prediction of druggable proteins using extreme gradient boosting and optimized features set. Sci. Rep. 12 5505.
[30] Huang B., Wang C. (2024) Retraction Note: Research on Data Analysis of Efficient Innovation and Entrepreneurship Practice Teaching Based on LightGBM Classification Algorithm. Int. J. Comput. Intell. Syst. 17 (52).
[31] Ju Y., Sun G., Chen Q., Zhang M., Zhu H., Rehman M. U. (2019) A model combining convolutional neural network and lightgbm algorithm for ultra-short-term wind power forecasting. IEEE Access. 7 28309 – 28318.
[32] Zhang D., Gong Y. (2020) The Comparison of LightGBM and XGBoost Coupling Factor Analysis and Prediagnosis of Acute Liver Failure. IEEE Access. 8 220990 - 221003.
[33] Ke G., Meng Q., Finley T., Wang T., Chen W., Ma W., Ye Q., Liu. Y. (2023) LightGBM : A Highly Efficient Gradient Boosting Decision Tree To cite this version : HAL Id : hal-03953007: 0–9.
[34] Mukherjee K., Colón Y. J. (2021) Machine learning and descriptor selection for the computational discovery of metal-organic frameworks. Mol. Simul. 47(10–11) 857–877.
[35] AlKheder S., AlOmair A. (2022) Urban traffic prediction using metrological data with fuzzy logic, long short-term memory (LSTM), and decision trees (DTs). Nat. Hazards. 111 1685–1719.
[36] Aher R. B., Khan K., Roy K. (2020) A brief introduction to quantitative structure-activity relationships as useful tools in predictive ecotoxicology. SAR QSAR Environ Res. 27–53.
[37] Calle L., Marrero-Ponce Y., Mora J. R. (2021) Molecular simulation of the (GPx)-like antioxidant activity of ebselen derivatives through machine learning techniques. Mol. Simul. 47 1402–10.
[38] Johnston A., Johnston B. F., Kennedy A. R., Florence A. J. (2008) Targeted crystallisation of novel carbamazepine solvates based on a retrospective Random Forest classification. Cryst. Eng. Comm. 10 23–5.
[39] Deepak T., Mohd Anul H., Gazi R., Prashant B., Joydip D. (2019) Comparison of Performance of Artificial Neural Network (ANN) and Random Forest (RF) in the Classification of Land Cover Zones of Urban Slum Region. LNCE. 51 225–36.
[40] Ferreira Neto, D. C., Alencar Lima, J., Sobreiro Francisco Diz de Almeida, J., Costa França, T. C., Jorge do Nascimento, C., & Figueroa Villar, J. D. (2018) New semicarbazones as gorge-spanning ligands of acetylcholinesterase and potential new drugs against Alzheimer’s disease: Synthesis, molecular modeling, NMR, and biological evaluation. J. Biomol. Struct. Dyn. 36 (15) 4099–4113.
[41] Danush S., Dutta A. (2023) Machine learning-based framework for predicting toxicity of ionic liquids. Mater Today Proc. 72 (1) 75–80.
[42] Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., & Liu, T. (2017). LightGBM: A Highly Efficient Gradient Boosting Decision Tree. Neural Information Processing Systems.
[43] Xu Y., Liaw A., Sheridan R. P., Svetnik V. (2023) Development and Evaluation of Conformal Prediction Methods for QSAR. q-bio.BM. https://doi.org/10.48550/arXiv.2304.00970.
[44] Vishwakarma G., Sonpal A., Hachmann J. (2021) Metrics for Benchmarking and Uncertainty Quantification: Quality, Applicability, and Best Practices for Machine Learning in Chemistry. Trends Chem. 3 146–56.
[45] Shahhosseini M., Hu G., Pham H. (2022) Optimizing ensemble weights and hyperparameters of machine learning models for regression problems. Machine Learning with Applications 7 100251.
[46] Noviandy T. R., Maulana A., Emran T. B., Idroes G. M., Idroes R. (2023) QSAR Classification of Beta-Secretase 1 Inhibitor Activity in Alzheimer’s Disease Using Ensemble Machine Learning Algorithms. HJAS. 1.
[47] Lusci A., Pollastri G., Baldi P. (2013) Deep architectures and deep learning in chemoinformatics: The prediction of aqueous solubility for drug-like molecules. J. Chem. Inf. Model. 53 1563–75.
[48] Rao, H., Zeng, X., Wang, Y., He, H., Zhu, F., Li, Z., & Chen, Y. (2012). Identification of DNA adduct formation of small molecules by molecular descriptors and machine learning methods. Mol. Simul. 38 (4) 259–273.
[49] Liu X, Zhu B, Dai XW, Xu ZA, Li R, Qian Y, Lu Y. P. Zhang Y., Liu Y., Zheng J. (2023) GBDT_KgluSite: An improved computational prediction model for lysine glutarylation sites based on feature fusion and GBDT classifier. BMC Genomics 24.
[50] Li N., Chen K., Bai J., Geng Z., Tang Y., Hou Y., Fan F., Ai X., Hu Y., Meng X., Wang X., Zhang Y. (2021) Tibetan medicine Duoxuekang ameliorates hypobaric hypoxia-induced brain injury in mice by restoration of cerebrovascular function. J. Ethnopharmacol. 270 113629.
[51] Hamada M., Zaqoot H. A., Sethar W. A. (2023) Using Supervised Learning Machine Approach to Predict Water Quality at Gaza’s Wastewater Treatment Plant. Environmental Science: Advances. https://doi.org/10.1039/d3va00170a.
[52] Binuya M. A. E, Engelhardt E. G., Schats W., Schmidt M. K., Steyerberg E. W. (2022) Methodological guidance for the evaluation and updating of clinical prediction models: a systematic review. BMC Med. Res. Methodol. 22 316.
[53] Ouabane M., Tabti K., Hajji H., Elbouhi M., Khaldan A., Elkamel K., Sbai A., Ajana M. A., Sekkate C. Bouachrine M. Lakhlifi T. (2023) Structure-odor relationship in pyrazines and derivatives: a physicochemical study using 3D-QSPR, HQSPR, Monte Carlo, Molecular Docking, ADME-Tox and. Arabian Journal of Chemistry 16 (11) 105207.
[54] Ouabane M., Hajji H, Belhassan A. Koubi Y., Elbouhi M., Badaoui H., Sekkate C., Lakhlifi T. (2022) RHAZES: Green and Applied Chemistry molecules in pectin gels of different concentration. RHAZES: Green and Applied Chemistry 14 15-35.
[55] Luo X., Yang X., Qiao X., Wang Y., Chen J., Wei X., Willie J. G. M. P. (2017) Development of a QSAR model for predicting aqueous reaction rate constants of organic chemicals with hydroxyl radicals. Environ Sci Process Impacts 19 350–6.
[56] Todeschini R., Ballabio D., Grisoni F. (2016) Beware of Unreliable Q2! A Comparative Study of Regression Metrics for Predictivity Assessment of QSAR Models. J. Chem. Inf. Model. 56 (10) 1905-1913.
[57] Zou G. Y. (2007) Toward Using Confidence Intervals to Compare Correlations. Psychol Methods 12 (4) 399-413.
[58] Mulaik S. A., Raju N. S., Harshman R. A. (2016) There is a time and a place for significance testing. What If There Were No Significance Tests. Classic Edition 2016: 61–106.
[59] Hajji H, En-nahli F, Aanouz I, Zaki H., Lakhlifi T., Ajana M. A., Bouachrine M. (2021) Catastrophic Collision Between Obesity and COVID-19 Have Evoked the Computational Chemistry for Research in Silico Design of New CaMKKII Inhibitors Against Obesity by Using 3D-QSAR, Molecular Docking, and ADMET. Orbital 13 (4) 316-327.
[60] Zhao D., Zhong S. (2021) Binding mechanisms of varic acid inhibitors on protein tyrosine phosphatase 1B and in silico design of the novel derivatives. Mol. Simul. 47 (9) 771–784.
[61] Wichur T., Pasieka A., Godyń J., Panek D., Góral I., Latacz G., et al. Discovery of 1-(phenylsulfonyl)-1H-indole-based multifunctional ligands targeting cholinesterases and 5-HT6 receptor with anti-aggregation properties against amyloid-beta and tau. Eur. J. Med. Chem. 225 113783.
[62] Belghalia E., Ouabane M., El Bahi S., Muzzammel H., Sbai A., Lakhlifi T., Bouachrine M. (2023) In silico research on new sulfonamide derivatives as BRD4 inhibitors targeting acute myeloid leukemia using various computational techniques including 3D-QSAR, HQSAR, molecular docking, ADME /Tox, and molecular dynamics. J. Biomol. Struct. Dyn. 0 1–19.
[63] Tabti K., Elmchichi L., Sbai A., Maghat H., Bouachrine M., Lakhlifi T. (2022) Molecular modelling of antiproliferative inhibitors based on SMILES descriptors using Monte-Carlo method, docking, MD simulations and ADME/Tox studies. Mol. Simul. 48 (17) 1575–1591.
[64] Ouabane M., Zaki K., Tabti K., Alaqarbeh M., Sbai A., Sekkate C., Bouachrine M., Lakhlifi T. (2024) Molecular toxicity of nitrobenzene derivatives to Tetrahymena pyriformis based on SMILES descriptors using Monte Carlo, Docking, and MD simulations. Comput. Biol. Med.: 169 107880.
[65] Calugi L., Sautariello G., Lenci E., Mattei M. L., Coppa C., Cini N., Contini A., Trabouchi A. (2023) Identification of a short ACE2-derived stapled peptide targeting the SARS-CoV-2 spike protein. Eur. J. Med. Chem. 249 (2023) 115118.
[66] Berenger F., Kumar A., Zhang K. Y. J., Yamanishi Y. (2021) Lean-Docking: Exploiting Ligands’ Predicted Docking Scores to Accelerate Molecular Docking. J. Chem. Inf. Model. 61 2341–52.
[67] Alnajjar R., Mostafa A., Kandeil A., Al-Karmalawy A. A. (2020) Molecular docking, molecular dynamics, and in vitro studies reveal the potential of angiotensin II receptor blockers to inhibit the COVID-19 main protease. Heliyon 6 e05641.
[68] Alnajjar R., Mohamed N., Kawafi N. (2021) Bicyclo[1.1.1]Pentane as Phenyl Substituent in Atorvastatin Drug to improve Physicochemical Properties: Drug-likeness, DFT, Pharmacokinetics, Docking, and Molecular Dynamic Simulation. J. Mol. Struct. 1230 129628.
[69] Martyna G. J., Klein M. L., Tuckerman M. (1992) Nosé-Hoover chains: The canonical ensemble via continuous dynamics. J. Chem. Phys. 97 2635–43.
[70] El-Masry R. M., Al-Karmalawy A. A., Alnajjar R., Mahmoud S. H., Mostafa A., Kadry H. H, Abou-Seri S. M., Taher A. T. (2022) Newly synthesized series of oxoindole–oxadiazole conjugates as potential anti-SARS-CoV-2 agents: in silico and in vitro studies. New J. Chem. 46 5078-5090.
[71] Mahmoud N., Piacente S., Pizza C., Burke A., Khan A. I., Hay A. J. (1996) The Anti-HIV Activity and Mechanisms of Action of Pure Compounds Isolated from Rosa damascene. Biochem. Biophys. Res. Commun. 229, 73-79.
[2] Awi NJ, Teow S. (2018) Antibody-Mediated Therapy against HIV/AIDS: Where Are We Standing Now?. J Pathog., (20)1–9.
[3] Su Q, Xu X, Zhou L. (2008) QSAR model of triterpene derivatives as potent anti-HIV agents. Mol Simul., 34 (7) 651–9.
[4] Sierawska O., Małkowska P., Taskin C., Hrynkiewicz R., Mertowska P., Grywalska E., Korzeniowski T., Torres K., Surowiecka A., Niedźwiedzka-Rystwej P., Strużyna J. (2022) Innate Immune System Response to Burn Damage—Focus on Cytokine Alteration. Int J Mol Sci., 23 (2) 117.
[5] Vang R., Shih I. M., Kurman R. J. (2013) Fallopian tube precursors of ovarian low- and high-grade serous neoplasms. Histopathology., 62 (1) 44–58.
[6] Mcarthur J. C., Steiner J., Sacktor N., Nath A. (2010) Human Immunodeficiency Virus- Associated Neurocognitive Disorders Mind the Gap., Ann. Neurol. 67 (6) 699–714.
[7] Watkins C. C., Treisman G. J. (2015) Cognitive impairment in patients with Aids – Prevalence and severity. Dove Med Press., 7 35–47.
[8] Karkhur S, Hasanreisoglu M, Vigil E, Halim MS, Hassan M, Plaza C., Nguyen N. V., Afridi R., Tran A. T., Do D. V., Sepah Y. J., Nguyen Q. D. (2019) Interleukin-6 inhibition in the management of non-infectious uveitis and beyond. J. Ophthal. Inflamm. Infect. 9 (1) 17.
[9] Garcia J. C. (2015) Review of Radiologic Infectious and Non-infectious Pulmonary Complications in Human Immunodeficiency Virus Patients. J. Pulm. Respir Med. 5 (3) 1000260.
[10] Kumar S., Singh S., Luthra K. (2023) An Overview of Human Anti-HIV-1 Neutralizing Antibodies against Diverse Epitopes of HIV-1. ACS Omega. 8 (8) 7252–61.
[11] Bloch M., John M., Smith D., Rasmussen T.A. (2020) Wright E. Managing HIV-associated in fl ammation and ageing in the era of modern ART. HIV Med. 21 2–16.
[12] Ensoli B, Moretti S, Borsetti A, Teresa M, Stefano M, Picconi O, Tripiciano A., Sgadar C., Monini P., Cafaro A. (2021) New insights into pathogenesis point to HIV 1 Tat as a key vaccine target. Arch Virol. 166 2955–74.
[13] Zhang F., Wang Z., Peijnenburg W. J. G. M., Vijver M. G. (2023) Machine learning-driven QSAR models for predicting the mixture toxicity of nanoparticles. Environ Int. 177 108025.
[14] Durrant J. D., Amaro R. E. (2015) Machine-learning techniques applied to antibacterial drug discovery. Chem Biol Drug Des. 85 14–21.
[15] Chen M., Yang X., Lai X., Gao Y. (2015) 2D and 3D QSAR models for identifying diphenylpyridylethanamine based inhibitors against cholesteryl ester transfer protein. Bioorg Med Chem Lett. 25 4487–95.
[16] Maltarollo V. G., Gertrudes J.C., Oliveira P. R., Honorio K. M. (2015) Applying machine learning techniques for ADME-Tox prediction: A review. Expert Opin Drug Metab Toxicol. 11 259–71.
[17] Nongonierma A. B., Fitzgerald R. J. (2016) Learnings from quantitative structure-activity relationship (QSAR) studies with respect to food protein-derived bioactive peptides: A review. RSC Adv. 6 75400–13.
[18] Sarfaraz k. N., Zamara M. (2023) Recent Advances in Machine-Learning-Based Chemoinformatics : A Comprehensive Review. Int. J. Mol. Sci. 24(14) 11488
[19] Ghafourian T., Cronin M. T. D. The impact of variable selection on the modelling of oestrogenicity. SAR QSAR Environ. Res. 16 171–90.
[20] Shahlaei M., Madadkar-Sobhani A., Saghaie L., Fassihi A. (2012) Application of an expert system based on Genetic Algorithm-Adaptive Neuro-Fuzzy Inference System (GA-ANFIS) in QSAR of cathepsin K inhibitors. Expert. Syst. Appl. 39 (6) 6182-6191.
[21] Tirelli T., Pessani D. (2011) Importance of feature selection in decision-tree and artificial-neural-network ecological applications. Alburnus alburnus alborella: A practical example. Ecol. Inform. 6 (5) 309–315.
[22] Izza Y., Ignatiev A., Silva J. M. (2021) On Explaining Decision Trees To cite this version: HAL Id: hal-03312480 pp:0–21.
[23] Pandya, P. N., Kumar, S. P., Bhadresha, K., Patel, C. N., Patel, S. K., Rawal, R. M., & Mankad, A. U. (2020) Identification of promising compounds from curry tree with cyclooxygenase inhibitory potential using a combination of machine learning, molecular docking, dynamics simulations and binding free energy calculations. Mol. Simul. 46 (11) 812–822.
[24] Qi A., Zhao D., Yu F., Asghar A., Wu Z., Cai Z., Alenezi F., Mansour R. F., Chen H., Chen M. (2022) Directional mutation and crossover boosted ant colony optimization with application to COVID-19 X-ray image segmentation. Comput. Biol. Med. 148 105810.
[25] Yadav A. K., Singh T. R. (2021) Novel inhibitors design through structural investigations and simulation studies for human PKMTs (SMYD2) involved in cancer. Mol. Simul. 47 (14) 1149–1158.
[26] Toropov A. A., Toropova A. P., Carnesecchi E., Benfenati E., Dorne J. L. (2020) The index of ideality of correlation and the variety of molecular rings as a base to improve model of HIV-1 protease inhibitors activity. Struct. Chem. 31 1441–8.
[27] Liu Y., Liu Y., Wang S., Zhu X. (2023) LBCE-XGB: A XGBoost Model for Predicting Linear B-Cell Epitopes Based on BERT Embeddings. Interdiscip. Sci. 15 293–305.
[28] Chen T., Guestrin C. (2016) XGBoost: A scalable tree boosting system. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 13 (17) 785–94.
[29] Sikander R., Ghulam A., Ali F. (2022) XGB-DrugPred: computational prediction of druggable proteins using extreme gradient boosting and optimized features set. Sci. Rep. 12 5505.
[30] Huang B., Wang C. (2024) Retraction Note: Research on Data Analysis of Efficient Innovation and Entrepreneurship Practice Teaching Based on LightGBM Classification Algorithm. Int. J. Comput. Intell. Syst. 17 (52).
[31] Ju Y., Sun G., Chen Q., Zhang M., Zhu H., Rehman M. U. (2019) A model combining convolutional neural network and lightgbm algorithm for ultra-short-term wind power forecasting. IEEE Access. 7 28309 – 28318.
[32] Zhang D., Gong Y. (2020) The Comparison of LightGBM and XGBoost Coupling Factor Analysis and Prediagnosis of Acute Liver Failure. IEEE Access. 8 220990 - 221003.
[33] Ke G., Meng Q., Finley T., Wang T., Chen W., Ma W., Ye Q., Liu. Y. (2023) LightGBM : A Highly Efficient Gradient Boosting Decision Tree To cite this version : HAL Id : hal-03953007: 0–9.
[34] Mukherjee K., Colón Y. J. (2021) Machine learning and descriptor selection for the computational discovery of metal-organic frameworks. Mol. Simul. 47(10–11) 857–877.
[35] AlKheder S., AlOmair A. (2022) Urban traffic prediction using metrological data with fuzzy logic, long short-term memory (LSTM), and decision trees (DTs). Nat. Hazards. 111 1685–1719.
[36] Aher R. B., Khan K., Roy K. (2020) A brief introduction to quantitative structure-activity relationships as useful tools in predictive ecotoxicology. SAR QSAR Environ Res. 27–53.
[37] Calle L., Marrero-Ponce Y., Mora J. R. (2021) Molecular simulation of the (GPx)-like antioxidant activity of ebselen derivatives through machine learning techniques. Mol. Simul. 47 1402–10.
[38] Johnston A., Johnston B. F., Kennedy A. R., Florence A. J. (2008) Targeted crystallisation of novel carbamazepine solvates based on a retrospective Random Forest classification. Cryst. Eng. Comm. 10 23–5.
[39] Deepak T., Mohd Anul H., Gazi R., Prashant B., Joydip D. (2019) Comparison of Performance of Artificial Neural Network (ANN) and Random Forest (RF) in the Classification of Land Cover Zones of Urban Slum Region. LNCE. 51 225–36.
[40] Ferreira Neto, D. C., Alencar Lima, J., Sobreiro Francisco Diz de Almeida, J., Costa França, T. C., Jorge do Nascimento, C., & Figueroa Villar, J. D. (2018) New semicarbazones as gorge-spanning ligands of acetylcholinesterase and potential new drugs against Alzheimer’s disease: Synthesis, molecular modeling, NMR, and biological evaluation. J. Biomol. Struct. Dyn. 36 (15) 4099–4113.
[41] Danush S., Dutta A. (2023) Machine learning-based framework for predicting toxicity of ionic liquids. Mater Today Proc. 72 (1) 75–80.
[42] Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., & Liu, T. (2017). LightGBM: A Highly Efficient Gradient Boosting Decision Tree. Neural Information Processing Systems.
[43] Xu Y., Liaw A., Sheridan R. P., Svetnik V. (2023) Development and Evaluation of Conformal Prediction Methods for QSAR. q-bio.BM. https://doi.org/10.48550/arXiv.2304.00970.
[44] Vishwakarma G., Sonpal A., Hachmann J. (2021) Metrics for Benchmarking and Uncertainty Quantification: Quality, Applicability, and Best Practices for Machine Learning in Chemistry. Trends Chem. 3 146–56.
[45] Shahhosseini M., Hu G., Pham H. (2022) Optimizing ensemble weights and hyperparameters of machine learning models for regression problems. Machine Learning with Applications 7 100251.
[46] Noviandy T. R., Maulana A., Emran T. B., Idroes G. M., Idroes R. (2023) QSAR Classification of Beta-Secretase 1 Inhibitor Activity in Alzheimer’s Disease Using Ensemble Machine Learning Algorithms. HJAS. 1.
[47] Lusci A., Pollastri G., Baldi P. (2013) Deep architectures and deep learning in chemoinformatics: The prediction of aqueous solubility for drug-like molecules. J. Chem. Inf. Model. 53 1563–75.
[48] Rao, H., Zeng, X., Wang, Y., He, H., Zhu, F., Li, Z., & Chen, Y. (2012). Identification of DNA adduct formation of small molecules by molecular descriptors and machine learning methods. Mol. Simul. 38 (4) 259–273.
[49] Liu X, Zhu B, Dai XW, Xu ZA, Li R, Qian Y, Lu Y. P. Zhang Y., Liu Y., Zheng J. (2023) GBDT_KgluSite: An improved computational prediction model for lysine glutarylation sites based on feature fusion and GBDT classifier. BMC Genomics 24.
[50] Li N., Chen K., Bai J., Geng Z., Tang Y., Hou Y., Fan F., Ai X., Hu Y., Meng X., Wang X., Zhang Y. (2021) Tibetan medicine Duoxuekang ameliorates hypobaric hypoxia-induced brain injury in mice by restoration of cerebrovascular function. J. Ethnopharmacol. 270 113629.
[51] Hamada M., Zaqoot H. A., Sethar W. A. (2023) Using Supervised Learning Machine Approach to Predict Water Quality at Gaza’s Wastewater Treatment Plant. Environmental Science: Advances. https://doi.org/10.1039/d3va00170a.
[52] Binuya M. A. E, Engelhardt E. G., Schats W., Schmidt M. K., Steyerberg E. W. (2022) Methodological guidance for the evaluation and updating of clinical prediction models: a systematic review. BMC Med. Res. Methodol. 22 316.
[53] Ouabane M., Tabti K., Hajji H., Elbouhi M., Khaldan A., Elkamel K., Sbai A., Ajana M. A., Sekkate C. Bouachrine M. Lakhlifi T. (2023) Structure-odor relationship in pyrazines and derivatives: a physicochemical study using 3D-QSPR, HQSPR, Monte Carlo, Molecular Docking, ADME-Tox and. Arabian Journal of Chemistry 16 (11) 105207.
[54] Ouabane M., Hajji H, Belhassan A. Koubi Y., Elbouhi M., Badaoui H., Sekkate C., Lakhlifi T. (2022) RHAZES: Green and Applied Chemistry molecules in pectin gels of different concentration. RHAZES: Green and Applied Chemistry 14 15-35.
[55] Luo X., Yang X., Qiao X., Wang Y., Chen J., Wei X., Willie J. G. M. P. (2017) Development of a QSAR model for predicting aqueous reaction rate constants of organic chemicals with hydroxyl radicals. Environ Sci Process Impacts 19 350–6.
[56] Todeschini R., Ballabio D., Grisoni F. (2016) Beware of Unreliable Q2! A Comparative Study of Regression Metrics for Predictivity Assessment of QSAR Models. J. Chem. Inf. Model. 56 (10) 1905-1913.
[57] Zou G. Y. (2007) Toward Using Confidence Intervals to Compare Correlations. Psychol Methods 12 (4) 399-413.
[58] Mulaik S. A., Raju N. S., Harshman R. A. (2016) There is a time and a place for significance testing. What If There Were No Significance Tests. Classic Edition 2016: 61–106.
[59] Hajji H, En-nahli F, Aanouz I, Zaki H., Lakhlifi T., Ajana M. A., Bouachrine M. (2021) Catastrophic Collision Between Obesity and COVID-19 Have Evoked the Computational Chemistry for Research in Silico Design of New CaMKKII Inhibitors Against Obesity by Using 3D-QSAR, Molecular Docking, and ADMET. Orbital 13 (4) 316-327.
[60] Zhao D., Zhong S. (2021) Binding mechanisms of varic acid inhibitors on protein tyrosine phosphatase 1B and in silico design of the novel derivatives. Mol. Simul. 47 (9) 771–784.
[61] Wichur T., Pasieka A., Godyń J., Panek D., Góral I., Latacz G., et al. Discovery of 1-(phenylsulfonyl)-1H-indole-based multifunctional ligands targeting cholinesterases and 5-HT6 receptor with anti-aggregation properties against amyloid-beta and tau. Eur. J. Med. Chem. 225 113783.
[62] Belghalia E., Ouabane M., El Bahi S., Muzzammel H., Sbai A., Lakhlifi T., Bouachrine M. (2023) In silico research on new sulfonamide derivatives as BRD4 inhibitors targeting acute myeloid leukemia using various computational techniques including 3D-QSAR, HQSAR, molecular docking, ADME /Tox, and molecular dynamics. J. Biomol. Struct. Dyn. 0 1–19.
[63] Tabti K., Elmchichi L., Sbai A., Maghat H., Bouachrine M., Lakhlifi T. (2022) Molecular modelling of antiproliferative inhibitors based on SMILES descriptors using Monte-Carlo method, docking, MD simulations and ADME/Tox studies. Mol. Simul. 48 (17) 1575–1591.
[64] Ouabane M., Zaki K., Tabti K., Alaqarbeh M., Sbai A., Sekkate C., Bouachrine M., Lakhlifi T. (2024) Molecular toxicity of nitrobenzene derivatives to Tetrahymena pyriformis based on SMILES descriptors using Monte Carlo, Docking, and MD simulations. Comput. Biol. Med.: 169 107880.
[65] Calugi L., Sautariello G., Lenci E., Mattei M. L., Coppa C., Cini N., Contini A., Trabouchi A. (2023) Identification of a short ACE2-derived stapled peptide targeting the SARS-CoV-2 spike protein. Eur. J. Med. Chem. 249 (2023) 115118.
[66] Berenger F., Kumar A., Zhang K. Y. J., Yamanishi Y. (2021) Lean-Docking: Exploiting Ligands’ Predicted Docking Scores to Accelerate Molecular Docking. J. Chem. Inf. Model. 61 2341–52.
[67] Alnajjar R., Mostafa A., Kandeil A., Al-Karmalawy A. A. (2020) Molecular docking, molecular dynamics, and in vitro studies reveal the potential of angiotensin II receptor blockers to inhibit the COVID-19 main protease. Heliyon 6 e05641.
[68] Alnajjar R., Mohamed N., Kawafi N. (2021) Bicyclo[1.1.1]Pentane as Phenyl Substituent in Atorvastatin Drug to improve Physicochemical Properties: Drug-likeness, DFT, Pharmacokinetics, Docking, and Molecular Dynamic Simulation. J. Mol. Struct. 1230 129628.
[69] Martyna G. J., Klein M. L., Tuckerman M. (1992) Nosé-Hoover chains: The canonical ensemble via continuous dynamics. J. Chem. Phys. 97 2635–43.
[70] El-Masry R. M., Al-Karmalawy A. A., Alnajjar R., Mahmoud S. H., Mostafa A., Kadry H. H, Abou-Seri S. M., Taher A. T. (2022) Newly synthesized series of oxoindole–oxadiazole conjugates as potential anti-SARS-CoV-2 agents: in silico and in vitro studies. New J. Chem. 46 5078-5090.
[71] Mahmoud N., Piacente S., Pizza C., Burke A., Khan A. I., Hay A. J. (1996) The Anti-HIV Activity and Mechanisms of Action of Pure Compounds Isolated from Rosa damascene. Biochem. Biophys. Res. Commun. 229, 73-79.