How to cite this paper
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.
Refrences
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[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.
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[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.
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[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.
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[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.
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[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.
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