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SAR and QSAR in Environmental Research Volume 30, 2019 - Issue 8
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Articles

Development and rigorous validation of antimalarial predictive models using machine learning approaches

DanishuddinSchool of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi, IndiaCorrespondence[email protected]
,
G. MadhukarSchool of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi, India
,
M.Z. MalikSchool of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi, India
&
N. SubbaraoSchool of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi, India
Pages 543-560 | Received 18 Apr 2019, Accepted 20 Jun 2019, Published online: 22 Jul 2019

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S. Nandi, P. Kumar, S.A. Amin, T. Jha & S. Gayen. (2021) First molecular modelling report on tri-substituted pyrazolines as phosphodiesterase 5 (PDE5) inhibitors through classical and machine learning based multi-QSAR analysis. SAR and QSAR in Environmental Research 32:11, pages 917-939.
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Q. Liu, J. Deng & M. Liu. (2020) Classification models for predicting the antimalarial activity against Plasmodium falciparum. SAR and QSAR in Environmental Research 31:4, pages 313-324.
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J. Devillers & H. Devillers. (2019) Toxicity profiling and prioritization of plant-derived antimalarial agents. SAR and QSAR in Environmental Research 30:11, pages 801-824.
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Sunghwan Kim & Evan E. Bolton. 2024. Open Access Databases and Datasets for Drug Discovery. Open Access Databases and Datasets for Drug Discovery 39 66 .
Adam Richard-Bollans, Conal Aitken, Alexandre Antonelli, Cássia Bitencourt, David Goyder, Eve Lucas, Ian Ondo, Oscar A. Pérez-Escobar, Samuel Pironon, James E. Richardson, David Russell, Daniele Silvestro, Colin W. Wright & Melanie-Jayne R. Howes. (2023) Machine learning enhances prediction of plants as potential sources of antimalarials. Frontiers in Plant Science 14.
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Osondu Everestus Oguike, Chikodili Helen Ugwuishiwu, Caroline Ngozi Asogwa, Charles Okeke Nnadi, Wilfred Ofem Obonga & Anthony Amaechi Attama. (2022) Systematic review on the application of machine learning to quantitative structure–activity relationship modeling against Plasmodium falciparum. Molecular Diversity 26:6, pages 3447-3462.
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Thanh-Hoang Nguyen-Vo, Quang H. Trinh, Loc Nguyen, Trang T. T. Do, Matthew Chin Heng Chua & Binh P. Nguyen. (2021) Predicting Antimalarial Activity in Natural Products Using Pretrained Bidirectional Encoder Representations from Transformers. Journal of Chemical Information and Modeling 62:21, pages 5050-5058.
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Lucas A. Machado, Eduardo Krempser & Ana Carolina Ramos Guimarães. (2022) A machine learning-based virtual screening for natural compounds capable of inhibiting the HIV-1 integrase. Frontiers in Drug Discovery 2.
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Haseeb Mughal, Elise C. Bell, Khadija Mughal, Emily R. Derbyshire & Joel S. Freundlich. (2022) Random Forest Model Predictions Afford Dual-Stage Antimalarial Agents. ACS Infectious Diseases 8:8, pages 1553-1562.
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Manuela Souza Leite, Anderson Alles de Jesus, Paulo Jardel Leite Araujo & Brunno Ferreira dos Santos. 2022. Contemporary Chemical Approaches for Green and Sustainable Drugs. Contemporary Chemical Approaches for Green and Sustainable Drugs 69 88 .
Medard Edmund Mswahili, Gati Lother Martin, Jiyoung Woo, Guang J. Choi & Young-Seob Jeong. (2021) Antimalarial Drug Predictions Using Molecular Descriptors and Machine Learning against Plasmodium Falciparum. Biomolecules 11:12, pages 1750.
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Asma Sellami, Matthieu Montes & Nathalie Lagarde. (2021) Predicting Potential Endocrine Disrupting Chemicals Binding to Estrogen Receptor α (ERα) Using a Pipeline Combining Structure-Based and Ligand-Based in Silico Methods. International Journal of Molecular Sciences 22:6, pages 2846.
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Ao Ding, Yuan Zhang, Lei Zhu, Yanping Du & Luping Ma. (2020) Recognition method research on rough handling of express parcels based on acceleration features and CNN. Measurement 163, pages 107942.
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