QSAR classification model for diverse series of antifungal agents based on binary coyote optimization algorithm-Reference-Cited by-同舟云学术

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QSAR classification model for diverse series of antifungal agents based on binary coyote optimization algorithm

Published:2023-04-03 Issue:4 Volume:34 Page:285-298
ISSN:1062-936X
Container-title:SAR and QSAR in Environmental Research
language:en
Short-container-title:SAR and QSAR in Environmental Research

Author:

Al-Fakih A.M.¹²^ORCID,Qasim M.K.³,Algamal Z.Y.⁴⁵^ORCID,Alharthi A.M.⁶^ORCID,Zainal-Abidin M.H.¹

Affiliation:

1. Department of Chemistry, Faculty of Science, Universiti Teknologi Malaysia, Johor, Malaysia

2. Department of Chemistry, Faculty of Science, Sana’a University, Sana’a, Yemen

3. Department of General Science, University of Mosul, Mosul, Iraq

4. Department of Statistics and Informatics, University of Mosul, Mosul, Iraq

5. College of Engineering, University of Warith Al-Anbiyaa, Karbala, Iraq

6. Department of Mathematics, Turabah University College, Taif University, Taif, Saudi Arabia

Funder

funding

Publisher

Informa UK Limited

Subject

Drug Discovery,Molecular Medicine,General Medicine,Bioengineering

Link

https://www.tandfonline.com/doi/pdf/10.1080/1062936X.2023.2208374

Reference40 articles.

1. Synthesis, antimicrobial activity and QSAR studies of 2,5-disubstituted benzoxazoles

2. Membrane Transporters and Antifungal Drug Resistance

3. QSAR classification model for diverse series of antimicrobial agents using classification tree configured by modified particle swarm optimization

4. QSAR model for predicting the fungicidal action of 1,2,4-triazole derivatives against Candida albicans

5. QuBiLs-MAS method in early drug discovery and rational drug identification of antifungal agents

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1. Overcoming the Challenges in Topical Delivery of Antifungal Agents for Skin Fungal Infections;Current Drug Therapy;2024-08-02

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