Fahimeh Ghasemi

698 total citations
23 papers, 472 citations indexed

About

Fahimeh Ghasemi is a scholar working on Computational Theory and Mathematics, Molecular Biology and Materials Chemistry. According to data from OpenAlex, Fahimeh Ghasemi has authored 23 papers receiving a total of 472 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Computational Theory and Mathematics, 11 papers in Molecular Biology and 7 papers in Materials Chemistry. Recurrent topics in Fahimeh Ghasemi's work include Computational Drug Discovery Methods (14 papers), Synthesis and biological activity (5 papers) and Protein Structure and Dynamics (5 papers). Fahimeh Ghasemi is often cited by papers focused on Computational Drug Discovery Methods (14 papers), Synthesis and biological activity (5 papers) and Protein Structure and Dynamics (5 papers). Fahimeh Ghasemi collaborates with scholars based in Iran, Spain and Cyprus. Fahimeh Ghasemi's co-authors include Alireza Mehridehnavi, Horacio Pérez‐Sánchez, Afshin Fassihi, Alfonso Pérez, Alireza Mehri Dehnavi, Laleh Shariati, Yao Lü, Alireza Sanati, John F. Presley and Chen Liang and has published in prestigious journals such as SHILAP Revista de lepidopterología, Bioinformatics and Scientific Reports.

In The Last Decade

Fahimeh Ghasemi

18 papers receiving 461 citations

Peers

Fahimeh Ghasemi
Ryan Byrne Switzerland
Limeng Pu United States
Tomasz Arodź United States
Dan Han China
Ch. Madhu Babu India
Ruihan Yang China
Qurrat Ul Ain New Zealand
Robert Burbidge United Kingdom
Zhuyifan Ye Macao
Ryan Byrne Switzerland
Fahimeh Ghasemi
Citations per year, relative to Fahimeh Ghasemi Fahimeh Ghasemi (= 1×) peers Ryan Byrne

Countries citing papers authored by Fahimeh Ghasemi

Since Specialization
Citations

This map shows the geographic impact of Fahimeh Ghasemi's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Fahimeh Ghasemi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Fahimeh Ghasemi more than expected).

Fields of papers citing papers by Fahimeh Ghasemi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Fahimeh Ghasemi. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Fahimeh Ghasemi. The network helps show where Fahimeh Ghasemi may publish in the future.

Co-authorship network of co-authors of Fahimeh Ghasemi

This figure shows the co-authorship network connecting the top 25 collaborators of Fahimeh Ghasemi. A scholar is included among the top collaborators of Fahimeh Ghasemi based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Fahimeh Ghasemi. Fahimeh Ghasemi is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Peytam, Fariba, Hayrettin Ozan Gülcan, Somayeh Mojtabavi, et al.. (2025). Design, synthesis, and evaluation of triazolo[1,5-a]pyridines as novel and potent α‑glucosidase inhibitors. Scientific Reports. 15(1). 17813–17813. 5 indexed citations
2.
Karami, Leila, et al.. (2025). Elucidating the Inhibitory Potential of Statins Against Oncogenic c-Met Tyrosine Kinase Through Computational and Cell-based Studies. Iranian journal of pharmaceutical research. 24(1). e158845–e158845.
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Peytam, Fariba, Saereh Hosseindoost, Mohamed A. Shahba, et al.. (2025). Coumarin-Chalcone derivatives as promising antioxidant agents targeting ischemia/reperfusion injury through Nrf2 pathway activation. Bioorganic Chemistry. 164. 108790–108790.
5.
Peytam, Fariba, Hayrettin Ozan Gülcan, Somayeh Mojtabavi, et al.. (2025). Identification of novel triazolopyrimidines as potent α-glucosidase inhibitor through design, synthesis, biological evaluations, and computational analysis. Scientific Reports. 15(1). 39667–39667.
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Haririan, Ismaeil, et al.. (2024). A deep learning model based on the BERT pre-trained model to predict the antiproliferative activity of anti-cancer chemical compounds. SAR and QSAR in environmental research. 35(11). 971–992. 2 indexed citations
9.
Rasti, Reza, et al.. (2024). Deep attention network for identifying ligand-protein binding sites. Journal of Computational Science. 81. 102368–102368.
10.
Ghasemi, Fahimeh, et al.. (2024). The Application of Artificial Intelligence and Drug Repositioning for the Identification of Fibroblast Growth Factor Receptor Inhibitors: A Review. SHILAP Revista de lepidopterología. 13. 9–9. 2 indexed citations
11.
Sardari, Soroush, et al.. (2023). Identification of new potential candidates to inhibit EGF via machine learning algorithm. European Journal of Pharmacology. 963. 176176–176176. 6 indexed citations
12.
Ghasemi, Fahimeh, et al.. (2023). A GU-Net-Based Architecture Predicting Ligand–Protein-Binding Atoms. Journal of Medical Signals & Sensors. 13(1). 1–10. 5 indexed citations
13.
Fassihi, Afshin, et al.. (2023). Finding New VEGFR2 Inhibitors Using Support Vector Machine Classification Model. Journal of Shahid Sadoughi University of Medical Sciences. 1 indexed citations
14.
Moakhar, Roozbeh Siavash, Mahsa Jalali, Alireza Sanati, et al.. (2022). A Versatile Biomimic Nanotemplating Fluidic Assay for Multiplex Quantitative Monitoring of Viral Respiratory Infections and Immune Responses in Saliva and Blood. Advanced Science. 9(33). e2204246–e2204246. 40 indexed citations
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Ghasemi, Fahimeh, et al.. (2021). Docking and Qsar Studies of Some Quinazolinone Derivatives as Possible Inhibitors of Thyrosine Kinase. DergiPark (Istanbul University). 1 indexed citations
17.
Pérez‐Sánchez, Horacio, et al.. (2021). Accelerating Big Data Analysis through LASSO-Random Forest Algorithm in QSAR Studies. Bioinformatics. 38(2). 469–475. 38 indexed citations
18.
Mehridehnavi, Alireza, et al.. (2020). Protein kinase inhibitors’ classification using K-Nearest neighbor algorithm. Computational Biology and Chemistry. 86. 107269–107269. 43 indexed citations
19.
Ghasemi, Fahimeh, Alireza Mehridehnavi, Alfonso Pérez, & Horacio Pérez‐Sánchez. (2018). Neural network and deep-learning algorithms used in QSAR studies: merits and drawbacks. Drug Discovery Today. 23(10). 1784–1790. 155 indexed citations
20.
Ghasemi, Fahimeh, Alireza Mehridehnavi, Afshin Fassihi, & Horacio Pérez‐Sánchez. (2017). Deep neural network in QSAR studies using deep belief network. Applied Soft Computing. 62. 251–258. 77 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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