Michael Fernández

6.8k total citations · 3 hit papers
123 papers, 5.4k citations indexed

About

Michael Fernández is a scholar working on Molecular Biology, Computational Theory and Mathematics and Materials Chemistry. According to data from OpenAlex, Michael Fernández has authored 123 papers receiving a total of 5.4k indexed citations (citations by other indexed papers that have themselves been cited), including 62 papers in Molecular Biology, 40 papers in Computational Theory and Mathematics and 24 papers in Materials Chemistry. Recurrent topics in Michael Fernández's work include Computational Drug Discovery Methods (39 papers), Machine Learning in Materials Science (15 papers) and Protein Structure and Dynamics (13 papers). Michael Fernández is often cited by papers focused on Computational Drug Discovery Methods (39 papers), Machine Learning in Materials Science (15 papers) and Protein Structure and Dynamics (13 papers). Michael Fernández collaborates with scholars based in Cuba, United States and Chile. Michael Fernández's co-authors include Julio Caballero, Tom K. Woo, Amanda S. Barnard, Artem Cherkasov, Leyden Fernández, Thomas D. Daff, Peter G. Boyd, Mohammad Zein Aghaji, Alain Tundidor‐Camba and Reynaldo Villalonga and has published in prestigious journals such as Cell, Nucleic Acids Research and Neuron.

In The Last Decade

Michael Fernández

121 papers receiving 5.3k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

The value of antimicrobial peptides in the age of resistance

2020 806 citations Michael Fernández, Artem Cherkasov et al. profile →
Artificial intelligence–enabled virtual screening of ultra-large chemical libraries with deep docking

2022 218 citations Michael Fernández, Fuqiang Ban et al. profile →
The transformational role of GPU computing and deep learning in drug discovery

2022 147 citations Mohit Pandey, Michael Fernández et al. profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Michael Fernández Cuba	37	2.7k	1.3k	1.2k	644	574	123	5.4k
Zhe Wang China	42	4.9k 1.8×	2.5k 1.9×	1.4k 1.1×	737 1.1×	1.1k 1.9×	223	8.8k
Chao Lü China	32	2.3k 0.9×	611 0.5×	810 0.7×	365 0.6×	807 1.4×	109	5.4k
Chitrangada Acharya India	17	3.6k 1.3×	790 0.6×	930 0.8×	169 0.3×	774 1.3×	21	7.0k
Peng Zhou China	45	3.9k 1.4×	921 0.7×	338 0.3×	172 0.3×	337 0.6×	315	6.6k
C. Ramakrishnan India	28	4.6k 1.7×	477 0.4×	1.7k 1.4×	201 0.3×	693 1.2×	125	6.4k
Yu Kang China	54	3.1k 1.2×	1.3k 1.0×	2.0k 1.7×	46 0.1×	448 0.8×	314	9.2k
Chu Wang China	43	6.4k 2.4×	602 0.5×	1.3k 1.1×	89 0.1×	2.1k 3.6×	245	9.8k
Ian Collins United Kingdom	49	4.0k 1.5×	531 0.4×	338 0.3×	90 0.1×	1.8k 3.2×	192	8.6k
Jiřı́ Damborský Czechia	58	9.5k 3.6×	801 0.6×	2.6k 2.2×	61 0.1×	826 1.4×	328	13.7k
Feng Ding United States	58	6.2k 2.3×	571 0.4×	2.6k 2.2×	80 0.1×	454 0.8×	255	10.7k

Countries citing papers authored by Michael Fernández

Since Specialization

Citations

This map shows the geographic impact of Michael Fernández'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 Michael Fernández with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michael Fernández more than expected).

Fields of papers citing papers by Michael Fernández

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Michael Fernández. 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 Michael Fernández. The network helps show where Michael Fernández may publish in the future.

Co-authorship network of co-authors of Michael Fernández

This figure shows the co-authorship network connecting the top 25 collaborators of Michael Fernández. A scholar is included among the top collaborators of Michael Fernández 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 Michael Fernández. Michael Fernández is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Pandey, Mohit, et al.. (2022). Ligand Binding Prediction Using Protein Structure Graphs and Residual Graph Attention Networks. Molecules. 27(16). 5114–5114. 13 indexed citations

Fernández, Michael, et al.. (2021). Machine-Learning Digital Twin of Overlay Metal Deposition for Distortion Control of Panel Structures. IFAC-PapersOnLine. 54(1). 767–772. 2 indexed citations

Morgan, Joshua T., et al.. (2020). Determining the dielectric constant of injection-molded polymer-matrix nanocomposites filled with barium titanate. MRS Communications. 10(4). 587–593. 7 indexed citations

Fernández, Michael, et al.. (2019). DeepCOP: deep learning-based approach to predict gene regulating effects of small molecules. Bioinformatics. 36(3). 813–818. 19 indexed citations

Fernández, Michael, Fuqiang Ban, Carl F. Perez, et al.. (2019). Quantitative Structure–Price Relationship (QS$R) Modeling and the Development of Economically Feasible Drug Discovery Projects. Journal of Chemical Information and Modeling. 59(4). 1306–1313. 3 indexed citations

Fernández, Michael, Fuqiang Ban, Michael Hsing, et al.. (2018). Toxic Colors: The Use of Deep Learning for Predicting Toxicity of Compounds Merely from Their Graphic Images. Journal of Chemical Information and Modeling. 58(8). 1533–1543. 93 indexed citations

Dalal, Kush, Hélène Morin, Fuqiang Ban, et al.. (2018). Small molecule-induced degradation of the full length and V7 truncated variant forms of human androgen receptor. European Journal of Medicinal Chemistry. 157. 1164–1173. 14 indexed citations

Fernández, Michael, et al.. (2018). A Computational Assessment of the Robustness of Cancer Treatments with Respect to Immune Response Strength, Tumor Size and Resistance. 7(1). 1–26. 6 indexed citations

Fernández, Michael, Ante Bilić, & Amanda S. Barnard. (2017). Machine learning and genetic algorithm prediction of energy differences between electronic calculations of graphene nanoflakes. Nanotechnology. 28(38). 38LT03–38LT03. 25 indexed citations

10.

Fernández, Michael, Héctor Barrón, & Amanda S. Barnard. (2017). Artificial neural network analysis of the catalytic efficiency of platinum nanoparticles. RSC Advances. 7(77). 48962–48971. 29 indexed citations

11.

Fernández, Michael, Michael Breedon, Ivan Cole, & Amanda S. Barnard. (2016). Modeling corrosion inhibition efficacy of small organic molecules as non-toxic chromate alternatives using comparative molecular surface analysis (CoMSA). Chemosphere. 160. 80–88. 22 indexed citations

12.

Fernández, Michael, Hongqing Shi, & Amanda S. Barnard. (2016). Geometrical features can predict electronic properties of graphene nanoflakes. Carbon. 103. 142–150. 39 indexed citations

13.

Aghaji, Mohammad Zein, Michael Fernández, Peter G. Boyd, Thomas D. Daff, & Tom K. Woo. (2016). Quantitative Structure–Property Relationship Models for Recognizing Metal Organic Frameworks (MOFs) with High CO₂ Working Capacity and CO₂/CH₄ Selectivity for Methane Purification. European Journal of Inorganic Chemistry. 2016(27). 4505–4511. 77 indexed citations

14.

Rossi, Daniela, Mariangela Urbano, Raffaella Gaggeri, et al.. (2011). Identification of a potent and selective σ1 receptor agonist potentiating NGF-induced neurite outgrowth in PC12 cells. Bioorganic & Medicinal Chemistry. 19(21). 6210–6224. 43 indexed citations

15.

Caballero, Julio, et al.. (2010). Quantitative Structure–Activity Relationship of Organosulphur Compounds as Soybean 15‐Lipoxygenase Inhibitors Using CoMFA and CoMSIA. Chemical Biology & Drug Design. 76(6). 511–517. 8 indexed citations

16.

Duchowicz, Pablo R., et al.. (2007). QSAR analysis for heterocyclic antifungals. Bioorganic & Medicinal Chemistry. 15(7). 2680–2689. 39 indexed citations

17.

Han, Xin, Guillermo Garcia‐Manero, Timothy J. McDonnell, et al.. (2006). HDM4 (HDMX) is widely expressed in adult pre-B acute lymphoblastic leukemia and is a potential therapeutic target. Modern Pathology. 20(1). 54–62. 34 indexed citations

18.

Fernández, Michael, Julio Caballero, Aliuska Morales Helguera, Eduardo A. Castro, & Maykel Pérez González. (2005). Quantitative structure–activity relationship to predict differential inhibition of aldose reductase by flavonoid compounds. Bioorganic & Medicinal Chemistry. 13(9). 3269–3277. 75 indexed citations

19.

Villalonga, Reynaldo, Michael Fernández, Alex Fragoso, et al.. (2003). Transglutaminase‐catalyzed synthesis of trypsin–cyclodextrin conjugates: Kinetics and stability properties. Biotechnology and Bioengineering. 81(6). 732–737. 54 indexed citations

20.

Fernández, Michael, María L. Villalonga, Julio Caballero, et al.. (2003). Effects of β‐cyclodextrin–dextran polymer on stability properties of trypsin. Biotechnology and Bioengineering. 83(6). 743–747. 17 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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