Ana C. Puhl

2.6k total citations · 1 hit paper
48 papers, 1.0k citations indexed

About

Ana C. Puhl is a scholar working on Molecular Biology, Infectious Diseases and Computational Theory and Mathematics. According to data from OpenAlex, Ana C. Puhl has authored 48 papers receiving a total of 1.0k indexed citations (citations by other indexed papers that have themselves been cited), including 23 papers in Molecular Biology, 14 papers in Infectious Diseases and 14 papers in Computational Theory and Mathematics. Recurrent topics in Ana C. Puhl's work include Computational Drug Discovery Methods (14 papers), SARS-CoV-2 and COVID-19 Research (8 papers) and Lysosomal Storage Disorders Research (5 papers). Ana C. Puhl is often cited by papers focused on Computational Drug Discovery Methods (14 papers), SARS-CoV-2 and COVID-19 Research (8 papers) and Lysosomal Storage Disorders Research (5 papers). Ana C. Puhl collaborates with scholars based in United States, Brazil and Russia. Ana C. Puhl's co-authors include Sean Ekins, Thomas R. Lane, Kimberley M. Zorn, Jennifer J. Klein, Anthony J. Hickey, Daniel P. Russo, Alex M. Clark, Fabio Urbina, Daniel H. Foil and Igor Polikarpov and has published in prestigious journals such as Nature Materials, SHILAP Revista de lepidopterología and PLoS Pathogens.

In The Last Decade

Ana C. Puhl

47 papers receiving 1.0k 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.

Exploiting machine learning for end-to-end drug discovery and development

2019 352 citations Sean Ekins, Ana C. Puhl et al. Nature Materials profile →

Peers

Ana C. Puhl

MB

CTM

MC

ID

PHARM

Thomas R. Lane United States

Aman Chandra Kaushik China

Konda Mani Saravanan India

Kunqian Yu China

Kimberley M. Zorn United States

Yifei Wang China

Yanjing Wang China

Farah Anjum Saudi Arabia

Karthikeyan Muthusamy India

Sayan Mondal United States

Thomas R. Lane United States

Ana C. Puhl

549 ×1.1

MB

572 ×1.5

CTM

227 ×1.4

MC

285 ×2.0

ID

94 ×1.1

PHARM

Citations per year, relative to Ana C. Puhl Ana C. Puhl (= 1×) peers Thomas R. Lane

Countries citing papers authored by Ana C. Puhl

Since Specialization

Citations

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

Fields of papers citing papers by Ana C. Puhl

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ana C. Puhl. 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 Ana C. Puhl. The network helps show where Ana C. Puhl may publish in the future.

Co-authorship network of co-authors of Ana C. Puhl

This figure shows the co-authorship network connecting the top 25 collaborators of Ana C. Puhl. A scholar is included among the top collaborators of Ana C. Puhl 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 Ana C. Puhl. Ana C. Puhl 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

1.

Puhl, Ana C., et al.. (2024). The Goldilocks paradigm: comparing classical machine learning, large language models, and few-shot learning for drug discovery applications. Communications Chemistry. 7(1). 134–134. 13 indexed citations

2.

Lane, Thomas R., et al.. (2024). Repurposing lapatinib as a triple antagonist of chemokine receptors 3, 4, and 5. Molecular Pharmacology. 107(1). 100010–100010. 1 indexed citations

3.

Puhl, Ana C., et al.. (2024). Machine learning-aided search for ligands of P2Y6 and other P2Y receptors. Purinergic Signalling. 20(6). 617–627. 5 indexed citations

4.

Puhl, Ana C., Thomas R. Lane, & Sean Ekins. (2023). Learning from COVID-19: How drug hunters can prepare for the next pandemic. Drug Discovery Today. 28(10). 103723–103723. 3 indexed citations

5.

Lane, Thomas R., et al.. (2023). Transporter Inhibition Profile for the Antivirals Tilorone, Quinacrine and Pyronaridine. ACS Omega. 8(13). 12532–12537. 2 indexed citations

6.

Kazakova, Elena, Thomas R. Lane, Ana C. Puhl, et al.. (2023). 1-Sulfonyl-3-amino-1H-1,2,4-triazoles as Yellow Fever Virus Inhibitors: Synthesis and Structure–Activity Relationship. ACS Omega. 8(45). 42951–42965. 4 indexed citations

7.

Puhl, Ana C., et al.. (2023). Developing Treatments for Rare Diseases on a Shoestring. PubMed. 2(5). 353–359.

8.

Puhl, Ana C., R.S. Fernandes, André S. Godoy, et al.. (2023). The protein disulfide isomerase inhibitor 3-methyltoxoflavin inhibits Chikungunya virus. Bioorganic & Medicinal Chemistry. 83. 117239–117239. 5 indexed citations

9.

Shu, Bo, Thiam‐Seng Ng, Bruno Segovia-Chumbez, et al.. (2023). Structure and neutralization mechanism of a human antibody targeting a complex Epitope on Zika virus. PLoS Pathogens. 19(1). e1010814–e1010814. 4 indexed citations

10.

Lane, Thomas R., et al.. (2023). Validation of Acetylcholinesterase Inhibition Machine Learning Models for Multiple Species. Chemical Research in Toxicology. 36(2). 188–201. 16 indexed citations

11.

Puhl, Ana C., André S. Godoy, G.D. Noske, et al.. (2023). Discovery of PL^pro and M^pro Inhibitors for SARS-CoV-2. ACS Omega. 8(25). 22603–22612. 11 indexed citations

12.

Puhl, Ana C. & Sean Ekins. (2022). Advancing the Research and Development of Enzyme Replacement Therapies for Lysosomal Storage Diseases. PubMed. 1(2). 156–162. 3 indexed citations

13.

Puhl, Ana C., Zhan‐Guo Gao, Kenneth A. Jacobson, & Sean Ekins. (2022). Machine Learning for Discovery of New ADORA Modulators. Frontiers in Pharmacology. 13. 920643–920643. 8 indexed citations

14.

Urbina, Fabio, Ana C. Puhl, & Sean Ekins. (2021). Recent advances in drug repurposing using machine learning. Current Opinion in Chemical Biology. 65. 74–84. 45 indexed citations

15.

Ekins, Sean, Melina Mottin, Bruno J. Neves, et al.. (2020). Déjà vu: Stimulating open drug discovery for SARS-CoV-2. Drug Discovery Today. 25(5). 928–941. 66 indexed citations

16.

Ekins, Sean, Ana C. Puhl, Kimberley M. Zorn, et al.. (2019). Exploiting machine learning for end-to-end drug discovery and development. Nature Materials. 18(5). 435–441. 352 indexed citations breakdown →

17.

Souza, Paulo C. T., Ana C. Puhl, Leandro Martı́nez, et al.. (2014). Identification of a New Hormone-Binding Site on the Surface of Thyroid Hormone Receptor. Molecular Endocrinology. 28(4). 534–545. 31 indexed citations

18.

Pereira‐Filho, Edenir Rodrigues, Maria Fátima das Graças Fernandes da Silva, João Batista Fernandes, et al.. (2012). Poly-ε-caprolactone nanoparticles loaded with hydrocortisone: preparation using factorial design and evaluation. SHILAP Revista de lepidopterología. 4(2). 54–76. 2 indexed citations

19.

Puhl, Ana C., Igor Polikarpov, María de Fátima, et al.. (2012). Preparation and characterization of polymeric nanoparticles loaded with the flavonoid luteolin, by using factorial design. International Journal of Phytomedicine. 3(4). 683–698. 31 indexed citations

20.

Puhl, Ana C., Amanda Bernardes, Rodrigo L. Silveira, et al.. (2012). Mode of Peroxisome Proliferator-Activated Receptor γ Activation by Luteolin. Molecular Pharmacology. 81(6). 788–799. 81 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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