Avid M. Afzal

586 citations
20 papers · 352 indexed · h-index 12
Topics
Computational Drug Discovery Methods (15 papers)Protein Structure and Dynamics (4 papers)Metabolomics and Mass Spectrometry Studies (3 papers)

In The Last Decade

Avid M. Afzal

20 papers receiving 346 citations

Peers

Avid M. Afzal
Comparison fields: 5 of 78
  • Computational Theory and Mathematics 247
  • Molecular Biology 211
  • Materials Chemistry 59
  • Pharmacology 56
  • Biophysics 33
Replace Shuaishi Gao with:
Shuaishi Gao China
Noé Sturm Sweden
Bo‐Han Su Taiwan
Jennifer Hemmerich Austria
Rishi R. Gupta United States
Tyler Peryea United States
Swetlana Dracheva United States
Jadson Castro Gertrudes Brazil
Arvid Berg Sweden
Tevfik Kizilören United Kingdom
Avid M. Afzal relative to Shuaishi Gao China Shuaishi Gao's profile →
Citations per field
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Citations per year

Countries citing papers authored by Avid M. Afzal

Since Specialization
Citations

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

Fields of papers citing papers by Avid M. Afzal

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Avid M. Afzal. 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 Avid M. Afzal. The network helps show where Avid M. Afzal may publish in the future.

Co-authorship network of co-authors of Avid M. Afzal

This figure shows the co-authorship network connecting the top 25 collaborators of Avid M. Afzal. A scholar is included among the top collaborators of Avid M. Afzal 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 Avid M. Afzal. Avid M. Afzal 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
#WorkIndexed citations
1 4
2 6
3 14
4 25
5 14
6 17
7 9
8 29
9 12
10 3
11 7
12 14
13 3
14 23
15
Maximizing Gain in HTS Screening Using Conformal Prediction
1
16 4
17 91
18 26
19 12
20 38

About Avid M. Afzal

Avid M. Afzal is a scholar working on Computational Theory and Mathematics, Toxicology and Statistics and Probability, having authored 20 papers that have together received 352 indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (15 papers), Protein Structure and Dynamics (4 papers) and Metabolomics and Mass Spectrometry Studies (3 papers). The work is most often cited by research in Computational Theory and Mathematics (247 citations), Pharmacology (56 citations) and Biophysics (33 citations). Avid M. Afzal has collaborated with scholars based in United Kingdom, Sweden and Australia. Frequent co-authors include Andreas Bender, Lewis Mervin, Ola Engkvist, Richard P. Lewis, Ian P. Barrett, Robert C. Glen, Fredrik Svensson, Elizaveta Semenova, Stanley E. Lazic and Hamse Y. Mussa. Their work appears in journals such as Bioinformatics, Proteins Structure Function and Bioinformatics and Frontiers in Pharmacology.

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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