Daniel J. Diaz

1.6k total citations · 1 hit paper
13 papers, 1.1k citations indexed

About

Daniel J. Diaz is a scholar working on Molecular Biology, Organic Chemistry and Biomedical Engineering. According to data from OpenAlex, Daniel J. Diaz has authored 13 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Molecular Biology, 2 papers in Organic Chemistry and 2 papers in Biomedical Engineering. Recurrent topics in Daniel J. Diaz's work include RNA and protein synthesis mechanisms (6 papers), Protein Structure and Dynamics (4 papers) and Machine Learning in Bioinformatics (3 papers). Daniel J. Diaz is often cited by papers focused on RNA and protein synthesis mechanisms (6 papers), Protein Structure and Dynamics (4 papers) and Machine Learning in Bioinformatics (3 papers). Daniel J. Diaz collaborates with scholars based in United States, Switzerland and France. Daniel J. Diaz's co-authors include Andrew D. Ellington, Raghav Shroff, Yan Zhang, Daniel J. Acosta, Hal S. Alper, Wantae Kim, Hannah Cole, Congzhi Zhu, Hongyuan Lu and Nathaniel A. Lynd and has published in prestigious journals such as Nature, Journal of the American Chemical Society and Nature Communications.

In The Last Decade

Daniel J. Diaz

13 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.

Machine learning-aided engineering of hydrolases for PET depolymerization

2022 767 citations Hongyuan Lu, Daniel J. Diaz et al. Nature profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Daniel J. Diaz United States	10	443	431	365	223	177	13	1.1k
Wantae Kim United States	5	439 1.0×	289 0.7×	354 1.0×	221 1.0×	153 0.9×	15	896
Hongyuan Lu United States	4	439 1.0×	199 0.5×	355 1.0×	223 1.0×	133 0.8×	5	800
Daniel J. Acosta United States	5	450 1.0×	239 0.6×	369 1.0×	228 1.0×	140 0.8×	13	876
Dominique Böttcher Germany	20	385 0.9×	919 2.1×	327 0.9×	157 0.7×	311 1.8×	49	1.5k
Isabel Pardo Spain	14	445 1.0×	289 0.7×	372 1.0×	213 1.0×	269 1.5×	19	1.2k
Lixin Ma China	13	168 0.4×	405 0.9×	135 0.4×	77 0.3×	152 0.9×	20	755
Hyeoncheol Francis Son South Korea	15	1.2k 2.6×	357 0.8×	1.0k 2.9×	465 2.1×	438 2.5×	33	1.7k
Hye-Young Sagong South Korea	13	1.1k 2.5×	279 0.6×	988 2.7×	443 2.0×	370 2.1×	19	1.5k
Congzhi Zhu United States	12	437 1.0×	218 0.5×	392 1.1×	221 1.0×	178 1.0×	20	1.3k
Ya‐Shan Cheng China	14	330 0.7×	393 0.9×	294 0.8×	138 0.6×	371 2.1×	21	949

Countries citing papers authored by Daniel J. Diaz

Since Specialization

Citations

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

Fields of papers citing papers by Daniel J. Diaz

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Daniel J. Diaz. 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 Daniel J. Diaz. The network helps show where Daniel J. Diaz may publish in the future.

Co-authorship network of co-authors of Daniel J. Diaz

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

All Works

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13 of 13 papers shown

1.

Vieira, Luiz Ângelo, et al.. (2025). A systematic evaluation of the language-of-viral-escape model using multiple machine learning frameworks. Journal of The Royal Society Interface. 22(225). 20240598–20240598. 2 indexed citations

2.

d’Oelsnitz, Simon, Daniel J. Diaz, Wantae Kim, et al.. (2024). Biosensor and machine learning-aided engineering of an amaryllidaceae enzyme. Nature Communications. 15(1). 2084–2084. 30 indexed citations

3.

Diaz, Daniel J., et al.. (2024). Machine Learning Guided Rational Design of a Non‐Heme Iron‐Based Lysine Dioxygenase Improves its Total Turnover Number. ChemBioChem. 25(24). e202400495–e202400495. 1 indexed citations

4.

Liu, Yi, Daniel J. Diaz, Andrew D. Ellington, et al.. (2024). Asymmetric Synthesis of α-Chloroamides via Photoenzymatic Hydroalkylation of Olefins. Journal of the American Chemical Society. 146(11). 7191–7197. 22 indexed citations

5.

Diaz, Daniel J., Chengyue Gong, J.M. Wells, et al.. (2024). Stability Oracle: a structure-based graph-transformer framework for identifying stabilizing mutations. Nature Communications. 15(1). 6170–6170. 28 indexed citations

6.

Diaz, Daniel J., et al.. (2023). Using machine learning to predict the effects and consequences of mutations in proteins. Current Opinion in Structural Biology. 78. 102518–102518. 32 indexed citations

7.

d’Oelsnitz, Simon, et al.. (2023). Pushing Differential Sensing Further: The Next Steps in Design and Analysis of Bio‐Inspired Cross‐Reactive Arrays. Analysis & Sensing. 3(2). 11 indexed citations

8.

Diaz, Daniel J., et al.. (2023). Two sequence- and two structure-based ML models have learned different aspects of protein biochemistry. Scientific Reports. 13(1). 13280–13280. 6 indexed citations

9.

Lu, Hongyuan, Daniel J. Diaz, Congzhi Zhu, et al.. (2022). Machine learning-aided engineering of hydrolases for PET depolymerization. Nature. 604(7907). 662–667. 767 indexed citations breakdown →

10.

d’Oelsnitz, Simon, et al.. (2022). GroovDB: A Database of Ligand-Inducible Transcription Factors. ACS Synthetic Biology. 11(10). 3534–3537. 19 indexed citations

11.

Diaz, Daniel J., et al.. (2021). Learning the local landscape of protein structures with convolutional neural networks. Journal of Biological Physics. 47(4). 435–454. 16 indexed citations

12.

Paik, Inyup, Raghav Shroff, Daniel J. Diaz, et al.. (2021). Improved Bst DNA Polymerase Variants Derived via a Machine Learning Approach. Biochemistry. 62(2). 410–418. 34 indexed citations

13.

Shroff, Raghav, Daniel J. Diaz, Barrett R. Morrow, et al.. (2020). Discovery of Novel Gain-of-Function Mutations Guided by Structure-Based Deep Learning. ACS Synthetic Biology. 9(11). 2927–2935. 97 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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