Daniel Kirshner

788 total citations
19 papers, 548 citations indexed

About

Daniel Kirshner is a scholar working on Molecular Biology, Computational Theory and Mathematics and Electrical and Electronic Engineering. According to data from OpenAlex, Daniel Kirshner has authored 19 papers receiving a total of 548 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Molecular Biology, 4 papers in Computational Theory and Mathematics and 4 papers in Electrical and Electronic Engineering. Recurrent topics in Daniel Kirshner's work include Protein Structure and Dynamics (6 papers), Computational Drug Discovery Methods (4 papers) and Smart Grid Energy Management (3 papers). Daniel Kirshner is often cited by papers focused on Protein Structure and Dynamics (6 papers), Computational Drug Discovery Methods (4 papers) and Smart Grid Energy Management (3 papers). Daniel Kirshner collaborates with scholars based in United States. Daniel Kirshner's co-authors include Felice C. Lightstone, Jerome P. Nilmeier, Sergio Wong, Timothy S. Carpenter, Edmond Y. Lau, Antti Talvitie, Kimmen Sjölander, Nandini Krishnamurthy, Jacob Glanville and N. Krishnamurthy and has published in prestigious journals such as Nucleic Acids Research, PLoS ONE and Biophysical Journal.

In The Last Decade

Daniel Kirshner

18 papers receiving 517 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Daniel Kirshner United States	11	255	128	109	60	44	19	548
Nidhi Nidhi India	7	197 0.8×	21 0.2×	231 2.1×	10 0.2×	21 0.5×	38	479
Joaquim Mendes Portugal	13	357 1.4×	39 0.3×	29 0.3×	27 0.5×	9 0.2×	20	484
Xiaoling Li China	8	196 0.8×	20 0.2×	9 0.1×	35 0.6×	80 1.8×	21	432
Yiliang Li China	11	292 1.1×	29 0.2×	32 0.3×	16 0.3×	27 0.6×	34	450
Ignacio Ponzoni Argentina	16	260 1.0×	22 0.2×	301 2.8×	54 0.9×	47 1.1×	54	687
Qi Huang China	15	152 0.6×	13 0.1×	242 2.2×	27 0.5×	45 1.0×	30	615
Stanislav Mazurenko Czechia	14	653 2.6×	34 0.3×	83 0.8×	14 0.2×	41 0.9×	39	917
Tanawut Tantimongcolwat Thailand	15	325 1.3×	47 0.4×	88 0.8×	5 0.1×	110 2.5×	41	633
Antonio de la Vega de León Spain	16	220 0.9×	61 0.5×	176 1.6×	5 0.1×	141 3.2×	44	756
Parviz Shahbazikhah Iran	8	78 0.3×	130 1.0×	86 0.8×	13 0.2×	31 0.7×	11	372

Countries citing papers authored by Daniel Kirshner

Since Specialization

Citations

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

Fields of papers citing papers by Daniel Kirshner

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Daniel Kirshner

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

All Works

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19 of 19 papers shown

Song, Lin Frank, Jun Pei, Edmond Y. Lau, et al.. (2025). How KRAS Mutations Impair Intrinsic GTP Hydrolysis: Experimental and Computational Investigations. Journal of Chemical Information and Modeling. 65(23). 12822–12833.

Kirshner, Daniel, Brian J. Bennion, Yue Yang, et al.. (2023). Clustering Protein Binding Pockets and Identifying Potential Drug Interactions: A Novel Ligand-Based Featurization Method. Journal of Chemical Information and Modeling. 63(21). 6655–6666. 3 indexed citations

Bennett, William F., et al.. (2023). Advances in Computational Approaches for Estimating Passive Permeability in Drug Discovery. Membranes. 13(11). 851–851. 4 indexed citations

Ahn, Dong H., Xiaohua Zhang, Jeffrey E. Mast, et al.. (2022). Scalable Composition and Analysis Techniques for Massive Scientific Workflows. OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information). 32–43. 3 indexed citations

Zemła, Adam, Jonathan Allen, Daniel Kirshner, & Felice C. Lightstone. (2022). PDBspheres: a method for finding 3D similarities in local regions in proteins. NAR Genomics and Bioinformatics. 4(4). lqac078–lqac078. 7 indexed citations

Carpenter, Timothy S., Daniel Kirshner, Edmond Y. Lau, et al.. (2014). A Method to Predict Blood-Brain Barrier Permeability of Drug-Like Compounds Using Molecular Dynamics Simulations. Biophysical Journal. 107(3). 630–641. 225 indexed citations

Nilmeier, Jerome P., Daniel Kirshner, Sergio Wong, & Felice C. Lightstone. (2013). Rapid Catalytic Template Searching as an Enzyme Function Prediction Procedure. PLoS ONE. 8(5). e62535–e62535. 25 indexed citations

Kirshner, Daniel, Jerome P. Nilmeier, & Felice C. Lightstone. (2013). Catalytic site identification—a web server to identify catalytic site structural matches throughout PDB. Nucleic Acids Research. 41(W1). W256–W265. 20 indexed citations

Cleves, Ann E., et al.. (2009). Physical Binding Pocket Induction for Affinity Prediction. Journal of Medicinal Chemistry. 52(19). 6107–6125. 16 indexed citations

10.

Glanville, Jacob, Daniel Kirshner, N. Krishnamurthy, & Kimmen Sjölander. (2007). Berkeley Phylogenomics Group web servers: resources for structural phylogenomic analysis. Nucleic Acids Research. 35(Web Server). W27–W32. 22 indexed citations

11.

Krishnamurthy, Nandini, et al.. (2006). PhyloFacts: an online structural phylogenomic encyclopedia for protein functional and structural classification. Genome biology. 7(9). R83–R83. 43 indexed citations

12.

Kirshner, Daniel. (2001). Efficiency Gains from Value Pricing: Case Study of Sunol Grade. Transportation Research Record Journal of the Transportation Research Board. 1747(1). 21–28. 3 indexed citations

13.

Kirshner, Daniel, et al.. (1999). Issues in electricity planning with computer models: illustrations with Elfin and WASP. Utilities Policy. 7(4). 201–219. 6 indexed citations

14.

Kirshner, Daniel. (1990). Implementation of conservation voltage reduction at Commonwealth Edison. IEEE Transactions on Power Systems. 5(4). 1178–1182. 68 indexed citations

15.

Kirshner, Daniel, et al.. (1984). Statistical Test of Energy Saving Due to Voltage Reduction. IEEE Transactions on Power Apparatus and Systems. PAS-103(6). 1205–1210. 30 indexed citations

16.

Kirshner, Daniel, et al.. (1984). Statistical Tests of Energy Savings Due to Voltage Reduction. IEEE Power Engineering Review. PER-4(6). 30–31. 23 indexed citations

17.

Kirshner, Daniel. (1980). Some nonexplanations of bicycle stability. American Journal of Physics. 48(1). 36–38. 11 indexed citations

18.

Kirshner, Daniel, et al.. (1979). A Comparison of Relative Predictive Power for Financial Models of Rates of Return. Journal of Financial and Quantitative Analysis. 14(2). 293–293. 2 indexed citations

19.

Talvitie, Antti & Daniel Kirshner. (1978). Specification, transferability and the effect of data outliers in modeling the choice of mode in urban travel. Transportation. 7(3). 311–331. 37 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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