Emma King‐Smith

571 total citations
10 papers, 402 citations indexed

About

Emma King‐Smith is a scholar working on Organic Chemistry, Molecular Biology and Pharmacology. According to data from OpenAlex, Emma King‐Smith has authored 10 papers receiving a total of 402 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Organic Chemistry, 5 papers in Molecular Biology and 3 papers in Pharmacology. Recurrent topics in Emma King‐Smith's work include Catalytic C–H Functionalization Methods (4 papers), Machine Learning in Materials Science (3 papers) and Microbial Natural Products and Biosynthesis (3 papers). Emma King‐Smith is often cited by papers focused on Catalytic C–H Functionalization Methods (4 papers), Machine Learning in Materials Science (3 papers) and Microbial Natural Products and Biosynthesis (3 papers). Emma King‐Smith collaborates with scholars based in United States, United Kingdom and Israel. Emma King‐Smith's co-authors include Hans Renata, Xiao Zhang, Fuzhuo Li, Jian Li, Jeffrey D. Rudolf, Liao‐Bin Dong, Li‐Cheng Yang, Ben Shen, Christian R. Zwick and Alpha A. Lee and has published in prestigious journals such as Science, Angewandte Chemie International Edition and Nature Communications.

In The Last Decade

Emma King‐Smith

9 papers receiving 397 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Emma King‐Smith United States	8	201	182	102	74	71	10	402
Suman Chakrabarty United States	10	300 1.5×	388 2.1×	111 1.1×	59 0.8×	167 2.4×	11	658
Sabrina Hoebenreich Germany	8	364 1.8×	116 0.6×	34 0.3×	106 1.4×	80 1.1×	10	483
Matthew D. DeMars United States	8	195 1.0×	220 1.2×	100 1.0×	112 1.5×	97 1.4×	9	402
Lauren A. M. Murray Australia	9	171 0.9×	242 1.3×	133 1.3×	44 0.6×	69 1.0×	13	428
Christian R. Zwick United States	10	200 1.0×	215 1.2×	107 1.0×	47 0.6×	116 1.6×	15	401
Jared L. Piper United States	17	224 1.1×	626 3.4×	104 1.0×	25 0.3×	87 1.2×	30	797
Fuzhuo Li China	12	212 1.1×	271 1.5×	129 1.3×	77 1.0×	60 0.8×	18	482
Justyna Kulig Germany	10	349 1.7×	78 0.4×	36 0.4×	88 1.2×	55 0.8×	10	431
Daniel Méndez‐Sánchez Spain	15	303 1.5×	198 1.1×	105 1.0×	53 0.7×	71 1.0×	26	488
Despina J. Bougioukou United States	9	413 2.1×	106 0.6×	43 0.4×	38 0.5×	78 1.1×	11	482

Countries citing papers authored by Emma King‐Smith

Since Specialization

Citations

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

Fields of papers citing papers by Emma King‐Smith

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Emma King‐Smith. 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 Emma King‐Smith. The network helps show where Emma King‐Smith may publish in the future.

Co-authorship network of co-authors of Emma King‐Smith

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

All Works

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10 of 10 papers shown

King‐Smith, Emma, Felix A. Faber, Louise Bernier, et al.. (2025). Predictive design of crystallographic chiral separation. Nature Communications. 16(1). 7977–7977.

London, Nir, et al.. (2024). Deconvoluting low yield from weak potency in direct-to-biology workflows with machine learning. RSC Medicinal Chemistry. 15(3). 1015–1021. 3 indexed citations

King‐Smith, Emma, Felix A. Faber, Usa Reilly, et al.. (2024). Predictive Minisci late stage functionalization with transfer learning. Nature Communications. 15(1). 426–426. 18 indexed citations

King‐Smith, Emma, Simon Berritt, Louise Bernier, et al.. (2024). Probing the chemical ‘reactome’ with high-throughput experimentation data. Nature Chemistry. 16(4). 633–643. 26 indexed citations

King‐Smith, Emma. (2023). Transfer learning for a foundational chemistry model. Chemical Science. 15(14). 5143–5151. 15 indexed citations

Zhang, Xiao, Emma King‐Smith, Liao‐Bin Dong, et al.. (2020). Divergent synthesis of complex diterpenes through a hybrid oxidative approach. Science. 369(6505). 799–806. 116 indexed citations

Li, Jian, Fuzhuo Li, Emma King‐Smith, & Hans Renata. (2020). Merging chemoenzymatic and radical-based retrosynthetic logic for rapid and modular synthesis of oxidized meroterpenoids. Nature Chemistry. 12(2). 173–179. 93 indexed citations

Zhang, Xiao, Emma King‐Smith, & Hans Renata. (2018). Total Synthesis of Tambromycin by Combining Chemocatalytic and Biocatalytic C−H Functionalization. Angewandte Chemie International Edition. 57(18). 5037–5041. 70 indexed citations

Zhang, Xiǎo, Emma King‐Smith, & Hans Renata. (2018). Total Synthesis of Tambromycin by Combining Chemocatalytic and Biocatalytic C−H Functionalization. Angewandte Chemie. 130(18). 5131–5135. 14 indexed citations

10.

King‐Smith, Emma, Christian R. Zwick, & Hans Renata. (2017). Applications of Oxygenases in the Chemoenzymatic Total Synthesis of Complex Natural Products. Biochemistry. 57(4). 403–412. 47 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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