Michael S. Lajiness

1.0k total citations
21 papers, 735 citations indexed

About

Michael S. Lajiness is a scholar working on Computational Theory and Mathematics, Molecular Biology and Spectroscopy. According to data from OpenAlex, Michael S. Lajiness has authored 21 papers receiving a total of 735 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Computational Theory and Mathematics, 7 papers in Molecular Biology and 6 papers in Spectroscopy. Recurrent topics in Michael S. Lajiness's work include Computational Drug Discovery Methods (12 papers), Analytical Chemistry and Chromatography (5 papers) and Biomedical Text Mining and Ontologies (4 papers). Michael S. Lajiness is often cited by papers focused on Computational Drug Discovery Methods (12 papers), Analytical Chemistry and Chromatography (5 papers) and Biomedical Text Mining and Ontologies (4 papers). Michael S. Lajiness collaborates with scholars based in United States, Germany and South Korea. Michael S. Lajiness's co-authors include Gerald M. Maggiora, John H. Van Drie, Veerabahu Shanmugasundaram, Michal Vieth, Jon A. Erickson, Jürgen Bajorath, Mathias J. Wawer, Lisa Peltason, Rajarshi Guha and Mark A. Johnson and has published in prestigious journals such as Journal of Medicinal Chemistry, BMC Bioinformatics and Drug Discovery Today.

In The Last Decade

Michael S. Lajiness

21 papers receiving 693 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Michael S. Lajiness United States 15 506 372 124 121 103 21 735
Bruno Bienfait United States 15 504 1.0× 639 1.7× 122 1.0× 122 1.0× 116 1.1× 29 1.1k
Mike Hann United Kingdom 4 393 0.8× 374 1.0× 80 0.6× 146 1.2× 81 0.8× 6 628
Linda Traphagen United States 6 413 0.8× 389 1.0× 68 0.5× 88 0.7× 117 1.1× 10 645
Maykel Cruz‐Monteagudo Portugal 19 629 1.2× 511 1.4× 81 0.7× 152 1.3× 105 1.0× 50 984
Ed Griffen United Kingdom 11 482 1.0× 434 1.2× 110 0.9× 215 1.8× 97 0.9× 15 775
Gavin Harper United Kingdom 11 725 1.4× 745 2.0× 117 0.9× 216 1.8× 174 1.7× 16 1.1k
Steven M. Muskal United States 9 437 0.9× 531 1.4× 146 1.2× 87 0.7× 66 0.6× 16 820
Wolfgang Muster Switzerland 13 324 0.6× 220 0.6× 81 0.7× 83 0.7× 65 0.6× 26 742
Jens Loesel United Kingdom 6 489 1.0× 403 1.1× 97 0.8× 196 1.6× 91 0.9× 7 885
David A. Cosgrove United Kingdom 13 457 0.9× 396 1.1× 104 0.8× 118 1.0× 78 0.8× 22 654

Countries citing papers authored by Michael S. Lajiness

Since Specialization
Citations

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

Fields of papers citing papers by Michael S. Lajiness

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Michael S. Lajiness. 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 Michael S. Lajiness. The network helps show where Michael S. Lajiness may publish in the future.

Co-authorship network of co-authors of Michael S. Lajiness

This figure shows the co-authorship network connecting the top 25 collaborators of Michael S. Lajiness. A scholar is included among the top collaborators of Michael S. Lajiness 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 Michael S. Lajiness. Michael S. Lajiness 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
1.
Lajiness, Michael S.. (2021). The power of a mentor. Journal of Computer-Aided Molecular Design. 36(5). 339–340. 1 indexed citations
2.
Wild, David, Ying Ding, Amit Sheth, et al.. (2011). Systems chemical biology and the Semantic Web: what they mean for the future of drug discovery research. Drug Discovery Today. 17(9-10). 469–474. 44 indexed citations
3.
Zhu, Qian, et al.. (2011). Semantic inference using chemogenomics data for drug discovery. BMC Bioinformatics. 12(1). 256–256. 14 indexed citations
4.
Zhu, Qian, Michael S. Lajiness, Ying Ding, & David Wild. (2010). WENDI: A tool for finding non-obvious relationships between compounds and biological properties, genes, diseases and scholarly publications. Journal of Cheminformatics. 2(1). 6–6. 28 indexed citations
5.
Zhu, Qian, et al.. (2010). Using Web Technologies for Integrative Drug Discovery. 188–191. 1 indexed citations
6.
Bajorath, Jürgen, Lisa Peltason, Mathias J. Wawer, et al.. (2009). Navigating structure–activity landscapes. Drug Discovery Today. 14(13-14). 698–705. 134 indexed citations
7.
Lajiness, Michael S. & I. J. Watson. (2008). Dissimilarity-based approaches to compound acquisition. Current Opinion in Chemical Biology. 12(3). 366–371. 19 indexed citations
8.
Lajiness, Michael S., Gerald M. Maggiora, & Veerabahu Shanmugasundaram. (2004). Assessment of the Consistency of Medicinal Chemists in Reviewing Sets of Compounds. Journal of Medicinal Chemistry. 47(20). 4891–4896. 96 indexed citations
9.
Lajiness, Michael S., Michal Vieth, & Jon A. Erickson. (2004). Molecular properties that influence oral drug-like behavior.. PubMed. 7(4). 470–7. 81 indexed citations
10.
Lajiness, Michael S. & Veerabahu Shanmugasundaram. (2004). Strategies for the Identification and Generation of Informative Compound Sets. Methods in molecular biology. 275. 111–129. 2 indexed citations
11.
Shanmugasundaram, Veerabahu, Gerald M. Maggiora, & Michael S. Lajiness. (2004). Hit-Directed Nearest-Neighbor Searching. Journal of Medicinal Chemistry. 48(1). 240–248. 40 indexed citations
12.
Gao, Hua, et al.. (2002). Enhancement of binary QSAR analysis by a GA-based variable selection method. Journal of Molecular Graphics and Modelling. 20(4). 259–268. 25 indexed citations
13.
Drie, John H. Van & Michael S. Lajiness. (1998). Approaches to virtual library design. Drug Discovery Today. 3(6). 274–283. 57 indexed citations
14.
Lajiness, Michael S.. (1996). Dissimilarity-based compound selection techniques. Perspectives in Drug Discovery and Design. 7-8(1). 65–84. 39 indexed citations
15.
Cheng, Cheng, Gerald M. Maggiora, Michael S. Lajiness, & Mark A. Johnson. (1996). Four Association Coefficients for Relating Molecular Similarity Measures. Journal of Chemical Information and Computer Sciences. 36(4). 909–915. 25 indexed citations
16.
Brunden, Marshall N., Betty H. Yagi, Michael S. Lajiness, & Gary E. Zurenko. (1991). Estimating the postantibiotic effect: A two-phase mathematical model. Journal of Pharmacokinetics and Biopharmaceutics. 19(4). 457–468. 2 indexed citations
17.
Lajiness, Michael S.. (1990). Molecular similarity-based methods for selecting compounds for screening. Nova Science Publishers, Inc. eBooks. 299–316. 32 indexed citations
18.
Johnson, Mark A., Michael S. Lajiness, & Gerald M. Maggiora. (1989). Molecular similarity: a basis for designing drug screening programs.. PubMed. 291. 167–71. 34 indexed citations
19.
Maggiora, Gerald M., et al.. (1988). Looking for buried treasures: The search for new drug leads in large chemical databases. Mathematical and Computer Modelling. 11. 626–629. 6 indexed citations
20.
Kay, Marguerite M.B., et al.. (1979). Age-related changes in the immune system of mice of eight medium and long-lived strains and hybrids. I. Organ, cellular, and activity changes. Mechanisms of Ageing and Development. 11(5-6). 295–346. 52 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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