Swarit Jasial

566 total citations
18 papers, 305 citations indexed

About

Swarit Jasial is a scholar working on Computational Theory and Mathematics, Molecular Biology and Materials Chemistry. According to data from OpenAlex, Swarit Jasial has authored 18 papers receiving a total of 305 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Computational Theory and Mathematics, 6 papers in Molecular Biology and 5 papers in Materials Chemistry. Recurrent topics in Swarit Jasial's work include Computational Drug Discovery Methods (13 papers), Machine Learning in Materials Science (5 papers) and Click Chemistry and Applications (4 papers). Swarit Jasial is often cited by papers focused on Computational Drug Discovery Methods (13 papers), Machine Learning in Materials Science (5 papers) and Click Chemistry and Applications (4 papers). Swarit Jasial collaborates with scholars based in Germany, Japan and France. Swarit Jasial's co-authors include Jürgen Bajorath, Ye Hu, Tomoyuki Miyao, Erik Gilberg, Kimito Funatsu, Martin Vogt, Dilyana Dimova, Dagmar Stumpfe, Akinori Sato and Thomas Blaschke and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and Journal of Medicinal Chemistry.

In The Last Decade

Swarit Jasial

18 papers receiving 303 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Swarit Jasial Germany 9 216 174 69 66 52 18 305
Wei P. Feinstein United States 8 223 1.0× 305 1.8× 53 0.8× 50 0.8× 45 0.9× 12 415
Chase M. Webb United States 3 217 1.0× 315 1.8× 50 0.7× 78 1.2× 61 1.2× 4 450
Andrew T. McNutt United States 6 268 1.2× 312 1.8× 41 0.6× 48 0.7× 113 2.2× 7 476
Andreas Luttens Sweden 7 204 0.9× 275 1.6× 39 0.6× 65 1.0× 49 0.9× 7 414
Christiane Ehrt Germany 11 210 1.0× 394 2.3× 49 0.7× 120 1.8× 72 1.4× 30 549
Anurag T. K. Baidya India 11 169 0.8× 175 1.0× 48 0.7× 97 1.5× 41 0.8× 27 419
Elissa A. Fink United States 4 179 0.8× 224 1.3× 37 0.5× 61 0.9× 43 0.8× 4 347
Sarah Tomas‐Hernández Spain 7 143 0.7× 234 1.3× 64 0.9× 56 0.8× 61 1.2× 10 418
Volker Hähnke Germany 9 162 0.8× 174 1.0× 45 0.7× 39 0.6× 39 0.8× 18 296
Yanxing Wang China 10 142 0.7× 221 1.3× 47 0.7× 43 0.7× 73 1.4× 21 344

Countries citing papers authored by Swarit Jasial

Since Specialization
Citations

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

Fields of papers citing papers by Swarit Jasial

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Swarit Jasial

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

All Works

18 of 18 papers shown
1.
Jasial, Swarit, et al.. (2024). Bridging Structure- and Ligand-Based Virtual Screening through Fragmented Interaction Fingerprint. ACS Omega. 9(37). 38957–38969. 2 indexed citations
2.
Jasial, Swarit, Miho Hatanaka, Yu‐ya Ohnishi, et al.. (2023). Chemometrics Approach Based on Wavelet Transforms for the Estimation of Monomer Concentrations from FTIR Spectra. ACS Omega. 8(22). 19781–19788. 5 indexed citations
3.
Jasial, Swarit, et al.. (2022). Screening and Validation of Odorants against Influenza A Virus Using Interpretable Regression Models. ACS Pharmacology & Translational Science. 6(1). 139–150. 3 indexed citations
4.
Nakano, Hiroshi, Tomoyuki Miyao, Swarit Jasial, & Kimito Funatsu. (2021). Sparse Topological Pharmacophore Graphs for Interpretable Scaffold Hopping. Journal of Chemical Information and Modeling. 61(7). 3348–3360. 8 indexed citations
5.
Sato, Akinori, Tomoyuki Miyao, Swarit Jasial, & Kimito Funatsu. (2021). Comparing predictive ability of QSAR/QSPR models using 2D and 3D molecular representations. Journal of Computer-Aided Molecular Design. 35(2). 179–193. 25 indexed citations
6.
Jasial, Swarit, et al.. (2021). Interpretation of Ligand-Based Activity Cliff Prediction Models Using the Matched Molecular Pair Kernel. Molecules. 26(16). 4916–4916. 8 indexed citations
7.
Miyao, Tomoyuki, Swarit Jasial, Jürgen Bajorath, & Kimito Funatsu. (2019). Evaluation of different virtual screening strategies on the basis of compound sets with characteristic core distributions and dissimilarity relationships. Journal of Computer-Aided Molecular Design. 33(8). 729–743. 5 indexed citations
8.
Rodríguez-Pérez, Raquel, Tomoyuki Miyao, Swarit Jasial, Martin Vogt, & Jürgen Bajorath. (2018). Prediction of Compound Profiling Matrices Using Machine Learning. ACS Omega. 3(4). 4713–4723. 26 indexed citations
9.
Vogt, Martin, Swarit Jasial, & Jürgen Bajorath. (2018). Extracting Compound Profiling Matrices from Screening Data. ACS Omega. 3(4). 4706–4712. 7 indexed citations
10.
Jasial, Swarit, Erik Gilberg, Thomas Blaschke, & Jürgen Bajorath. (2018). Machine Learning Distinguishes with High Accuracy between Pan-Assay Interference Compounds That Are Promiscuous or Represent Dark Chemical Matter. Journal of Medicinal Chemistry. 61(22). 10255–10264. 22 indexed citations
11.
Hu, Ye, Swarit Jasial, Erik Gilberg, & Jürgen Bajorath. (2017). Structure-Promiscuity Relationship Puzzles—Extensively Assayed Analogs with Large Differences in Target Annotations. The AAPS Journal. 19(3). 856–864. 13 indexed citations
12.
Jasial, Swarit & Jürgen Bajorath. (2017). Dark chemical matter in public screening assays and derivation of target hypotheses. MedChemComm. 8(11). 2100–2104. 4 indexed citations
13.
14.
Jasial, Swarit, Ye Hu, & Jürgen Bajorath. (2016). Determining the Degree of Promiscuity of Extensively Assayed Compounds. PLoS ONE. 11(4). e0153873–e0153873. 35 indexed citations
15.
Jasial, Swarit, Ye Hu, & Jürgen Bajorath. (2016). Assessing the Growth of Bioactive Compounds and Scaffolds over Time: Implications for Lead Discovery and Scaffold Hopping. Journal of Chemical Information and Modeling. 56(2). 300–307. 17 indexed citations
16.
Gilberg, Erik, Swarit Jasial, Dagmar Stumpfe, Dilyana Dimova, & Jürgen Bajorath. (2016). Highly Promiscuous Small Molecules from Biological Screening Assays Include Many Pan-Assay Interference Compounds but Also Candidates for Polypharmacology. Journal of Medicinal Chemistry. 59(22). 10285–10290. 36 indexed citations
17.
Jasial, Swarit, Ye Hu, Martin Vogt, & Jürgen Bajorath. (2016). Activity-relevant similarity values for fingerprints and implications for similarity searching [version 2; referees: 3 approved]. SHILAP Revista de lepidopterología. 5. 1 indexed citations
18.
Jasial, Swarit, et al.. (2015). Determination of Meta‐Parameters for Support Vector Machine Linear Combinations. Molecular Informatics. 34(2-3). 127–133. 1 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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