Andrew M. Lynn

1.0k total citations
57 papers, 693 citations indexed

About

Andrew M. Lynn is a scholar working on Molecular Biology, Computational Theory and Mathematics and Organic Chemistry. According to data from OpenAlex, Andrew M. Lynn has authored 57 papers receiving a total of 693 indexed citations (citations by other indexed papers that have themselves been cited), including 37 papers in Molecular Biology, 8 papers in Computational Theory and Mathematics and 6 papers in Organic Chemistry. Recurrent topics in Andrew M. Lynn's work include Protein Structure and Dynamics (14 papers), Machine Learning in Bioinformatics (8 papers) and Computational Drug Discovery Methods (8 papers). Andrew M. Lynn is often cited by papers focused on Protein Structure and Dynamics (14 papers), Machine Learning in Bioinformatics (8 papers) and Computational Drug Discovery Methods (8 papers). Andrew M. Lynn collaborates with scholars based in India, United Kingdom and United States. Andrew M. Lynn's co-authors include Amresh Prakash, Ashwani Pareek, Sneh L. Singla‐Pareek, Hemant R. Kushwaha, Manoj Kumar, Anupama Singh, Vijay Kumar, Rakesh Srivastava, Niranjan Kumar and Dhwani Desai and has published in prestigious journals such as Journal of Molecular Biology, Biochemistry and PLANT PHYSIOLOGY.

In The Last Decade

Andrew M. Lynn

53 papers receiving 670 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Andrew M. Lynn India 16 376 157 95 68 42 57 693
Seyed Shahriar Arab Iran 20 761 2.0× 74 0.5× 184 1.9× 44 0.6× 52 1.2× 87 1.2k
Krishanpal Anamika India 14 879 2.3× 102 0.6× 66 0.7× 66 1.0× 10 0.2× 25 1.3k
Yu‐Feng Huang China 18 429 1.1× 114 0.7× 48 0.5× 50 0.7× 10 0.2× 87 1.0k
Yan Niu China 18 329 0.9× 118 0.8× 60 0.6× 59 0.9× 13 0.3× 58 923
Prateek Kumar India 19 421 1.1× 57 0.4× 76 0.8× 278 4.1× 21 0.5× 65 862
Lijun Cai China 23 1.2k 3.1× 121 0.8× 220 2.3× 34 0.5× 28 0.7× 61 1.6k
Elizabeth Bilsland United Kingdom 14 446 1.2× 79 0.5× 91 1.0× 63 0.9× 5 0.1× 23 723
Pablo Moreno United Kingdom 20 1.0k 2.8× 508 3.2× 99 1.0× 31 0.5× 11 0.3× 62 1.7k
Yiwei Cao China 16 377 1.0× 75 0.5× 53 0.6× 285 4.2× 13 0.3× 52 1.0k

Countries citing papers authored by Andrew M. Lynn

Since Specialization
Citations

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

Fields of papers citing papers by Andrew M. Lynn

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Andrew M. Lynn. 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 Andrew M. Lynn. The network helps show where Andrew M. Lynn may publish in the future.

Co-authorship network of co-authors of Andrew M. Lynn

This figure shows the co-authorship network connecting the top 25 collaborators of Andrew M. Lynn. A scholar is included among the top collaborators of Andrew M. Lynn 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 Andrew M. Lynn. Andrew M. Lynn 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.
Saxena, Anjali, et al.. (2025). Synthesis, in-silico and in-vitro evaluation of quinoline-chromene hybrids as dual topoisomerase inhibitors. Bioorganic & Medicinal Chemistry. 131. 118422–118422.
2.
Lynn, Andrew M., et al.. (2025). Pancancer analysis of DNA damage repair gene mutations and their impact on immune regulatory gene expression. Scientific Reports. 15(1). 15667–15667.
3.
Kundu, Debasree, Samudrala Gourinath, Andrew M. Lynn, et al.. (2025). Structural Insight Into the Conversion of DhNik1, A Hybrid Histidine Kinase From Debaryomyces hansenii to a Cytotoxic Phosphatase Conformation for Novel Antifungal Agent. Journal of Molecular Biology. 437(12). 169116–169116.
4.
5.
Lynn, Andrew M., et al.. (2023). Mapping the FtsQBL divisome components in bacterial NTD pathogens as potential drug targets. Frontiers in Genetics. 13. 1010870–1010870. 3 indexed citations
6.
Lee, Pei‐Jun, et al.. (2022). Reduction deep learning model for floods recognition in satellite images. 273–274. 2 indexed citations
7.
Banerjee, Atanu, et al.. (2020). Unraveling the Mechanism of the MFS Multidrug Transporter using Steered Molecular Dynamics. Biophysical Journal. 118(3). 527a–527a. 1 indexed citations
8.
Prakash, Amresh, Preeti Pandey, Asimul Islam, et al.. (2020). Effects of natural mutations (L94I and L94V) on the stability and mechanism of folding of horse cytochrome c: A combined in vitro and molecular dynamics simulations approach. International Journal of Biological Macromolecules. 159. 976–985. 5 indexed citations
9.
Lynn, Andrew M., et al.. (2019). GCAC: galaxy workflow system for predictive model building for virtual screening. BMC Bioinformatics. 19(S13). 550–550. 9 indexed citations
10.
Kumar, Vijay, Preeti Pandey, Danish Idrees, Amresh Prakash, & Andrew M. Lynn. (2019). Delineating the effect of mutations on the conformational dynamics of N-terminal domain of TDP-43. Biophysical Chemistry. 250. 106174–106174. 21 indexed citations
11.
Kumar, Niranjan, Rakesh Srivastava, Amresh Prakash, & Andrew M. Lynn. (2019). Structure-based virtual screening, molecular dynamics simulation and MM-PBSA toward identifying the inhibitors for two-component regulatory system protein NarL ofMycobacterium Tuberculosis. Journal of Biomolecular Structure and Dynamics. 38(11). 3396–3410. 52 indexed citations
12.
13.
Khandelwal, Nitesh, et al.. (2018). Inventory of ABC proteins and their putative role in salt and drug tolerance in Debaryomyces hansenii. Gene. 676. 227–242. 6 indexed citations
14.
Lynn, Andrew M., et al.. (2017). QSAR based predictive modeling for anti-malarial molecules. Bioinformation. 13(5). 154–159. 10 indexed citations
15.
Pandey, Preeti, Andrew M. Lynn, & Pradipta Bandyopadhyay. (2017). Identification of inhibitors against α-Isopropylmalate Synthase of Mycobacterium tuberculosis using docking-MM/PBSA hybrid approach. Bioinformation. 13(5). 144–148. 8 indexed citations
16.
Vasantha, Gokula, et al.. (2015). Crowdsourcing solutions to 2D irregular strip packing problems from Internet workers. International Journal of Production Research. 54(14). 4104–4125. 7 indexed citations
17.
Sinha, Swati, et al.. (2015). Functional analysis of TPM domain containing Rv2345 of Mycobacterium tuberculosis identifies its phosphatase activity. Protein Expression and Purification. 111. 23–27. 2 indexed citations
18.
Singh, Shilpi, Urmi Bajpai, & Andrew M. Lynn. (2014). Structure based virtual screening to identify inhibitors against MurE Enzyme of Mycobacterium tuberculosis using AutoDock Vina. Bioinformation. 10(11). 697–702. 14 indexed citations
19.
Alden, Carl L., Andrew M. Lynn, Daniel Morton, et al.. (2011). A Critical Review of the Effectiveness of Rodent Pharmaceutical Carcinogenesis Testing in Predicting for Human Risk. Veterinary Pathology. 48(3). 772–784. 35 indexed citations
20.
Rehan, Mohd, et al.. (2011). Enzymatic characterization of Catalase from Bacillus anthracis and prediction of critical residues using information theoretic measure of Relative Entropy. Biochemical and Biophysical Research Communications. 411(1). 88–95. 12 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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