Alexander Heifetz

2.2k total citations
45 papers, 1.5k citations indexed

About

Alexander Heifetz is a scholar working on Molecular Biology, Computational Theory and Mathematics and Cellular and Molecular Neuroscience. According to data from OpenAlex, Alexander Heifetz has authored 45 papers receiving a total of 1.5k indexed citations (citations by other indexed papers that have themselves been cited), including 37 papers in Molecular Biology, 21 papers in Computational Theory and Mathematics and 16 papers in Cellular and Molecular Neuroscience. Recurrent topics in Alexander Heifetz's work include Receptor Mechanisms and Signaling (29 papers), Computational Drug Discovery Methods (21 papers) and Neuropeptides and Animal Physiology (14 papers). Alexander Heifetz is often cited by papers focused on Receptor Mechanisms and Signaling (29 papers), Computational Drug Discovery Methods (21 papers) and Neuropeptides and Animal Physiology (14 papers). Alexander Heifetz collaborates with scholars based in United Kingdom, Japan and Germany. Alexander Heifetz's co-authors include Philip C. Biggin, Matteo Aldeghi, Michael J. Bodkin, Stefan Knapp, Mike J. Bodkin, Dmitri G. Fedorov, Andrea Townsend‐Nicholson, Michelle Southey, Iñaki Morao and Merav Fichman and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Journal of the American Chemical Society and The Journal of Physical Chemistry B.

In The Last Decade

Alexander Heifetz

44 papers receiving 1.5k citations

Peers

Alexander Heifetz
Viet Hoang Man United States
David J. Huggins United States
Matteo Masetti Italy
K. Anton Feenstra Netherlands
Callum J. Dickson United Kingdom
Giorgio Saladino United Kingdom
Robin M. Betz United States
Brigita Urbanc United States
Manuel Rueda Spain
Dan Sindhikara United States
Viet Hoang Man United States
Alexander Heifetz
Citations per year, relative to Alexander Heifetz Alexander Heifetz (= 1×) peers Viet Hoang Man

Countries citing papers authored by Alexander Heifetz

Since Specialization
Citations

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

Fields of papers citing papers by Alexander Heifetz

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Alexander Heifetz

This figure shows the co-authorship network connecting the top 25 collaborators of Alexander Heifetz. A scholar is included among the top collaborators of Alexander Heifetz 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 Alexander Heifetz. Alexander Heifetz 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.
Heifetz, Alexander. (2023). High Performance Computing for Drug Discovery and Biomedicine. Methods in molecular biology. 7 indexed citations
2.
Heifetz, Alexander. (2023). Accelerating COVID-19 Drug Discovery with High-Performance Computing. Methods in molecular biology. 2716. 405–411. 2 indexed citations
3.
Heifetz, Alexander, et al.. (2023). High-Throughput Structure-Based Drug Design (HT-SBDD) Using Drug Docking, Fragment Molecular Orbital Calculations, and Molecular Dynamic Techniques. Methods in molecular biology. 2716. 293–306. 9 indexed citations
4.
Heifetz, Alexander, et al.. (2021). Predicting Residence Time of GPCR Ligands with Machine Learning. Methods in molecular biology. 2390. 191–205. 5 indexed citations
5.
Morao, Iñaki, Alexander Heifetz, & Dmitri G. Fedorov. (2020). Accurate Scoring in Seconds with the Fragment Molecular Orbital and Density-Functional Tight-Binding Methods. Methods in molecular biology. 2114. 143–148. 8 indexed citations
6.
Habgood, Matthew, et al.. (2020). Conformational Searching with Quantum Mechanics. Methods in molecular biology. 2114. 207–229. 18 indexed citations
7.
Heifetz, Alexander & Andrea Townsend‐Nicholson. (2020). Characterizing Rhodopsin-Arrestin Interactions with the Fragment Molecular Orbital (FMO) Method. Methods in molecular biology. 2114. 177–186. 1 indexed citations
8.
Heifetz, Alexander, Michelle Southey, Iñaki Morao, et al.. (2020). Analyzing GPCR-Ligand Interactions with the Fragment Molecular Orbital (FMO) Method. Methods in molecular biology. 163–175.
9.
Heifetz, Alexander, et al.. (2020). Guiding Medicinal Chemistry with Fragment Molecular Orbital (FMO) Method. Methods in molecular biology. 2114. 37–48. 7 indexed citations
10.
Wan, Shunzhou, David W. Wright, Alexander Heifetz, et al.. (2020). Hit-to-lead and lead optimization binding free energy calculations for G protein-coupled receptors. Interface Focus. 10(6). 20190128–20190128. 15 indexed citations
11.
Heifetz, Alexander, Michelle Southey, Iñaki Morao, et al.. (2019). Characterising GPCR–ligand interactions using a fragment molecular orbital-based approach. Current Opinion in Structural Biology. 55. 85–92. 14 indexed citations
12.
Townsend‐Nicholson, Andrea, et al.. (2019). Computational prediction of GPCR oligomerization. Current Opinion in Structural Biology. 55. 178–184. 12 indexed citations
13.
Zhang, Xin, Jason B. Cross, Jan Antoinette C. Romero, et al.. (2018). In-silico guided discovery of novel CCR9 antagonists. Journal of Computer-Aided Molecular Design. 32(4). 573–582. 3 indexed citations
14.
Heifetz, Alexander, Michelle Southey, Iñaki Morao, Andrea Townsend‐Nicholson, & Mike J. Bodkin. (2017). Computational Methods Used in Hit-to-Lead and Lead Optimization Stages of Structure-Based Drug Discovery. Methods in molecular biology. 1705. 375–394. 21 indexed citations
15.
Chudyk, Ewa I., Matteo Aldeghi, Dmitri G. Fedorov, et al.. (2017). Exploring GPCR-Ligand Interactions with the Fragment Molecular Orbital (FMO) Method. Methods in molecular biology. 1705. 179–195. 14 indexed citations
16.
Heifetz, Alexander, et al.. (2017). Synergistic Use of GPCR Modeling and SDM Experiments to Understand Ligand Binding. Methods in molecular biology. 1705. 335–343. 1 indexed citations
17.
Biggin, Philip C., Matteo Aldeghi, Michael J. Bodkin, & Alexander Heifetz. (2016). Beyond Membrane Protein Structure: Drug Discovery, Dynamics and Difficulties. Advances in experimental medicine and biology. 922. 161–181. 4 indexed citations
18.
Davenport, Adam J., Clemens Möller, Alexander Heifetz, et al.. (2010). Using Electrophysiology and In Silico Three-Dimensional Modeling to Reduce Human Ether-à-go-go Related Gene K + Channel Inhibition in a Histamine H3 Receptor Antagonist Program. Assay and Drug Development Technologies. 8(6). 781–789. 12 indexed citations
19.
Tye, Heather, J. Prestle, Stefan Scheuerer, et al.. (2010). Novel 6,7,8,9-tetrahydro-5H-1,4,7,10a-tetraaza-cyclohepta[f]indene analogues as potent and selective 5-HT2C agonists for the treatment of metabolic disorders. Bioorganic & Medicinal Chemistry Letters. 21(1). 34–37. 21 indexed citations
20.
Becker, Oren M., Yael Marantz, Sharon Shacham, et al.. (2004). G protein-coupled receptors: In silico drug discovery in 3D. Proceedings of the National Academy of Sciences. 101(31). 11304–11309. 110 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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