Petrina Kamya

609 total citations · 2 hit papers
8 papers, 317 citations indexed

About

Petrina Kamya is a scholar working on Computational Theory and Mathematics, Organic Chemistry and Molecular Biology. According to data from OpenAlex, Petrina Kamya has authored 8 papers receiving a total of 317 indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Computational Theory and Mathematics, 2 papers in Organic Chemistry and 2 papers in Molecular Biology. Recurrent topics in Petrina Kamya's work include Computational Drug Discovery Methods (4 papers), Machine Learning in Materials Science (2 papers) and Synthesis and Properties of Aromatic Compounds (2 papers). Petrina Kamya is often cited by papers focused on Computational Drug Discovery Methods (4 papers), Machine Learning in Materials Science (2 papers) and Synthesis and Properties of Aromatic Compounds (2 papers). Petrina Kamya collaborates with scholars based in Canada, Hong Kong and United States. Petrina Kamya's co-authors include Feng Ren, Alex Zhavoronkov, Alex Aliper, Daniil Polykovskiy, Dmitry S. Bezrukov, Heidi M. Muchall, Yan A. Ivanenkov, Bogdan Zagribelnyy, Vladimir Aladinskiy and Yen‐Chu Lin and has published in prestigious journals such as The Journal of Physical Chemistry A, Clinical Pharmacology & Therapeutics and Drug Discovery Today.

In The Last Decade

Petrina Kamya

8 papers receiving 288 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Chemistry42: An AI-Driven Platform for Molecular Design and Optimization

2023 109 citations Yan A. Ivanenkov, Daniil Polykovskiy et al. Journal of Chemical Information and Modeling profile →
PandaOmics: An AI-Driven Platform for Therapeutic Target and Biomarker Discovery

2024 65 citations Petrina Kamya, Ivan V. Ozerov et al. Journal of Chemical Information and Modeling profile →

Peers

Countries citing papers authored by Petrina Kamya

Since Specialization

Citations

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

Fields of papers citing papers by Petrina Kamya

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Petrina Kamya

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

All Works

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8 of 8 papers shown

#	Work	Indexed citations
1	PandaOmics: An AI-Driven Platform for Therapeutic Target and Biomarker Discovery breakdown → 2024 ·Journal of Chemical Information and Modeling ·Petrina Kamya, Ivan V. Ozerov, Frank W. Pun, Kyle Tretina, (unknown), Shan Chen, Vladimir Naumov, Xi Long, Sha Lin, Mikhail Korzinkin, Daniil Polykovskiy, Alex Aliper, Feng Ren, Alex Zhavoronkov	65
2	Chemistry42: An AI-Driven Platform for Molecular Design and Optimization breakdown → 2023 ·Journal of Chemical Information and Modeling ·Yan A. Ivanenkov, Daniil Polykovskiy, Dmitry S. Bezrukov, Bogdan Zagribelnyy, Vladimir Aladinskiy, Petrina Kamya, Alex Aliper, Feng Ren, Alex Zhavoronkov	109
3	Quantum computing for near-term applications in generative chemistry and drug discovery 2023 ·Drug Discovery Today ·(unknown), Alex Aliper, Dmitry S. Bezrukov, Yen‐Chu Lin, Daniil Polykovskiy, Petrina Kamya, Feng Ren, Alex Zhavoronkov	46
4	Prediction of Clinical Trials Outcomes Based on Target Choice and Clinical Trial Design with Multi‐Modal Artificial Intelligence 2023 ·Clinical Pharmacology & Therapeutics ·Alex Aliper, (unknown), Daniil Polykovskiy, Petrina Kamya, Elena Tutubalina, Shan Chen, Feng Ren, Alex Zhavoronkov	35
5	The Hitchhiker’s Guide to Deep Learning Driven Generative Chemistry 2023 ·ACS Medicinal Chemistry Letters ·Yan A. Ivanenkov, Bogdan Zagribelnyy, (unknown), (unknown), Victor A. Terentiev, Petrina Kamya, Dmitry S. Bezrukov, Alex Aliper, Feng Ren, Alex Zhavoronkov	24
6	Revisiting the Effects of Sequence and Structure on the Hydrogen Bonding and π-Stacking Interactions in Nucleic Acids 2011 ·The Journal of Physical Chemistry A ·Petrina Kamya, Heidi M. Muchall	15
7	New Insights into the Use of (TD-)DFT for Geometries and Electronic Structures of Constrained π-Stacked Systems: [n.n]Paracyclophanes 2008 ·The Journal of Physical Chemistry A ·Petrina Kamya, Heidi M. Muchall	19
8	The conformational behaviour of methylenecyclohexanes revisited 2003 ·Canadian Journal of Chemistry ·Heidi M. Muchall, Petrina Kamya, Jean Lessard	4

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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