Daniil Polykovskiy

3.4k total citations · 2 hit papers
14 papers, 562 citations indexed

About

Daniil Polykovskiy is a scholar working on Computational Theory and Mathematics, Molecular Biology and Materials Chemistry. According to data from OpenAlex, Daniil Polykovskiy has authored 14 papers receiving a total of 562 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Computational Theory and Mathematics, 5 papers in Molecular Biology and 5 papers in Materials Chemistry. Recurrent topics in Daniil Polykovskiy's work include Computational Drug Discovery Methods (7 papers), Machine Learning in Materials Science (5 papers) and Generative Adversarial Networks and Image Synthesis (2 papers). Daniil Polykovskiy is often cited by papers focused on Computational Drug Discovery Methods (7 papers), Machine Learning in Materials Science (5 papers) and Generative Adversarial Networks and Image Synthesis (2 papers). Daniil Polykovskiy collaborates with scholars based in Russia, United States and Hong Kong. Daniil Polykovskiy's co-authors include Alex Zhavoronkov, Vladimir Aladinskiy, Yan A. Ivanenkov, Artur Kadurin, Alex Aliper, Petrina Kamya, Feng Ren, Alexander Aliper, Dmitry Vetrov and Dmitry S. Bezrukov and has published in prestigious journals such as Chemical Science, Clinical Pharmacology & Therapeutics and Drug Discovery Today.

In The Last Decade

Daniil Polykovskiy

13 papers receiving 514 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 — A (Enhanced Table)

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

Name	h						Papers	Cites
Daniil Polykovskiy Russia	8	327	274	221	60	40	14	562
Kuzma Khrabrov United States	2	420 1.3×	328 1.2×	287 1.3×	56 0.9×	43 1.1×	4	605
Vladimir Aladinskiy Russia	9	431 1.3×	360 1.3×	301 1.4×	38 0.6×	41 1.0×	17	764
Jeff Blaney United States	7	392 1.2×	342 1.2×	179 0.8×	32 0.5×	32 0.8×	10	680
Kaitlyn Gayvert United States	8	257 0.8×	377 1.4×	90 0.4×	61 1.0×	28 0.7×	18	607
Fergus Imrie United Kingdom	9	318 1.0×	288 1.1×	180 0.8×	54 0.9×	21 0.5×	17	494
Artur Kadurin Russia	9	650 2.0×	523 1.9×	464 2.1×	101 1.7×	66 1.6×	14	937
Santosh Putta United States	17	388 1.2×	437 1.6×	147 0.7×	72 1.2×	24 0.6×	38	858
Lifan Chen China	10	429 1.3×	465 1.7×	183 0.8×	32 0.5×	12 0.3×	20	663
Jiahua Rao China	10	256 0.8×	434 1.6×	158 0.7×	131 2.2×	30 0.8×	24	641
Christian Margreitter Sweden	13	363 1.1×	558 2.0×	293 1.3×	18 0.3×	21 0.5×	21	826

Countries citing papers authored by Daniil Polykovskiy

Since Specialization

Citations

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

Fields of papers citing papers by Daniil Polykovskiy

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Daniil Polykovskiy

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

All Works

Sort: Min cites: Since: Top N: Style:

14 of 14 papers shown

Kuznetsov, Maksim, et al.. (2025). nach0-pc: Multi-task Language Model with Molecular Point Cloud Encoder. Proceedings of the AAAI Conference on Artificial Intelligence. 39(23). 24357–24365. 1 indexed citations

Zholus, Artem, et al.. (2025). BindGPT: A Scalable Framework for 3D Molecular Design via Language Modeling and Reinforcement Learning. Proceedings of the AAAI Conference on Artificial Intelligence. 39(24). 26083–26091. 3 indexed citations

Miftahutdinov, Zulfat, Elena Tutubalina, Maksim Kuznetsov, et al.. (2024). nach0: multimodal natural and chemical languages foundation model. Chemical Science. 15(22). 8380–8389. 22 indexed citations

Kuznetsov, Maksim, et al.. (2024). COSMIC: Molecular Conformation Space Modeling in Internal Coordinates with an Adversarial Framework. Journal of Chemical Information and Modeling. 64(9). 3610–3620. 1 indexed citations

Kamya, Petrina, Ivan V. Ozerov, Frank W. Pun, et al.. (2024). PandaOmics: An AI-Driven Platform for Therapeutic Target and Biomarker Discovery. Journal of Chemical Information and Modeling. 64(10). 3961–3969. 65 indexed citations breakdown →

Ivanenkov, Yan A., Daniil Polykovskiy, Dmitry S. Bezrukov, et al.. (2023). Chemistry42: An AI-Driven Platform for Molecular Design and Optimization. Journal of Chemical Information and Modeling. 63(3). 695–701. 109 indexed citations breakdown →

Aliper, Alex, Dmitry S. Bezrukov, Yen‐Chu Lin, et al.. (2023). Quantum computing for near-term applications in generative chemistry and drug discovery. Drug Discovery Today. 28(8). 103675–103675. 46 indexed citations

Aliper, Alex, Daniil Polykovskiy, Petrina Kamya, et al.. (2023). Prediction of Clinical Trials Outcomes Based on Target Choice and Clinical Trial Design with Multi‐Modal Artificial Intelligence. Clinical Pharmacology & Therapeutics. 114(5). 972–980. 35 indexed citations

Kuznetsov, Maksim, Alexander Zhebrak, Artur Kadurin, et al.. (2020). Molecular Generation for Desired Transcriptome Changes With Adversarial Autoencoders. Frontiers in Pharmacology. 11. 269–269. 28 indexed citations

10.

Kuznetsov, Maksim, Daniil Polykovskiy, Dmitry Vetrov, & Alexander Zhebrak. (2019). A Prior of a Googol Gaussians: a Tensor Ring Induced Prior for Generative Models. arXiv (Cornell University). 32. 4102–4112. 3 indexed citations

11.

Polykovskiy, Daniil, et al.. (2018). Concorde: Morphological Agreement in Conversational Models. Asian Conference on Machine Learning. 407–421.

12.

Polykovskiy, Daniil, et al.. (2018). Extracting Invariant Features From Images Using An Equivariant Autoencoder.. Asian Conference on Machine Learning. 438–453. 3 indexed citations

13.

Polykovskiy, Daniil, Dmitry Vetrov, Yan A. Ivanenkov, et al.. (2018). Entangled Conditional Adversarial Autoencoder for de Novo Drug Discovery. Molecular Pharmaceutics. 15(10). 4398–4405. 179 indexed citations

14.

Polykovskiy, Daniil, et al.. (2018). 3D Molecular Representations Based on the Wave Transform for Convolutional Neural Networks. Molecular Pharmaceutics. 15(10). 4378–4385. 67 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.

Explore authors with similar magnitude of impact