Yashaswi Pathak

416 total citations
10 papers, 202 citations indexed

About

Yashaswi Pathak is a scholar working on Computational Theory and Mathematics, Molecular Biology and Materials Chemistry. According to data from OpenAlex, Yashaswi Pathak has authored 10 papers receiving a total of 202 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Computational Theory and Mathematics, 5 papers in Molecular Biology and 5 papers in Materials Chemistry. Recurrent topics in Yashaswi Pathak's work include Computational Drug Discovery Methods (6 papers), Machine Learning in Materials Science (5 papers) and Metabolomics and Mass Spectrometry Studies (2 papers). Yashaswi Pathak is often cited by papers focused on Computational Drug Discovery Methods (6 papers), Machine Learning in Materials Science (5 papers) and Metabolomics and Mass Spectrometry Studies (2 papers). Yashaswi Pathak collaborates with scholars based in India, Canada and Japan. Yashaswi Pathak's co-authors include U. Deva Priyakumar, Sarvesh Mehta, Girish Varma, Masahiro Ehara, Mallika Alvala, Aaftaab Sethi, Sujit Kumar Verma, Sarita Bajaj, Sarath Chandar and Boris Sattarov and has published in prestigious journals such as Diabetes, Physical Chemistry Chemical Physics and The Journal of Physical Chemistry Letters.

In The Last Decade

Yashaswi Pathak

10 papers receiving 198 citations

Peers

Yashaswi Pathak

MC

CTM

MB

BE

PTC

Kangjie Lin China

Jens A. Fuchs Switzerland

Sina Stocker Germany

Daniel Wigh United Kingdom

Zhuoran Qiao United States

Yuki Koda Japan

Edvard Lindelöf Sweden

Sarvesh Mehta India

Yuanqing Wang United States

Talia B. Kimber Germany

Kangjie Lin China

Yashaswi Pathak

147 ×1.0

MC

171 ×1.3

CTM

124 ×1.6

MB

25 ×1.2

BE

7 ×0.4

PTC

Citations per year, relative to Yashaswi Pathak Yashaswi Pathak (= 1×) peers Kangjie Lin

Countries citing papers authored by Yashaswi Pathak

Since Specialization

Citations

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

Fields of papers citing papers by Yashaswi Pathak

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Yashaswi Pathak

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

All Works

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10 of 10 papers shown

1.

Mehta, Sarvesh, et al.. (2024). DeepSPInN – deep reinforcement learning for molecular structure prediction from infrared and ¹³ C NMR spectra. Digital Discovery. 3(4). 818–829. 9 indexed citations

2.

Pathak, Yashaswi, et al.. (2022). 158-LB: Effect of Vitamin D Replacement on Glycemic Status and Pregnancy Outcomes in Vitamin D–Deficient Subjects with Gestational Diabetes Mellitus. Diabetes. 71(Supplement_1). 1 indexed citations

3.

Mehta, Sarvesh, et al.. (2022). Deep Reinforcement Learning for Molecular Inverse Problem of Nuclear Magnetic Resonance Spectra to Molecular Structure. The Journal of Physical Chemistry Letters. 13(22). 4924–4933. 20 indexed citations

4.

Pathak, Yashaswi, Sarvesh Mehta, & U. Deva Priyakumar. (2021). Learning Atomic Interactions through Solvation Free Energy Prediction Using Graph Neural Networks. Journal of Chemical Information and Modeling. 61(2). 689–698. 31 indexed citations

5.

Mehta, Sarvesh, et al.. (2021). MEMES: Machine learning framework for Enhanced MolEcular Screening. Chemical Science. 12(35). 11710–11721. 32 indexed citations

6.

Pathak, Yashaswi, et al.. (2021). Towered Actor Critic For Handling Multiple Action Types In Reinforcement Learning For Drug Discovery. Proceedings of the AAAI Conference on Artificial Intelligence. 35(1). 142–150. 5 indexed citations

7.

Pathak, Yashaswi, et al.. (2020). Deep learning enabled inorganic material generator. Physical Chemistry Chemical Physics. 22(46). 26935–26943. 40 indexed citations

8.

Pathak, Yashaswi, et al.. (2020). Chemically Interpretable Graph Interaction Network for Prediction of Pharmacokinetic Properties of Drug-Like Molecules. Proceedings of the AAAI Conference on Artificial Intelligence. 34(1). 873–880. 26 indexed citations

9.

Pathak, Yashaswi, et al.. (2019). BAND NN: A Deep Learning Framework for Energy Prediction and Geometry Optimization of Organic Small Molecules. Journal of Computational Chemistry. 41(8). 790–799. 28 indexed citations

10.

Pathak, Yashaswi, et al.. (2017). Metabolic status and hypogonadism in human immunodeficiency virus-infected males. Indian Journal of Endocrinology and Metabolism. 21(5). 684–684. 10 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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