Akash Khandelwal

1.4k total citations
51 papers, 1.1k citations indexed

About

Akash Khandelwal is a scholar working on Oncology, Molecular Biology and Computational Theory and Mathematics. According to data from OpenAlex, Akash Khandelwal has authored 51 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 26 papers in Oncology, 15 papers in Molecular Biology and 14 papers in Computational Theory and Mathematics. Recurrent topics in Akash Khandelwal's work include Computational Drug Discovery Methods (14 papers), Drug Transport and Resistance Mechanisms (9 papers) and Statistical Methods in Clinical Trials (7 papers). Akash Khandelwal is often cited by papers focused on Computational Drug Discovery Methods (14 papers), Drug Transport and Resistance Mechanisms (9 papers) and Statistical Methods in Clinical Trials (7 papers). Akash Khandelwal collaborates with scholars based in United States, Germany and Switzerland. Akash Khandelwal's co-authors include Peter W. Swaan, Štefan Baláž, Pascal Girard, Gregory Kaler, David M. Truong, Sanjay K. Nigám, Viera Lukáčová, Soumyendu Raha, Daniel M. Kroll and Sean Ekins and has published in prestigious journals such as Journal of Biological Chemistry, Nature Communications and Journal of Clinical Oncology.

In The Last Decade

Akash Khandelwal

48 papers receiving 1.0k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Akash Khandelwal United States 17 387 339 249 131 129 51 1.1k
Hiroyuki Hirano Japan 17 367 0.9× 320 0.9× 222 0.9× 138 1.1× 128 1.0× 51 1.0k
Nageshwar Budha United States 22 477 1.2× 478 1.4× 97 0.4× 189 1.4× 144 1.1× 52 1.5k
Abdelhakim Ahmed‐Belkacem France 17 379 1.0× 502 1.5× 80 0.3× 117 0.9× 173 1.3× 31 1.2k
Fabio Broccatelli United States 18 404 1.0× 455 1.3× 435 1.7× 328 2.5× 301 2.3× 30 1.5k
Matthew Wright United States 25 352 0.9× 472 1.4× 174 0.7× 284 2.2× 342 2.7× 85 1.8k
Ana Ruiz-Garcı́a United States 23 952 2.5× 622 1.8× 100 0.4× 95 0.7× 93 0.7× 65 2.0k
George Tonn United States 17 242 0.6× 332 1.0× 104 0.4× 256 2.0× 178 1.4× 31 939
Fiona Macintyre United Kingdom 11 244 0.6× 305 0.9× 275 1.1× 336 2.6× 96 0.7× 16 1.1k
Handan He United States 26 485 1.3× 411 1.2× 280 1.1× 591 4.5× 75 0.6× 54 1.8k
Jane P. F. Bai United States 25 283 0.7× 553 1.6× 236 0.9× 105 0.8× 34 0.3× 72 1.4k

Countries citing papers authored by Akash Khandelwal

Since Specialization
Citations

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

Fields of papers citing papers by Akash Khandelwal

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Akash Khandelwal

This figure shows the co-authorship network connecting the top 25 collaborators of Akash Khandelwal. A scholar is included among the top collaborators of Akash Khandelwal 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 Akash Khandelwal. Akash Khandelwal 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.
Dai, Haiqing, James S. Bourdage, Dongli Zhou, et al.. (2024). Immunogenicity of avelumab in patients with metastatic Merkel cell carcinoma or advanced urothelial carcinoma. Clinical and Translational Science. 17(3). e13730–e13730. 4 indexed citations
2.
Dembélé, Laurent, Perrine Courlet, Akash Khandelwal, et al.. (2024). Towards clinically relevant dose ratios for Cabamiquine and Pyronaridine combination using P. falciparum field isolate data. Nature Communications. 15(1). 7659–7659. 1 indexed citations
3.
Sidhu, Jagdev, Laura Shaughnessy, Rocío Lledó‐García, et al.. (2024). Population PK modeling of certolizumab pegol in pregnant women with chronic inflammatory diseases. CPT Pharmacometrics & Systems Pharmacology. 13(11). 1904–1914. 1 indexed citations
4.
Terranova, Nadia, Diane R. Mould, Yulia Vugmeyster, et al.. (2023). Tumor growth inhibition modeling in patients with second line biliary tract cancer and first line non‐small cell lung cancer based on bintrafusp alfa trials. CPT Pharmacometrics & Systems Pharmacology. 13(1). 143–153. 4 indexed citations
5.
Krishnan, Sreenath M., Siv Jönsson, Lena E. Friberg, et al.. (2023). A multistate modeling and simulation framework to learn dose–response of oncology drugs: Application to bintrafusp alfa in non‐small cell lung cancer. CPT Pharmacometrics & Systems Pharmacology. 12(11). 1738–1750. 5 indexed citations
6.
Khandelwal, Akash, et al.. (2023). Nontubercular Bacterial and Fungal Infections in Patients of Chronic Obstructive Pulmonary Disease. Annals of African Medicine. 22(1). 77–81. 1 indexed citations
7.
Khandelwal, Akash, et al.. (2022). Pharmacometrics Golems: Exposure‐Response Models in Oncology. Clinical Pharmacology & Therapeutics. 112(5). 941–945. 8 indexed citations
8.
Venkatakrishnan, Karthik, et al.. (2022). Variable or variate? A conundrum in pharmacometrics exposure–response models. CPT Pharmacometrics & Systems Pharmacology. 12(2). 144–147. 2 indexed citations
9.
Aggarwal, Mukul, Akash Khandelwal, Rishi Dhawan, et al.. (2022). COVID-19 infection in patients with haematological disease - A tertiary centre experience from north India. International Journal of Microbiology Research. 155(5&6). 570–574.
10.
Khandelwal, Akash, Paula M. Alves, Lassina Badolo, et al.. (2022). Translation of liver stage activity of M5717, a Plasmodium elongation factor 2 inhibitor: from bench to bedside. Malaria Journal. 21(1). 151–151. 8 indexed citations
11.
12.
Khandelwal, Akash, et al.. (2021). Fast screening of covariates in population models empowered by machine learning. Journal of Pharmacokinetics and Pharmacodynamics. 48(4). 597–609. 39 indexed citations
13.
Aggarwal, Mukul, Rishi Dhawan, Jasmita Dass, et al.. (2020). Tele-Medicine Services in Hematological Practice During Covid Pandemic: Its Feasibility and Difficulties. Indian Journal of Hematology and Blood Transfusion. 37(4). 528–533. 7 indexed citations
14.
Watson, Estelle, et al.. (2019). <p>Population pharmacokinetic modeling to facilitate dose selection of tapentadol in the pediatric population</p>. Journal of Pain Research. Volume 12. 2835–2850. 5 indexed citations
15.
Natesan, Senthil, et al.. (2011). Rigorous Treatment of Multispecies Multimode Ligand−Receptor Interactions in 3D-QSAR: CoMFA Analysis of Thyroxine Analogs Binding to Transthyretin. Journal of Chemical Information and Modeling. 51(5). 1132–1150. 6 indexed citations
16.
Truong, David M., Gregory Kaler, Akash Khandelwal, Peter W. Swaan, & Sanjay K. Nigám. (2008). Multi-level Analysis of Organic Anion Transporters 1, 3, and 6 Reveals Major Differences in Structural Determinants of Antiviral Discrimination. Journal of Biological Chemistry. 283(13). 8654–8663. 82 indexed citations
17.
Khandelwal, Akash & Štefan Baláž. (2007). QM/MM linear response method distinguishes ligand affinities for closely related metalloproteins. Proteins Structure Function and Bioinformatics. 69(2). 326–339. 27 indexed citations
18.
Khandelwal, Akash, Praveen M. Bahadduri, Cheng Chang, et al.. (2007). Computational Models to Assign Biopharmaceutics Drug Disposition Classification from Molecular Structure. Pharmaceutical Research. 24(12). 2249–2262. 43 indexed citations
19.
Bulusu, Gopalakrishnan, et al.. (2003). Three-dimensional quantitative structure–activity relationship (3D-QSAR) studies of tricyclic oxazolidinones as antibacterial agents. Bioorganic & Medicinal Chemistry. 11(12). 2569–2574. 12 indexed citations
20.
Khandelwal, Akash. (2003). 3-D-QSAR CoMFA and CoMSIA studies on tetrahydrofuroyl-?-phenylalanine derivatives as VLA-4 antagonists. Bioorganic & Medicinal Chemistry. 11(19). 4235–4244. 8 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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