Pravin Ambure

3.0k total citations · 3 hit papers
26 papers, 2.4k citations indexed

About

Pravin Ambure is a scholar working on Computational Theory and Mathematics, Materials Chemistry and Pharmacology. According to data from OpenAlex, Pravin Ambure has authored 26 papers receiving a total of 2.4k indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Computational Theory and Mathematics, 8 papers in Materials Chemistry and 7 papers in Pharmacology. Recurrent topics in Pravin Ambure's work include Computational Drug Discovery Methods (20 papers), Machine Learning in Materials Science (7 papers) and Cholinesterase and Neurodegenerative Diseases (7 papers). Pravin Ambure is often cited by papers focused on Computational Drug Discovery Methods (20 papers), Machine Learning in Materials Science (7 papers) and Cholinesterase and Neurodegenerative Diseases (7 papers). Pravin Ambure collaborates with scholars based in India, United States and United Kingdom. Pravin Ambure's co-authors include Kunal Roy, Supratik Kar, Rahul Balasaheb Aher, Rudra Narayan Das, Tomasz Puzyn, Agnieszka Gajewicz, M. Natália D. S. Cordeiro, Priyanka De, Amit Kumar Halder and Probir Kumar Ojha and has published in prestigious journals such as RSC Advances, Environmental Toxicology and Chemistry and Bioorganic & Medicinal Chemistry.

In The Last Decade

Pravin Ambure

26 papers receiving 2.3k 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.

On a simple approach for determining applicability domain of QSAR models

2015 606 citations Kunal Roy, Supratik Kar et al. Chemometrics and Intelligent Laboratory Systems profile →
Be aware of error measures. Further studies on validation of predictive QSAR models

2016 583 citations Kunal Roy, Rudra Narayan Das et al. Chemometrics and Intelligent Laboratory Systems profile →
Prediction reliability of QSAR models: an overview of various validation tools

2022 115 citations Priyanka De, Supratik Kar et al. Archives of Toxicology profile →

Peers

Countries citing papers authored by Pravin Ambure

Since Specialization

Citations

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

Fields of papers citing papers by Pravin Ambure

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Pravin Ambure

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

All Works

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20 of 20 papers shown

#	Work	Indexed citations
1	Addressing the challenges of acute fish toxicity hazard classification using a non-animal defined approach 2025 ·Environmental Toxicology and Chemistry ·Donna S. Macmillan, Pravin Ambure, V. Aranda, (unknown), (unknown), (unknown), Nicolás Fabre, Stephan Fischer, Geoff Hodges, (unknown), (unknown), Claudia Rivetti, (unknown), Kristin Schirmer, Eva Serrano‐Candelas, (unknown), (unknown)	4
2	G4-QuadScreen: A Computational Tool for Identifying Multi-Target-Directed Anticancer Leads against G-Quadruplex DNA 2023 ·Cancers ·(unknown), Pravin Ambure, Eva Serrano‐Candelas, (unknown), (unknown), Mario Guerrero, Margarita Martı́n, Jorge González‐García, Enrique García‐España, Rafael Gozalbes	6
3	Prediction reliability of QSAR models: an overview of various validation tools breakdown → 2022 ·Archives of Toxicology ·Priyanka De, Supratik Kar, Pravin Ambure, Kunal Roy	115
4	Turning deep-eutectic solvents into value-added products for CO2 capture: A desirability-based virtual screening study 2022 ·Journal of CO2 Utilization ·Amit Kumar Halder, Pravin Ambure, Yunierkis Pérez‐Castillo, M. Natália D. S. Cordeiro	37
5	New Workflow for QSAR Model Development from Small Data Sets: Small Dataset Curator and Small Dataset Modeler. Integration of Data Curation, Exhaustive Double Cross-Validation, and a Set of Optimal Model Selection Techniques 2019 ·Journal of Chemical Information and Modeling ·Pravin Ambure, Agnieszka Gajewicz, M. Natália D. S. Cordeiro, Kunal Roy	53
6	Exploration of synthetic antioxidant flavonoid analogs as acetylcholinesterase inhibitors: an approach towards finding their quantitative structure–activity relationship 2019 ·Medicinal Chemistry Research ·(unknown), Pravin Ambure, (unknown), Sreeparna Das, Kunal Roy, Naznin Ara Begum	24
7	“Prediction Reliability Indicator”: A new tool to judge the quality of predictions from QSAR models for new query compounds 2018 ·Kunal Roy, Pravin Ambure, Supratik Kar	6
8	How Precise Are Our Quantitative Structure–Activity Relationship Derived Predictions for New Query Chemicals? 2018 ·ACS Omega ·Kunal Roy, Pravin Ambure, Supratik Kar	114
9	Is it possible to improve the quality of predictions from an “intelligent” use of multiple QSAR/QSPR/QSTR models? 2018 ·Journal of Chemometrics ·Kunal Roy, Pravin Ambure, Supratik Kar, Probir Kumar Ojha	101
10	CADD Modeling of Multi-Target Drugs Against Alzheimer's Disease 2017 ·Current Drug Targets ·Pravin Ambure, Kunal Roy	15
11	How important is to detect systematic error in predictions and understand statistical applicability domain of QSAR models? 2017 ·Chemometrics and Intelligent Laboratory Systems ·Kunal Roy, Pravin Ambure, Rahul Balasaheb Aher	130
12	Be aware of error measures. Further studies on validation of predictive QSAR models breakdown → 2016 ·Chemometrics and Intelligent Laboratory Systems ·Kunal Roy, Rudra Narayan Das, Pravin Ambure, Rahul Balasaheb Aher	583
13	The “double cross-validation” software tool for MLR QSAR model development 2016 ·Chemometrics and Intelligent Laboratory Systems ·Kunal Roy, Pravin Ambure	75
14	“NanoBRIDGES” software: Open access tools to perform QSAR and nano-QSAR modeling 2015 ·Chemometrics and Intelligent Laboratory Systems ·Pravin Ambure, Rahul Balasaheb Aher, Agnieszka Gajewicz, Tomasz Puzyn, Kunal Roy	151
15	In vitro evaluation and in silico screening of synthetic acetylcholinesterase inhibitors bearing functionalized piperidine pharmacophores 2015 ·Bioorganic & Medicinal Chemistry ·Goutam Brahmachari, Chee‐Yan Choo, Pravin Ambure, Kunal Roy	47
16	Exploring Structural Requirements of Imaging Agents Against Aβ Plaques in Alzheimer’s Disease: A QSAR Approach 2015 ·Combinatorial Chemistry & High Throughput Screening ·Pravin Ambure, Kunal Roy	4
17	Advances in quantitative structure–activity relationship models of anti-Alzheimer’s agents 2014 ·Expert Opinion on Drug Discovery ·Pravin Ambure, Kunal Roy	15
18	Exploring structural requirements of leads for improving activity and selectivity against CDK5/p25 in Alzheimer's disease: an in silico approach 2014 ·RSC Advances ·Pravin Ambure, Kunal Roy	19
19	Pharmacophore mapping-based virtual screening followed by molecular docking studies in search of potential acetylcholinesterase inhibitors as anti-Alzheimer's agents 2013 ·Biosystems ·Pravin Ambure, Supratik Kar, Kunal Roy	53
20	3D-QSAR and molecular docking analysis of biphenyl amide derivatives as p38α mitogen-activated protein kinase inhibitors 2012 ·Molecular Diversity ·Pravin Ambure, Rahul P. Gangwal, Abhay T. Sangamwar	22

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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