Amol Thakkar

1.7k total citations · 1 hit paper
15 papers, 923 citations indexed

About

Amol Thakkar is a scholar working on Computational Theory and Mathematics, Materials Chemistry and Molecular Biology. According to data from OpenAlex, Amol Thakkar has authored 15 papers receiving a total of 923 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Computational Theory and Mathematics, 11 papers in Materials Chemistry and 6 papers in Molecular Biology. Recurrent topics in Amol Thakkar's work include Machine Learning in Materials Science (11 papers), Computational Drug Discovery Methods (11 papers) and Chemical Synthesis and Analysis (5 papers). Amol Thakkar is often cited by papers focused on Machine Learning in Materials Science (11 papers), Computational Drug Discovery Methods (11 papers) and Chemical Synthesis and Analysis (5 papers). Amol Thakkar collaborates with scholars based in Switzerland, Sweden and United Kingdom. Amol Thakkar's co-authors include Ola Engkvist, Jean‐Louis Reymond, Esben Jannik Bjerrum, Rocío Mercado, Veronika Chadimová, Samuel Genheden, Thierry Kogej, Simon Johansson, David Buttar and Kjell Jorner and has published in prestigious journals such as Chemical Communications, Journal of Medicinal Chemistry and Chemical Science.

In The Last Decade

Amol Thakkar

15 papers receiving 897 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.

Molecular representations in AI-driven drug discovery: a review and practical guide

2020 347 citations Amol Thakkar, Rocío Mercado et al. Journal of Cheminformatics profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Amol Thakkar Switzerland	8	646	554	420	120	79	15	923
Jike Wang China	18	753 1.2×	458 0.8×	660 1.6×	79 0.7×	68 0.9×	52	1.1k
Philipp Eiden Germany	8	895 1.4×	788 1.4×	537 1.3×	69 0.6×	97 1.2×	9	1.2k
Josep Arús‐Pous Switzerland	12	1.0k 1.6×	741 1.3×	776 1.8×	119 1.0×	72 0.9×	17	1.3k
Tomasz Klucznik South Korea	10	574 0.9×	624 1.1×	454 1.1×	239 2.0×	76 1.0×	15	1.1k
Simon Johansson Sweden	8	487 0.8×	404 0.7×	330 0.8×	69 0.6×	59 0.7×	19	678
Christos A. Nicolaou United States	16	660 1.0×	287 0.5×	512 1.2×	78 0.7×	59 0.7×	30	941
Wen Torng United States	7	715 1.1×	337 0.6×	632 1.5×	60 0.5×	65 0.8×	7	1.0k
Feisheng Zhong China	13	1.1k 1.8×	671 1.2×	952 2.3×	157 1.3×	123 1.6×	18	1.7k
Lukas Friedrich Switzerland	13	574 0.9×	324 0.6×	490 1.2×	105 0.9×	40 0.5×	25	940
Valery Tkachenko United States	14	450 0.7×	201 0.4×	470 1.1×	78 0.7×	83 1.1×	18	1.0k

Countries citing papers authored by Amol Thakkar

Since Specialization

Citations

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

Fields of papers citing papers by Amol Thakkar

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Amol Thakkar

This figure shows the co-authorship network connecting the top 25 collaborators of Amol Thakkar. A scholar is included among the top collaborators of Amol Thakkar 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 Amol Thakkar. Amol Thakkar 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:

15 of 15 papers shown

Reynoso-Moreno, Inés, et al.. (2025). Exploring Simple Drug Scaffolds from the Generated Database Chemical Space Reveals a Chiral Bicyclic Azepane with Potent Neuropharmacology. Journal of Medicinal Chemistry. 68(9). 9176–9201. 6 indexed citations

Zipoli, Federico, et al.. (2024). Activity recognition in scientific experimentation using multimodal visual encoding. Digital Discovery. 4(2). 393–402. 1 indexed citations

Leonov, Artem I., et al.. (2023). Tools for Synthesis Planning, Automation, and Analytical Data Analysis. CHIMIA International Journal for Chemistry. 77(1/2). 17–17. 1 indexed citations

Thakkar, Amol, et al.. (2023). Standardizing chemical compounds with language models. Machine Learning Science and Technology. 4(3). 35014–35014. 1 indexed citations

Thakkar, Amol, et al.. (2023). Unbiasing Retrosynthesis Language Models with Disconnection Prompts. ACS Central Science. 9(7). 1488–1498. 20 indexed citations

Thakkar, Amol, Veronika Chadimová, Esben Jannik Bjerrum, Ola Engkvist, & Jean‐Louis Reymond. (2021). Retrosynthetic accessibility score (RAscore) – rapid machine learned synthesizability classification from AI driven retrosynthetic planning. Chemical Science. 12(9). 3339–3349. 123 indexed citations

Thakkar, Amol & Philippe Schwaller. (2021). How AI for Synthesis Can Help Tackle Challenges in Molecular Discovery. CHIMIA International Journal for Chemistry. 75(7-8). 677–677. 2 indexed citations

Bjerrum, Esben Jannik, Amol Thakkar, & Ola Engkvist. (2020). Artificial applicability labels for improving policies in retrosynthesis prediction. Machine Learning Science and Technology. 2(1). 17001–17001. 2 indexed citations

Thakkar, Amol, Simon Johansson, Kjell Jorner, et al.. (2020). Artificial intelligence and automation in computer aided synthesis planning. Reaction Chemistry & Engineering. 6(1). 27–51. 49 indexed citations

10.

Genheden, Samuel, Amol Thakkar, Veronika Chadimová, et al.. (2020). AiZynthFinder: a fast, robust and flexible open-source software for retrosynthetic planning. Journal of Cheminformatics. 12(1). 70–70. 206 indexed citations

11.

Thakkar, Amol, et al.. (2020). Molecular representations in AI-driven drug discovery: a review and practical guide. Journal of Cheminformatics. 12(1). 56–56. 347 indexed citations breakdown →

12.

Thakkar, Amol, Nidhal Selmi, Jean‐Louis Reymond, Ola Engkvist, & Esben Jannik Bjerrum. (2020). “Ring Breaker”: Neural Network Driven Synthesis Prediction of the Ring System Chemical Space. Journal of Medicinal Chemistry. 63(16). 8791–8808. 24 indexed citations

13.

Johansson, Simon, Amol Thakkar, Thierry Kogej, et al.. (2019). AI-assisted synthesis prediction. Drug Discovery Today Technologies. 32-33. 65–72. 34 indexed citations

14.

Thakkar, Amol, Thierry Kogej, Jean‐Louis Reymond, Ola Engkvist, & Esben Jannik Bjerrum. (2019). Datasets and their influence on the development of computer assisted synthesis planning tools in the pharmaceutical domain. Chemical Science. 11(1). 154–168. 106 indexed citations

15.

Sieffert, Nicolas, Amol Thakkar, & Michæl Bühl. (2018). Modelling uranyl chemistry in liquid ammonia from density functional theory. Chemical Communications. 54(74). 10431–10434. 1 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