| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Computer Science | 138 | 56 | 136 | 34 |
Journal of Cheminformatics was organized to reinforce research efforts on Data mining, Artificial intelligence, Cheminformatics, Computational biology and Virtual screening. While Data mining is the focus of it, it also provided insights into the studies of Quantitative structure–activity relationship, Set (abstract data type), Support vector machine, Similarity (network science) and PubChem. Journal of Cheminformatics explores topics in Artificial intelligence which can be helpful for research in disciplines like Machine learning and Pattern recognition.
Some problems in Cheminformatics that were presented in Journal of Cheminformatics overlapped with concepts under Software and Data science. It addresses concerns in Computational biology which are intertwined with other disciplines, such as Drug, Small molecule and Drug discovery, Bioinformatics. While Journal of Cheminformatics focused on Virtual screening, it was also able to explore topics like Pharmacophore and Chemical space.
The published papers facilitate discussions on Data mining, Artificial intelligence, Cheminformatics, Data science and Virtual screening. While Artificial intelligence is the focus of the published articles, it also provides insights into the studies of Machine learning and Pattern recognition. The journal articles hold forums on Cheminformatics that merge themes from other disciplines such as Python (programming language), Programming language, Software, Quantitative structure–activity relationship and Data set.
The journal mainly tackles studies in Artificial intelligence, Machine learning, Set (abstract data type), Cheminformatics and Drug discovery. The research on Artificial intelligence featured in it combines topics in other fields like Virtual screening, Docking (molecular) and Pattern recognition. The Set (abstract data type) works featured in the journal incorporate elements from Chemical nomenclature, Natural language processing, Representation (mathematics), Identifier and Algorithm.
The journal holds forums on Cheminformatics that merges themes from other disciplines such as Similarity (network science), Chemical space, World Wide Web and Open science. It deals with Artificial neural network in conjunction with PubChem and similar fields in Software. In the journal, Web application and Workflow are investigated in conjunction with one another to address concerns in Software research.
A key indicator for each journal is its effectiveness in reaching other researchers with the papers published at that venue.
The chart below presents the interquartile range (first quartile 25%, median 50% and third quartile 75%) of the number of citations of articles over time.
The top authors publishing in Journal of Cheminformatics (based on the number of publications) are:
The overall trend for top authors publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top authors.
Only papers with recognized affiliations are considered
The top affiliations publishing in Journal of Cheminformatics (based on the number of publications) are:
The overall trend for top affiliations publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top affiliations.
The publication chance index shows the ratio of articles published by the best research institutions in the journal edition to all articles published within that journal. The best research institutions were selected based on the largest number of articles published during all editions of the journal.
The chart below presents the percentage ratio of articles from top institutions (based on their ranking of total papers).Top affiliations were grouped by their rank into the following tiers: top 1-10, top 11-20, top 21-50, and top 51+. Only articles with a recognized affiliation are considered.
During the most recent 2021 edition, 3.66% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 15.19% were posted by at least one author from the top 10 institutions publishing in the journal. Another 16.46% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 13.92% of all publications and 54.43% were from other institutions.
A very common phenomenon observed among researchers publishing scientific articles is the intentional selection of journals they have already attended in the past. In particular, it is worth analyzing the case when the authors participate in the same journal from year to year.
The Returning Authors Index presented below illustrates the ratio of authors who participated in both a given as well as the previous edition of the journal in relation to all participants in a given year.
The graph below shows the Returning Institution Index, illustrating the ratio of institutions that participated in both a given and the previous edition of the conference in relation to all affiliations present in a given year.
Our experience to innovation index was created to show a cross-section of the experience level of authors publishing in a journal. The index includes the authors publishing at the last edition of a journal, grouped by total number of publications throughout their academic career (P) and the total number of citations of these publications ever received (C).
The group intervals were selected empirically to best show the diversity of the authors' experiences, their labels were selected as a convenience, not as judgment. The authors were divided into the following groups:
The chart below illustrates experience levels of first authors in cases of publications with multiple authors.
If you find these topics interesting, you may consider pursuing a career in cheminformatics. However, the first step towards a career in cheminformatics or any other academic field is obtaining the appropriate educational qualifications. The path towards becoming a cheminformatics specialist can vary greatly depending upon the individual's educational background and interests. Usually, those in the field have a strong background in computer science and chemistry. However, those with degrees in related fields may also find opportunities in cheminformatics.
Some of the positions available for those with a degree in cheminformatics include bioinformatics scientists, research scientists, computational chemists, and data scientists. These positions may require further education or training, especially for those with a bachelor's degree.
For those in Utah seeking to transition from a different field, especially teaching, it may be necessary to complete additional studies. Several institutions offer programs that allow individuals to make these career switches without necessitating a complete return to undergraduate studies. You may refer to our guide on how to become a teacher in utah with a bachelor's degree for more detailed information.
Regardless of the path you choose, the field of cheminformatics promises a wealth of opportunities with the prospect of contributing to vital research and development within many scientific disciplines.
Dejun Jiang;Zhenxing Wu;Chang-Yu Hsieh;Guangyong Chen
(2021)Maria Sorokina;Peter Merseburger;Kohulan Rajan;Mehmet Aziz Yirik
(2021)Laurianne David;Amol Thakkar;Amol Thakkar;Rocío Mercado;Ola Engkvist
(2020)Maria Sorokina;Christoph Steinbeck
(2020)Tamer N. Jarada;Jon George Rokne;Reda Alhajj;Reda Alhajj
(2020)Samuel Genheden;Amol Thakkar;Amol Thakkar;Veronika Chadimová;Jean-Louis Reymond
(2020)Pavel Karpov;Guillaume Godin;Igor V. Tetko
(2020)Michael Withnall;Edvard Lindelöf;Ola Engkvist;Hongming Chen
(2020)Josep Arús-Pous;Josep Arús-Pous;Atanas Patronov;Esben Jannik Bjerrum;Christian Tyrchan
(2020)Nalini Schaduangrat;Samuel Lampa;Saw Simeon;Matthew Paul Gleeson
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