1386-2073
Published by: Bentham Science
https://benthamscience.com/journals/combinatorial-chemistry-and-high-throughput-screening/
| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Biology and Biochemistry | 726 | 23 | 27 | 6 |
| Chemistry | 772 | 40 | 49 | 8 |
The aim of Combinatorial Chemistry & High Throughput Screening is to expand the discussion of research in Computational biology, Combinatorial chemistry, Drug discovery, Biochemistry and High-throughput screening. In Combinatorial Chemistry & High Throughput Screening, Virtual screening, Bioinformatics, In silico, Gene and Drug are investigated in conjunction with one another to address concerns in Computational biology research. The Combinatorial chemistry study featured in it draws connections with the study of Organic chemistry.
The in-depth study on Drug discovery also explores topics in the intersecting field of Nanotechnology.
The most cited papers are organized to address concerns in the fields of Computational biology, Drug discovery, High-throughput screening, Nanotechnology and Combinatorial chemistry. The most cited articles facilitate discussions on Computational biology that incorporate concepts from other fields like Bioinformatics, Proteomics, Gene, In silico and DNA microarray. The journal papers focus on Drug discovery but the discussions also offer insight into other areas such as Pharmacokinetics, Pharmacology, Drug and In vivo.
Combinatorial Chemistry & High Throughput Screening covers a variety of subjects, including Internal medicine, Computational biology, Gene, Pharmacology and Catalysis. While Internal medicine is the focus of the journal, it also provided insights into the studies of Gastroenterology, Endocrinology and Oncology. Topics in Oncology explored in the journal were investigated in conjunction with research in Hepatocellular carcinoma, Survival analysis and Proportional hazards model.
While Computational biology is the key highlight in it, it also covered some subjects on Docking (molecular) and In silico. The journal focuses on Gene but the discussions also offer insight into other areas such as Cancer, Cancer research and Signal transduction. Some problems in Catalysis that were presented in the journal overlapped with concepts under Chemical engineering, Methanol and Nuclear chemistry.
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 Combinatorial Chemistry & High Throughput Screening (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 Combinatorial Chemistry & High Throughput Screening (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, 7.86% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 15.50% were posted by at least one author from the top 10 institutions publishing in the journal. Another 10.08% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 15.89% of all publications and 58.53% 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 are interested in embarking on a career in Computational Biology, it's important to consider all available career paths. One of these paths is becoming a medical biller and coder.
Working in the field of Computational Biology doesn't necessarily mean working directly in research or development. Medical billing and coding is a vital component of the healthcare industry, involving sorting and organizing patient data in hospital databases. This career path requires a robust understanding of biology and data management, making it a great choice for those interested in Computational Biology.
This realm of work is especially prominent in certain areas of the U.S. For instance, the healthcare industry in Colorado is particularly robust. If you're contemplating this career choice, you might want to consider learning more about how to become a medical coder in Colorado.
As technology continues to grow, the field of Computational Biology will continue to expand its reach. By exploring all possible career paths, you can find a role that leverages your interest and skills in the field.
Ali Maleki;Reza Taheri-Ledari;Reza Ghalavand
(2020)Cheng-Tang Pan;Karishma Dutt;Chung-Kun Yen;Ajay Kumar
(2021)Amal Rebai;Tuba Reçber;Emirhan Nemutlu;Chahra Chbili
(2020)For students interested in expanding their career options beyond traditional Chemistry roles, several related online degrees offer practical and fast-track pathways. Those interested in healthcare will find programs like fast track medical training ideal for quickly entering the medical field. These programs focus on essential skills in patient care and health services.
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Exploring these related online degrees can complement a Chemistry background and open diverse career pathways in the expanding healthcare and medical industries.
