1573-4099
Published by: Bentham Science
https://benthamscience.com/journals/current-computer-aided-drug-design/
| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Chemistry | 988 | 14 | 14 | 4 |
Current Computer - Aided Drug Design covers a variety of subjects, including Quantitative structure–activity relationship, Docking (molecular), Computational biology, Stereochemistry and Virtual screening. Topics in Quantitative structure–activity relationship explored in the journal were investigated in conjunction with research in Combinatorial chemistry, Biological system, Linear regression and Artificial intelligence. The research on Artificial intelligence discussed in Current Computer - Aided Drug Design draws on the closely related field of Pattern recognition.
In addition to Docking (molecular) research, Current Computer - Aided Drug Design aims to explore topics under Pharmacophore, In silico and Binding site. The in-depth study on In silico also explores topics in the intersecting field of ADME. Issues in Computational biology were discussed, taking into consideration concepts from other disciplines like Drug and Bioinformatics.
Ligand (biochemistry), Molecular dynamics, Active site and Molecule, Hydrogen bond are some topics wherein Stereochemistry research discussed in Current Computer - Aided Drug Design have an impact. The Virtual screening study featured in the journal draws parallels with the field of Drug discovery. The study on Biochemistry featured in the journal expounds on the topic of Enzyme in particular.
The published papers aim to foster the development of research in Quantitative structure–activity relationship, Drug discovery, Virtual screening, Docking (molecular) and Data mining. The studies on Quantitative structure–activity relationship discussed at the journal articles can also contribute to research in the domains of Molecule, Biological system and Artificial intelligence. The most cited papers focus on Drug discovery but the discussions also offer insight into other areas such as Combinatorial chemistry, Lead compound, Pharmacophore and Computational biology.
Current Computer - Aided Drug Design focuses largely on the fields of Docking (molecular), In silico, Computational biology, Biochemistry and Virtual screening. The Docking (molecular) research presented falls under the domain of Stereochemistry. The research on In silico featured in it combines topics in other fields like Cancer research, In vitro, Molecular model, Mycobacterium tuberculosis and Combinatorial chemistry.
The study on Molecular model presented in the journal intersects with the topics under Quantitative structure–activity relationship. Current Computer - Aided Drug Design focuses on Computational biology but the discussions also offer insight into other areas such as Drug discovery, Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), Coronavirus disease 2019 (COVID-19) and Peptide. Concepts in Drug, as well as related topics in Function (biology) and Multiple drug resistance, are covered in the Biochemistry research presented in the journal.
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 Current Computer - Aided Drug Design (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 Current Computer - Aided Drug Design (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, 10.67% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 22.39% were posted by at least one author from the top 10 institutions publishing in the journal. Another 5.97% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 29.85% of all publications and 41.79% 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.
Taibi Ben Hadda;Vesna Rastija;Faisal AlMalki;Abderrahim Titi
(2021)Andrey A Toropov;Alla P Toropova
(2020)Subhash C. Basak;Lemont B. Kier
(2021)Thomas Scior;Hassan H. Abdallah;Kenia Salvador-Atonal;Stefan Laufer
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