Journal of Chemical Information and Modeling

Top Research Topics at Journal of Chemical Information and Modeling?

Journal of Chemical Information and Modeling was organized to reinforce research efforts on Artificial intelligence, Computational biology, Molecular dynamics, Docking (molecular) and Virtual screening. Topics in Artificial intelligence explored in it were investigated in conjunction with research in Quantitative structure–activity relationship, Machine learning, Data mining and Pattern recognition. The majority of Machine learning studies in Journal of Chemical Information and Modeling are focused on the subject of Support vector machine.

The research on Data mining tackled can also make contributions to studies in the areas of Set (abstract data type) and Data set. The presented Computational biology research focuses mostly on Binding site and, on occasion, topics in Plasma protein binding. While Molecular dynamics is the focus of it, it also provided insights into the studies of Protein structure, Biophysics, Molecule and Biological system.

Journal of Chemical Information and Modeling encompasses Docking (molecular) studies in the context of Stereochemistry as a whole. Studies on Stereochemistry discussed in Journal of Chemical Information and Modeling link to the field of Ligand (biochemistry). Discussions in the journal are anchored in the subject of Virtual screening and the similar topic of Pharmacophore.

• Artificial intelligence (15.52%)
• Computational biology (14.46%)
• Molecular dynamics (13.12%)

What are the most cited papers published in the journal?

• ZINC - A Free Database of Commercially Available Compounds for Virtual Screening (2765 citations)
• LigPlot+: multiple ligand-protein interaction diagrams for drug discovery. (2491 citations)
• Extended-Connectivity Fingerprints (2420 citations)

Research areas of the most cited articles at Journal of Chemical Information and Modeling:

The most cited publications primarily focus on research topics in Artificial intelligence, Virtual screening, Data mining, Docking (molecular) and Machine learning. Issues in Artificial intelligence were discussed in the most cited articles, taking into consideration concepts from other disciplines like Quantitative structure–activity relationship and Pattern recognition. While work presented in the published papers provide substantial information on Virtual screening, it also covers topics in Pharmacophore, DOCK and Computational biology.

What topics the last edition of the journal is best known for?

• Enzyme
• Gene
• Artificial intelligence

The previous edition focused in particular on these issues:

The journal covers a variety of subjects, including Artificial intelligence, Molecular dynamics, Computational biology, Biophysics and Machine learning. Journal of Chemical Information and Modeling focuses on Artificial intelligence but the discussions also offer insight into other areas such as Set (abstract data type) and Pattern recognition. Journal of Chemical Information and Modeling explores research in Binding site and overlapping concepts in Ligand (biochemistry) to expand the discourse in Molecular dynamics.

The journal tackles studies in Drug discovery and the interrelated subject of Virtual screening to gain insights into Computational biology. The work on Biophysics tackled in Journal of Chemical Information and Modeling brings together disciplines like Membrane, Allosteric regulation and Peptide. Molecular Docking Simulation is a major topic of Docking (molecular) research.

The most cited articles from the last journal are:

• Recent Force Field Strategies for Intrinsically Disordered Proteins. (15 citations)
• Transferable Multilevel Attention Neural Network for Accurate Prediction of Quantum Chemistry Properties via Multitask Learning. (14 citations)
• Program Package for the Calculation of Origin-Independent Electron Current Density and Derived Magnetic Properties in Molecular Systems. (13 citations)

Papers citation over time

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.

Top authors and change over time

The top authors publishing in Journal of Chemical Information and Modeling (based on the number of publications) are:

• Jürgen Bajorath (106 papers) published 1 paper at the last edition, 1 less than at the previous edition,
• Matthias Rarey (51 papers) published 4 papers at the last edition, 1 more than at the previous edition,
• Stephen R. Heller (43 papers) absent at the last edition,
• Andreas Bender (40 papers) published 1 paper at the last edition the same number as at the previous edition,
• Tingjun Hou (39 papers) published 2 papers at the last edition, 2 less than at the previous edition.

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.

Top affiliations and change over time

Only papers with recognized affiliations are considered

The top affiliations publishing in Journal of Chemical Information and Modeling (based on the number of publications) are:

• University of Cambridge (103 papers) published 10 papers at the last edition, 6 less than at the previous edition,
• University of Bonn (102 papers) published 1 paper at the last edition the same number as at the previous edition,
• AstraZeneca (91 papers) published 9 papers at the last edition, 2 more than at the previous edition,
• Novartis (87 papers) published 3 papers at the last edition, 3 less than at the previous edition,
• Chinese Academy of Sciences (86 papers) published 8 papers at the last edition, 3 less than at the previous edition.

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.

Publication chance based on affiliation

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, 3.51% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 12.21% were posted by at least one author from the top 10 institutions publishing in the journal. Another 6.21% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 14.78% of all publications and 66.81% were from other institutions.

Returning Authors Index

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.

Returning Institution Index

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.

The experience to innovation index

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:

• Novice - P < 5 or C < 25 (the number of publications less than 5 or the number of citations less than 25),
• Competent - P < 10 or C < 100 (the number of publications less than 10 or the number of citations less than 100),
• Experienced - P < 25 or C < 625 (the number of publications less than 25 or the number of citations less than 625),
• Master - P < 50 or C < 2500 (the number of publications less than 50 or the number of citations less than 2500),
• Star - P ≥ 50 and C ≥ 2500 (both the number of publications greater than 50 and the number of citations greater than 2500).

The chart below illustrates experience levels of first authors in cases of publications with multiple authors.

Career Opportunities for Journal of Chemical Information and Modeling Readers and Contributors

If reading about or contributing to papers in the Journal of Chemical Information and Modeling is your passion, you might be interested in pursuing career paths that allow you to delve deeper into these subjects. For instance, careers in education, such as becoming a high school teacher, could give you the opportunity to inspire the next generation in these exciting scientific fields. One specific career path that might interest you is becoming a high school history teacher in Missouri. Historically, the field of chemistry has had numerous breakthroughs and personalities that shaped the world as we know it. As a history teacher, you can inspire students not only with the fascinating past of chemical research and discovery, but also its potential future seen in fields like Computational Biology, Machine Learning and Artificial intelligence that are frequently covered in the Journal of Chemical Information and Modeling. While the subjects covered in the journal are quite complex, the concepts can be simplified and taught to young minds eager to learn. Inspiring the next generation of scientists could be your calling, and you would be in a unique position to bring these groundbreaking topics into the classroom. This would not only help in educating the students, but also foster the progression of these topical scientific fields.