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Molecular Informatics is a peer-reviewed journal published by Wiley, covering Computational Drug Discovery Methods, Machine Learning in Materials Science, Protein Structure and Dynamics.
| Publisher | Wiley |
|---|---|
| Reference style | Author–year (Chicago) Author–year — (Smith, 2023) in the text Smith, Ada, Ben Jones, and Cara Lee. 2023. "A Representative Article Title." Molecular Informatics 12 (3): 45–58.
Formats any DOI in Molecular Informatics style. No sign-up. |
| Publishes research in | Computational Drug Discovery Methods Machine Learning in Materials Science Protein Structure and Dynamics Genetics, Bioinformatics, and Biomedical Research Click Chemistry and Applications |
| ISSN | 1868-1743 |
| Citation impact (2-yr) | 2.74 |
| h-index | 65 |
| i10-index | 529 |
| Total citations | 26,101 |
| Article processing charge | $3,450 |
| Top institutions publishing here | Centre National de la Recherche Scientifique |
| Journal website | onlinelibrary.wiley.com |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Citations each year’s papers have accumulated so far — the most recent years are still building up.
After nearly five decades "in the making", QSAR modeling has established itself as one of the major computational molecular modeling methodologies. As any mature research discipline, QSAR modeling can be characterized by a collection of well defined protocols and procedures that enable the expert application of the method for exploring and exploiting ever growing collections…
Detailed knowledge of how molecules recognize interaction partners and of the conformational preferences of biomacromolecules is pivotal for understanding biochemical processes. Such knowledge also provides the foundation for the design of novel molecules, as undertaken in pharmaceutical research. Computer-based free energy calculations enable a detailed investigation of the energetic factors that are responsible for molecular…
Artificial neural networks had their first heyday in molecular informatics and drug discovery approximately two decades ago. Currently, we are witnessing renewed interest in adapting advanced neural network architectures for pharmaceutical research by borrowing from the field of "deep learning". Compared with some of the other life sciences, their application in drug discovery is still…
The recently emerged 2019 Novel Coronavirus (SARS-CoV-2) and associated COVID-19 disease cause serious or even fatal respiratory tract infection and yet no approved therapeutics or effective treatment is currently available to effectively combat the outbreak. This urgent situation is pressing the world to respond with the development of novel vaccine or a small molecule therapeutics…
Generative artificial intelligence models present a fresh approach to chemogenomics and de novo drug design, as they provide researchers with the ability to narrow down their search of the chemical space and focus on regions of interest. We present a method for molecular de novo design that utilizes generative recurrent neural networks (RNN) containing long…
