Drug Discovery Today Technologies Template
Write in a clean editor, then format for Drug Discovery Today Technologies in one click — DocuGuru applies the official Elsevier template with numbered references and exports a submission-ready PDF plus the editable LaTeX source. Free to start.
About the Drug Discovery Today Technologies format
Drug Discovery Today Technologies is a peer-reviewed journal published by Elsevier, covering Computational Drug Discovery Methods, Monoclonal and Polyclonal Antibodies Research, Chemical Synthesis and Analysis.
| Publisher | Elsevier |
|---|---|
| Reference style | Numbered (Elsevier) Numbered — [1], [2] in the text [1] A. Smith, B. Jones, C. Lee, A representative article title, Drug Discovery Today Technologies 12 (2023) 45–58.
Formats any DOI in Drug Discovery Today Technologies style. No sign-up. |
| Publishes research in | Computational Drug Discovery Methods Monoclonal and Polyclonal Antibodies Research Chemical Synthesis and Analysis Receptor Mechanisms and Signaling Pharmacogenetics and Drug Metabolism |
| ISSN | 1740-6749 |
| h-index | 76 |
| i10-index | 431 |
| Total citations | 29,763 |
| Top institutions publishing here | Vrije Universiteit Amsterdam |
| Journal website | www.journals.elsevier.com |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Papers published in Drug Discovery Today Technologies per year
Citation impact of Drug Discovery Today Technologies by publication year
Citations each year’s papers have accumulated so far — the most recent years are still building up.
Most-cited papers in Drug Discovery Today Technologies
The majority of currently used therapeutics are small molecule-based and utilize occupancy-driven pharmacology as the mode of action (MOA), in which the protein function is modulated via temporary inhibition. New modalities that operate using alternative MOAs are essential for tapping into the "undruggable" proteome. The PROteolysis Targeting Chimera (PROTAC) technology provides an attractive new approach…
As graph neural networks are becoming more and more powerful and useful in the field of drug discovery, many pharmaceutical companies are getting interested in utilizing these methods for their own in-house frameworks. This is especially compelling for tasks such as the prediction of molecular properties which is often one of the most crucial tasks…