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npj Drug Discovery is a peer-reviewed journal published by Springer Nature, covering Computational Drug Discovery Methods, Protein Structure and Dynamics, Monoclonal and Polyclonal Antibodies Research. Over its lifetime it has published 50 papers, which have received 260 citations.
| Publisher | Springer Nature |
|---|---|
| Reference style | Superscript numbered (Nature) Superscript — small raised numerals in the text 1. Smith, A., Jones, B. & Lee, C. A representative article title. npj Drug Discovery 12, 45–58 (2023).
Formats any DOI in npj Drug Discovery style. No sign-up. |
| Publishes research in | Computational Drug Discovery Methods Protein Structure and Dynamics Monoclonal and Polyclonal Antibodies Research Receptor Mechanisms and Signaling Pharmacogenetics and Drug Metabolism |
| ISSN | 3005-1452 |
| Citation impact (2-yr) | 5.2 |
| h-index | 10 |
| i10-index | 10 |
| Open access | Yes — indexed in DOAJ |
| Top institutions publishing here | QB3 |
| Journal website | link.springer.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.
The roles of computing in structure-based drug discovery are considered, from early studies based on some of the first experimental structures of enzyme-inhibitor complexes, through the use of advanced molecular dynamics simulations and machine learning methods. This perspective aims to explore the history, current trends, and future directions of these methodologies.
The rapid evolution of molecular representation methods has significantly advanced the drug discovery process. Advances in language models, graph-based representations, and novel learning strategies have greatly improved the ability to characterize molecules. These AI-driven strategies extend beyond traditional structural data, facilitating exploration of broader chemical spaces and accelerating scaffold hopping. This review summarizes key advancements,…
This perspective examines how artificial intelligence (AI) is transforming small-molecule development for precision cancer immunomodulation therapy. It outlines AI-driven approaches for de novo design, virtual screening, multi-parameter optimization, and ADMET prediction, targeting immune checkpoints, tumor microenvironment modulation, antigen presentation, and metabolic pathways. The article highlights patient stratification, multi-omics integration, digital twin simulations, translational challenges, and…
G protein-coupled receptors (GPCRs) are a prominent class of therapeutic targets for which structure-based drug discovery (SBDD) has traditionally been challenging to apply. However, recent artificial intelligence (AI)-powered breakthroughs have opened new avenues. Here, we discuss the impact of computational models on hit discovery and lead optimization for GPCRs. We also provide best practices for…
ITK is a kinase involved in T cell activation, proliferation and differentiation. In mice, selective knock-out of the ITK gene produces Th1 skewing of T helper cell differentiation. Soquelitinib, a covalent ITK inhibitor, blocks ITK activity with greater than 100-fold selectivity compared to inhibition of a related kinase, RLK. We describe the chemistry and biologic…
