About the Journal
ISSN: pending registration. Founding Editorial Board: recruitment in progress.
The Journal of AI-Driven Drug Discovery (JAIDD) is a peer-reviewed, open-access journal advancing the application of artificial intelligence across the drug discovery pipeline.
The convergence of deep learning, structural biology, and high-throughput screening is transforming how medicines are discovered. From AlphaFold-enabled target identification to generative models for molecular design, AI approaches are compressing timelines and reducing costs. JAIDD provides the rigorous, open-access venue this rapidly evolving field requires.
JAIDD serves computational chemists, structural biologists, pharmacologists, AI researchers, and drug discovery scientists in academia, biotechnology, and the pharmaceutical industry. Published by Vallensis Publishing, Switzerland. Follows the COPE Core Practices; an application for COPE membership has been submitted.
Aims & Scope
JAIDD publishes original research, reviews, methodological papers, and technical notes on AI-driven drug discovery, molecular design, and computational pharmacology. Topics of interest include:
- Structure-based drug design using AI
- Generative models for molecular design (VAEs, GANs, diffusion models, transformers)
- Protein structure prediction and validation (AlphaFold2/3 and successors)
- De novo protein design and engineering
- Molecular docking and free energy perturbation
- AI-driven ADMET prediction and optimisation
- AI for target identification and validation
- Drug repurposing and repositioning using AI
- Protein-protein interaction prediction and modulation
- Computational antibody and biologics design
- AI-assisted synthesis planning and retrosynthesis
- Machine learning force fields and molecular dynamics
- Benchmarking studies and reproducibility in AI-driven drug discovery
Out of scope: purely synthetic chemistry without an AI/computational component, clinical pharmacology, and animal studies without computational methodology.
