Journal of Artificial Intelligence and Digital Health
ISSN:
3139-6267 (Online)
DOI Prefix (Crossref): 10.67238
AI in Drug Discovery
AI in Drug Discovery uses machine learning, deep learning, and predictive modeling to accelerate the identification and development of new medicines. AI analyzes vast datasets—including chemical structures, biological targets, and clinical outcomes—to predict drug–target interactions and optimize compound design. It reduces the time and cost of traditional drug development by quickly screening potential molecules and identifying promising candidates. AI also supports repurposing existing drugs, simulating drug behavior, and improving toxicity prediction. By enhancing accuracy and innovation, AI-driven drug discovery is transforming pharmaceutical research and enabling faster development of effective, personalized treatments.
Article Processing Timeline
| 2-5 Days | Initial Quality & Plagiarism Check |
| 25-35 Days |
Peer Review Feedback |
| 45-60 Days | Total article processing time |
EDITORIAL & PUBLICATION INFORMATION
| English | Publication Language |
| Single-Blind | Peer-Review Model |
| 17% | Acceptance Rate |
| <18% | Similarity Screening Guideline |
| Open Access | Access Model |
All manuscripts undergo editorial assessment and originality screening as part of the journal's evaluation process. The acceptance rate shown is based on journal-level editorial data and may change over time. Similarity reports are assessed editorially and are not interpreted solely on the basis of a numerical similarity score.
Why Publish with us?
Ethics & Policies
Editorial & Management
Useful Links
Journal Highlights
Open Access Journals
Journal Flyer
