Journal of Artificial Intelligence and Digital Health
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 |
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Journal Highlights
Artificial Intelligence in Healthcare
Machine Learning and Deep Learning
Medical Imaging and Diagnostics
Predictive Analytics
Digital Health Technologies
Telemedicine and mHealth
Wearable Devices and Remote Monitoring
Health Informatics and EHR
Big Data in Medicine
Robotics and Automation
AI in Drug Discovery
Healthcare Cybersecurity
Smart Hospital Systems
Clinical Decision Support Systems
Natural Language Processing
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