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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.

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2-5 Days Initial Quality & Plagiarism Check
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Days		 Peer Review Feedback
85% Acceptance Rate (after peer review)
30-45 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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