Current advancement in AI-integrated drug discovery: Methods and applications

Area of Science:

• Computational Biology
• Bioinformatics
• Artificial Intelligence

Background:

• Artificial intelligence (AI) adoption is rapidly increasing across scientific disciplines, including human healthcare.
• AI's interdisciplinary nature facilitates its application in diverse fields, notably in drug development.
• Advancements in AI models and computer hardware are enhancing precision and predictive capabilities in scientific research.

Purpose of the Study:

• To review recent advancements in Artificial Intelligence (AI) applications within the drug discovery and development pipeline.
• To provide an overview of AI models, architectures, and tools utilized in drug discovery.
• To discuss the prospects, challenges, and limitations of AI-driven drug discovery.

Main Methods:

• Exploration of AI models and architectures commonly employed in drug discovery.
• Review of state-of-the-art AI tools for data analysis and functional annotation.
• Analysis of AI applications in virtual screening and clinical trial optimization.

Main Results:

• AI models are increasingly utilized for improving predictions and parameters in drug discovery techniques.
• AI tools are enhancing various stages of the drug discovery workflow, from initial analysis to clinical trials.
• The review highlights the growing impact of AI on accelerating the identification and development of new drugs.

Conclusions:

• AI presents significant opportunities to innovate and improve various subtopics within drug discovery and development.
• Continued research and development in AI are crucial for overcoming existing challenges and limitations in the field.
• The integration of AI is poised to significantly advance the efficiency and success rate of therapeutic development.