Artificial Intelligence and Machine Learning Technology Driven Modern Drug Discovery and Development.

Area of Science:

• Pharmaceutical Sciences
• Computational Chemistry
• Biotechnology

Background:

• Drug discovery is a lengthy, expensive process, typically taking 12 years and costing billions.
• Traditional methods face challenges in efficiency and cost-effectiveness.
• Advancements in data science and computing power have created opportunities for innovation.

Purpose of the Study:

• To review the role and impact of Artificial Intelligence (AI), particularly deep learning (DL), in accelerating and optimizing drug discovery.
• To highlight how AI and DL are transforming medicinal chemistry and pharmaceutical development.
• To discuss the integration of AI with modern experimental techniques for more efficient pharmaceutical research.

Main Methods:

• Utilizing machine learning (ML) and deep learning (DL) algorithms for computer-aided drug discovery.
• Leveraging large datasets and enhanced computing power for AI-driven analysis.
• Employing advanced DL techniques like artificial neural networks (ANNs) for automatic feature extraction and nonlinear relationship identification.

Main Results:

• AI and DL are overcoming previous reluctance, significantly advancing medicinal chemistry.
• DL methods demonstrate superior capabilities over classical ML by automatically extracting features and identifying complex molecular relationships.
• Emerging AI technologies show promise in addressing key drug discovery challenges.

Conclusions:

• AI, especially DL, is poised to significantly improve the speed, cost-effectiveness, and success rate of drug discovery.
• Future advancements in AI, including novel ML paradigms and open data sharing, will be crucial for tackling complex pharmaceutical research questions.
• The integration of AI with experimental methods heralds a new era in the quest for novel therapeutics.