Piquing artificial intelligence towards drug discovery: Tools, techniques, and applications

Area of Science:

• Computational chemistry
• Pharmacology
• Drug discovery

Background:

• Traditional drug development is lengthy and expensive.
• Artificial intelligence (AI) offers transformative potential for optimizing drug discovery pipelines.
• Data-driven approaches are increasingly vital in pharmaceutical research.

Purpose of the Study:

• To review the impact of artificial intelligence (AI) methods on drug development.
• To explore how AI models and data resources enhance the efficiency and cost-effectiveness of drug discovery.
• To highlight AI's role in accelerating the creation of novel therapeutics.

Main Methods:

• Overview of drug development information sources.
• Discussion of molecular representation techniques for AI compatibility.
• Review of AI algorithms for drug discovery model creation.
• Exploration of AI applications in predicting drug toxicity, bioactivity, and properties.
• Detailed examination of AI models for de novo drug design and drug-target interactions.
• Coverage of AI in nanomedicine and synergistic/antagonistic effect prediction.

Main Results:

• AI significantly enhances the prediction of drug properties, toxicity, and bioactivity.
• AI models demonstrate efficacy in de novo drug design and predicting drug-target interactions.
• AI facilitates advanced applications such as nanomedicine design and pharmacological synergy prediction.
• AI integration offers more affordable and expedient alternatives to conventional drug development.

Conclusions:

• AI has the potential to dramatically accelerate and improve the efficiency of drug discovery.
• AI facilitates the development of new and improved medicines.
• Further research and development are necessary to fully leverage AI's capabilities in drug discovery and overcome existing challenges.