Embracing the changes and challenges with modern early drug discovery

Area of Science:

• Drug Discovery and Development
• Computational Biology
• Bioinformatics

Background:

• Early drug discovery faces significant challenges in time, cost, and efficacy.
• Advancements in artificial intelligence (AI) and machine learning (ML) offer transformative potential.
• There is a growing need to integrate AI and ML into the drug discovery pipeline.

Purpose of the Study:

• To explore the role and applications of AI and ML in modern early drug discovery.
• To discuss AI/ML applications in drug target identification, compound screening, and biomarker discovery.
• To highlight how AI/ML addresses key challenges in the drug discovery pipeline.

Main Methods:

• A thorough literature search of the PubMed database was conducted.
• Relevant studies on AI/ML models in computational chemistry, systems biology, and data-driven approaches were identified.
• Emphasis was placed on studies addressing data integration, predictive performance, and cost-efficiency.

Main Results:

• AI and ML significantly enhance the accuracy and speed of identifying viable drug candidates.
• Applications span drug target identification, compound screening, and biomarker discovery.
• These technologies offer improved data integration, predictive performance, and cost-efficiency.

Conclusions:

• AI and ML hold revolutionary potential for early drug discovery.
• Successful integration necessitates overcoming challenges in data quality, model interpretability, and fostering interdisciplinary collaboration.
• These advancements are crucial for accelerating the development of new therapeutics.