Drug Discovery in the Age of Artificial Intelligence: Transformative Target-Based Approaches

Area of Science:

• Computational chemistry and pharmacology
• Artificial intelligence in medicine
• Drug discovery and development

Background:

• Drug development faces challenges in accuracy, speed, and efficiency, often limiting success.
• Machine learning (ML) is increasingly impacting target-based drug discovery, especially for small-molecule approaches.
• Simplified Molecular Input Line Entry System (SMILES) is a key tool for representing chemical structures in ML applications.

Purpose of the Study:

• To review the significant impact of recent machine learning developments on target-based drug discovery.
• To highlight how ML enhances various stages of the drug discovery pipeline, from lead identification to optimization.
• To discuss the role of deep learning and fragment-based approaches in accelerating drug design.

Main Methods:

• Utilizing machine learning and natural language processing with SMILES for drug design, mining, and repurposing.
• Applying deep learning models, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs), for virtual screening and target identification.
• Employing ML algorithms and generative adversarial networks (GANs) in fragment-based and structure-based drug design.

Main Results:

• ML models improve the accuracy of predicting binding affinity and selectivity, reducing experimental screening needs.
• Deep learning shows promise for virtual screening, target identification, and de novo drug design.
• ML accelerates hit selection and design optimization in fragment-based and structure-based approaches.

Conclusions:

• Machine learning is transforming target-based drug discovery, enhancing efficiency and innovation.
• Despite challenges like interpretability and data quality, ML's impact is significant and growing.
• ML holds substantial potential for developing novel and improved therapeutics for various diseases.