Applications of machine learning in computer-aided drug discovery

Area of Science:

• Computational chemistry and cheminformatics
• Artificial intelligence in drug discovery

Background:

• Machine learning (ML) is transforming structure-based drug design (SBDD).
• Deep learning (DL), a subset of ML, excels at identifying complex patterns in experimental data.
• DL is increasingly adopted in SBDD for its sophisticated analytical capabilities.

Purpose of the Study:

• To review recent trends in deep learning (DL) applications within structure-based drug design (SBDD).
• To focus on key DL applications including de novo drug design, binding site prediction, and binding affinity prediction.

Main Methods:

• Literature review of recent advancements in DL for SBDD.
• Analysis of DL methodologies applied to specific SBDD tasks.
• Synthesis of findings related to de novo design, binding site, and binding affinity prediction.

Main Results:

• DL models demonstrate significant potential in enhancing SBDD processes.
• Recent DL trends show promise in de novo drug design, improving efficiency and novelty.
• DL applications for predicting binding sites and affinities are becoming more accurate and reliable.

Conclusions:

• Deep learning is a powerful tool revolutionizing structure-based drug design.
• Continued research in DL for SBDD is expected to accelerate the discovery of novel therapeutics.
• DL's ability to analyze complex patterns offers significant advantages in predicting molecular interactions and properties.