Current methods and challenges for deep learning in drug discovery

Area of Science:

• Computational chemistry
• Biotechnology
• Machine learning

Background:

• Deep learning (DL) has transformed computational disciplines due to hardware and data advances.
• DL offers novel neural architectures for representing chemical and biological data in drug design.
• Existing DL methods have advanced biochemical prediction but face challenges in complex drug discovery projects.

Purpose of the Study:

• To review the impact and challenges of deep learning in computer-aided drug design (CADD).
• To highlight the potential of DL in transforming drug discovery pipelines.
• To identify limitations and suggest future directions for DL in CADD.

Main Methods:

• Review of deep learning architectures applied to chemistry and biology.
• Analysis of DL's capabilities in molecule discrimination and generation.
• Assessment of DL performance in biochemical prediction tasks.

Main Results:

• DL has developed versatile neural networks for molecular representation and generation.
• State-of-the-art results achieved in some biochemical prediction tasks.
• Significant challenges remain due to limited quantity and quality of drug discovery data.

Conclusions:

• Deep learning holds transformative potential for computer-aided drug design.
• Addressing data limitations is crucial for advancing DL in complex drug discovery.
• More effective utilization of existing data sources is needed to fully leverage DL.