Antibacterial Drug Discovery: Deep Learning Successes and Challenges through the Structural Biology Lens.

Area of Science:

• Structural biology
• Computational biology
• Drug discovery

Background:

• Multidrug-resistant pathogens are a growing threat, necessitating new antibiotics.
• Traditional antibiotic discovery faces challenges, with high failure rates due to toxicity and lack of efficacy.
• Microbiological hurdles include selective targeting, cell envelope penetration, and overcoming resistance mechanisms.

Purpose of the Study:

• To review the application of deep learning (DL) in antibacterial hit discovery.
• To provide a structural biology perspective on integrating DL tools into antibiotic discovery workflows.
• To offer a roadmap for combating antimicrobial resistance using advanced computational methods.

Main Methods:

• Analysis of deep learning applications in identifying bacterial targets.
• Evaluation of DL for predicting 3D structures and assessing druggability.
• Review of generative models for de novo design of antibiotic candidates.
• Examination of DL's role in streamlining screening and improving efficiency.

Main Results:

• Deep learning can identify novel bacterial targets and potential lead molecules.
• Generative models enable de novo design with optimized pharmacokinetics and safety.
• DL accelerates the drug discovery pipeline, addressing toxicity and efficacy issues.
• Effective DL application requires appropriate models, quality data, and careful interpretation.

Conclusions:

• Deep learning presents a powerful approach to accelerate antibacterial drug discovery.
• Integrating DL with structural biology can overcome key microbiological challenges.
• Judicious use of DL is essential for developing effective new antibiotics against resistant pathogens.