학술논문
Machine learning for antimicrobial peptide identification and design
Document Type
Review Paper
Source
Nature Reviews Bioengineering. 2(5):392-407
Subject
Language
English
ISSN
2731-6092
Abstract
Artificial intelligence (AI) and machine learning (ML) models are being deployed in many domains of society and have recently reached the field of drug discovery. Given the increasing prevalence of antimicrobial resistance, as well as the challenges intrinsic to antibiotic development, there is an urgent need to accelerate the design of new antimicrobial therapies. Antimicrobial peptides (AMPs) are therapeutic agents for treating bacterial infections, but their translation into the clinic has been slow owing to toxicity, poor stability, limited cellular penetration and high cost, among other issues. Recent advances in AI and ML have led to breakthroughs in our abilities to predict biomolecular properties and structures and to generate new molecules. The ML-based modelling of peptides may overcome some of the disadvantages associated with traditional drug discovery and aid the rapid development and translation of AMPs. Here, we provide an introduction to this emerging field and survey ML approaches that can be used to address issues currently hindering AMP development. We also outline important limitations that can be addressed for the broader adoption of AMPs in clinical practice, as well as new opportunities in data-driven peptide design.
Artificial intelligence (AI) and machine learning (ML) are reshaping antibiotic discovery. In this Review, ML approaches that have been and can be used to address issues hindering antimicrobial peptide identification and development are surveyed.
Key points: Machine learning (ML) can aid antimicrobial peptide (AMP) design and discovery. It can be applied to improve drug efficacy, predict medicinal chemistry and reduce the overall time and cost of drug development.ML can be used for the prediction of therapeutic properties — such as antimicrobial efficacy, and absorption, distribution, metabolism, excretion and toxicity (ADMET) — and macromolecular structures.Deep generative models are promising approaches to designing new AMPs.Important limitations in AMP development include lack of selectivity, undesirable physicochemical and medicinal chemistry properties, unspecific or unknown mechanisms of action, high cost of peptide synthesis, and generation of industrial waste. ML can help to overcome these limitations by applying relevant models trained on high-quality datasets.
Artificial intelligence (AI) and machine learning (ML) are reshaping antibiotic discovery. In this Review, ML approaches that have been and can be used to address issues hindering antimicrobial peptide identification and development are surveyed.
Key points: Machine learning (ML) can aid antimicrobial peptide (AMP) design and discovery. It can be applied to improve drug efficacy, predict medicinal chemistry and reduce the overall time and cost of drug development.ML can be used for the prediction of therapeutic properties — such as antimicrobial efficacy, and absorption, distribution, metabolism, excretion and toxicity (ADMET) — and macromolecular structures.Deep generative models are promising approaches to designing new AMPs.Important limitations in AMP development include lack of selectivity, undesirable physicochemical and medicinal chemistry properties, unspecific or unknown mechanisms of action, high cost of peptide synthesis, and generation of industrial waste. ML can help to overcome these limitations by applying relevant models trained on high-quality datasets.