Accelerating antibiotic discovery through artificial intelligence

  title={Accelerating antibiotic discovery through artificial intelligence},
  author={Marcelo C. R. Melo and Jacqueline R. M. A. Maasch and C{\'e}sar de la Fuente-Nunez},
  journal={Communications Biology},
By targeting invasive organisms, antibiotics insert themselves into the ancient struggle of the host-pathogen evolutionary arms race. As pathogens evolve tactics for evading antibiotics, therapies decline in efficacy and must be replaced, distinguishing antibiotics from most other forms of drug development. Together with a slow and expensive antibiotic development pipeline, the proliferation of drug-resistant pathogens drives urgent interest in computational methods that promise to expedite… 
Application of Artificial Intelligence in Combating High Antimicrobial Resistance Rates
To combat the rise in AMR rates, it is critical to implement an institutional antibiotic stewardship program that monitors correct antibiotic use, controls antibiotics, and generates antibiograms, and these types of tools may aid in the treatment of patients in the event of a medical emergency.
Biological Dark Matter Exploration using Data Mining for the Discovery of Antimicrobial Natural Products.
This review analyzes the state-of-the-art for data mining in the fields of bacteria, fungi, and plant genomic data, as well as metabologenomics and summarizes some of the most recent research accomplishments in the field.
Expanding the search for small-molecule antibacterials by multidimensional profiling
How multidimensional small-molecule profiling and the ever-increasing computing power are accelerating the discovery of unconventional antibacterials capable of bypassing resistance and exploiting synergies with established antibacterial treatments and with protective host mechanisms is discussed.
dbAMP 2.0: updated resource for antimicrobial peptides with an enhanced scanning method for genomic and proteomic data
An efficient online tool is launched that can effectively identify AMPs from genome/metagenome and proteome data of all species in a short period and promotes the dbAMP as one of the most abundant and comprehensively annotated resources for AMPs.
Traditional and Computational Screening of Non-Toxic Peptides and Approaches to Improving Selectivity
The development of in silico predictive approaches to peptide toxicity has just started, but their important contributions clearly demonstrate their potential for peptide science and computer-aided drug design.
Phloroglucinol and Its Derivatives: Antimicrobial Properties toward Microbial Pathogens.
This review focuses on the use of PG and its derivatives to control microbial infection and the underlying mechanism of action and some of the various alternative strategies, such as the use in conjugation, nanoformulation, antibiotic combination, and encapsulation are thoroughly discussed.
Deep generative models for peptide design
This review discusses several popular deep generative model frameworks as well as their applications to generate peptides with various kinds of properties, and discusses a discussion of current limitations and future perspectives in this emerging field.
Serverless Prediction of Peptide Properties with Recurrent Neural Networks
We present three deep learning sequence prediction models for hemolysis, solubility, and resistance to non-specific interactions of peptides that achieve comparable results to the state-of-the art


Toward computer-made artificial antibiotics.
Toward Autonomous Antibiotic Discovery.
Computer-made drugs may enable the discovery of unprecedented functions in biological systems and help replenish the authors' arsenal of effective antibiotics.
Identification of novel antibacterial peptides by chemoinformatics and machine learning.
The best peptides identified through screening were found to have activities comparable or superior to those of four conventional antibiotics and superior to the peptide most advanced in clinical development against a broad array of multiresistant human pathogens.
Machine learning-powered antibiotics phenotypic drug discovery
It is demonstrated that BPF classification tool can be successfully used to guide chemical structure activity relationship optimization, enabling antibiotic development and that this approach can be fruitfully applied across species.
Machine‐Learning Techniques Applied to Antibacterial Drug Discovery
Two machine‐learning techniques, neural networks and decision trees, that have been used to identify experimentally validated antibiotics are described, and the future directions of this exciting field are described.
Reprogramming biological peptides to combat infectious diseases.
Several new strategies that have been developed to counter pathogenic microorganisms by designing and constructing antimicrobial peptides (AMPs) are outlined.
Machine Learning Algorithm Identifies an Antibiotic Vocabulary for Permeating Gram-Negative Bacteria
This work presents proof-of-concept of an approach to rationally identify a "chemical vocabulary" related to a specific drug activity of interest without employing known rules, and investigates the molecular mechanism behind identified fragments promoting compound entry into Pseudomonas aeruginosa.
Prediction of antibiotic resistance in Escherichia coli from large-scale pan-genome data
It is demonstrated that antibiotic resistance in E. coli can be accurately predicted from whole genome sequences without a priori knowledge of mechanisms, and that both genomic and epidemiological data can be informative.