Machine‐Learning Techniques Applied to Antibacterial Drug Discovery

@article{Durrant2015MachineLearningTA,
  title={Machine‐Learning Techniques Applied to Antibacterial Drug Discovery},
  author={Jacob D. Durrant and Rommie E. Amaro},
  journal={Chemical Biology \& Drug Design},
  year={2015},
  volume={85}
}
The emergence of drug‐resistant bacteria threatens to revert humanity back to the preantibiotic era. Even now, multidrug‐resistant bacterial infections annually result in millions of hospital days, billions in healthcare costs, and, most importantly, tens of thousands of lives lost. As many pharmaceutical companies have abandoned antibiotic development in search of more lucrative therapeutics, academic researchers are uniquely positioned to fill the pipeline. Traditional high‐throughput screens… 

Machine Learning in Antibacterial Drug Design

TLDR
This review focuses on the latest machine learning approaches used in the discovery of new antibacterial agents and targets, covering both small molecules and antibacterial peptides.

The application of machine learning techniques to innovative antibacterial discovery and development

TLDR
This review covers some of the applications of MLT in medicinal chemistry, focusing on the development of new antibiotics, the prediction of resistance and its mechanisms, and the main advantages and disadvantages and the major trends from studies over the past 5 years.

Accelerating antibiotic discovery through artificial intelligence

TLDR
This review describes AI-facilitated advances in the discovery of both small molecule antibiotics and antimicrobial peptides and analyzes uptake of open science best practices in AI-driven antibiotic discovery and argues for openness and reproducibility as a means of accelerating preclinical research.

Heterologous Machine Learning for the Identification of Antimicrobial Activity in Human-Targeted Drugs

TLDR
Heterologous machine learning rendered an efficient computational approach to classify antimicrobial compounds, and this classification model was tested on the latest human-approved drugs expecting to identify antibiotics with broad-spectrum activity and results show that the model rendered predictions consistent with current knowledge about broad-Spectrum antibiotics.

Deep Neural Networks in the Discovery of Novel Antibiotics Drug Molecule: A Review

TLDR
The use of the artificial intelligence techniques in the antibiotics discovery will be reviewed with focus on the deep neural networks model as compared with other methods.

A Machine Learning Tool to Predict the Antibacterial Capacity of Nanoparticles

TLDR
This tool assists various stakeholders and scientists in predicting the antibacterial effects of NPs based on their p-chem properties and diverse exposure settings and aids the safe-by-design paradigm by incorporating functionality tools.

Machine learning: novel bioinformatics approaches for combating antimicrobial resistance

TLDR
Application of machine learning to studying AMR is feasible but remains limited, and future applications are likely to be laboratory-based, such as antimicrobial susceptibility phenotype prediction.

Screening of antibacterial compounds with novel structure from the FDA approved drugs using machine learning methods

TLDR
An antibacterial compound predictor was constructed using the support vector machines and random forest methods and the data of the active and inactive antibacterial compounds from the ChEMBL database and showed that both models have excellent prediction performance.

Neural-Network Scoring Functions Identify Structurally Novel Estrogen-Receptor Ligands

TLDR
The estrogen receptor is used to demonstrate that neural networks are adept at identifying structurally novel small molecules that bind to a selected drug target, ultimately allowing experimentalists to test fewer compounds in the earliest stages of lead identification while obtaining higher hit rates.

References

SHOWING 1-10 OF 65 REFERENCES

Identification of novel antibacterial peptides by chemoinformatics and machine learning.

TLDR
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.

Use of artificial intelligence in the design of small peptide antibiotics effective against a broad spectrum of highly antibiotic-resistant superbugs.

TLDR
The best peptides, representing the top quartile of predicted activities, were effective against a broad array of multidrug-resistant "Superbugs" with activities that were equal to or better than four highly used conventional antibiotics.

Why is big Pharma getting out of antibacterial drug discovery?

  • S. Projan
  • Political Science
    Current opinion in microbiology
  • 2003

Drugs for bad bugs: confronting the challenges of antibacterial discovery

TLDR
The experience of evaluating more than 300 genes and 70 high-throughput screening campaigns over a period of 7 years is shared, and what is learned is looked at and how that has influenced GlaxoSmithKline's antibacterials strategy going forward.

Simultaneous virtual prediction of anti-Escherichia coli activities and ADMET profiles: A chemoinformatic complementary approach for high-throughput screening.

TLDR
The first multitasking model based on quantitative-structure biological effect relationships (mtk-QSBER) for simultaneous virtual prediction of anti-E.

Prediction of Antimicrobial Activity of Synthetic Peptides by a Decision Tree Model

TLDR
The development of synthetic peptides with antimicrobial activity, created in silico by site-directed mutation modeling using wild-type peptides as scaffolds for these mutations, are described.

Successful applications of computer aided drug discovery: moving drugs from concept to the clinic.

TLDR
This review is focused on the clinical status of experimental drugs that were discovered and/or optimized using computer-aided drug design and 12 small molecules that are in clinical trial or have become approved for therapeutic use.

Simultaneous modeling of antimycobacterial activities and ADMET profiles: a chemoinformatic approach to medicinal chemistry.

TLDR
This work introduces the first multitasking model based on quantitative-structure biological effect relationships (mtk-QSBER) for simultaneous prediction of antimycobacterial activities and ADMET profiles of drugs/chemicals under diverse experimental conditions and demonstrates that the present mtk-QsBER model can be used for virtual screening of safer antimyCobacterial agents.

Hierarchical virtual screening for the discovery of new molecular scaffolds in antibacterial hit identification

TLDR
A novel computational methodology is presented that is able to identify a high proportion of structurally diverse inhibitors by searching unusually large molecular databases in a time-, cost- and resource-efficient manner and discovered over 50 new active molecular scaffolds that underscore the benefits that a wide application of prospectively validated in silico screening tools is likely to bring to antibacterial hit identification.
...