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 candidate discovery. Strides in artificial intelligence (AI) have encouraged its application to multiple dimensions of computer-aided drug design, with increasing application to antibiotic discovery. This review describes AI-facilitated advances in the discovery of both small molecule antibiotics and antimicrobial peptides. Beyond the essential prediction of antimicrobial activity, emphasis is also given to antimicrobial compound representation, determination of drug-likeness traits, antimicrobial resistance, and de novo molecular design. Given the urgency of the antimicrobial resistance crisis, we analyze uptake of open science best practices in AI-driven antibiotic discovery and argue for openness and reproducibility as a means of accelerating preclinical research. Finally, trends in the literature and areas for future inquiry are discussed, as artificially intelligent enhancements to drug discovery at large offer many opportunities for future applications in antibiotic development.

Background With the expansion of soil transmitted helminth (STH) intervention efforts and the corresponding decline in infection prevalence, there is an increased need for sensitive and specific STH diagnostic assays. Previously, through next generation sequencing (NGS)-based identification and targeting of non-coding, high copy-number repetitive DNA sequences, we described the development of a panel of improved quantitative real-time PCR (qPCR)-based assays for the detection of Necator americanus , Ancylostoma duodenale , Ancylostoma ceylanicum , Trichuris trichiura , and Strongyloides stercoralis . However, due to the phenomenon of chromosome diminution, a similar assay based on high copy-number repetitive DNA was not developed for the detection of Ascaris lumbricoides . Recently, the publication of a reference-level germline genome sequence for A . lumbricoides has facilitated our development of an improved assay for this human pathogen of vast global importance. Methodology/Principal findings Repurposing raw DNA sequence reads from a previously published Illumina-generated, NGS-based A . lumbricoides germline genome sequencing project, we performed a cluster-based repeat analysis utilizing RepeatExplorer2 software. This analysis identified the most prevalent repetitive DNA element of the A . lumbricoides germline genome (AGR, Ascaris germline repeat), which was then used to develop an improved qPCR assay. During experimental validation, this assay demonstrated a fold increase in sensitivity of ~3,100, as determined by relative Cq values, when compared with an assay utilizing a previously published, frequently employed, ribosomal internal transcribed spacer (ITS) DNA target. A comparative analysis of 2,784 field-collected samples was then performed, successfully verifying this improved sensitivity. Conclusions/Significance Through analysis of the germline genome sequence of A . lumbricoides , a vastly improved qPCR assay has been developed. This assay, utilizing a high copy-number repeat target found in eggs and embryos (the AGR repeat), will improve prevalence estimates that are fundamental to the programmatic decision-making process, while simultaneously strengthening mathematical models used to examine STH infection rates. Furthermore, through the identification of an optimal target for PCR, future assay development efforts will also benefit, as the identity of the optimized repeat DNA target is likely to remain unchanged despite continued improvement in PCR-based diagnostic technologies.

There is growing interest in local elimination of soil-transmitted helminth (STH) infection in endemic settings. In such settings, highly sensitive diagnostics are needed to detect STH infection. We compared double-slide Kato-Katz, the most commonly used copromicroscopic detection method, to multi-parallel quantitative polymerase chain reaction (qPCR) in 2,799 stool samples from children aged 2-12 years in a setting in rural Bangladesh with predominantly low STH infection intensity. We estimated the sensitivity and specificity of each diagnostic using Bayesian latent class analysis. Compared to double-slide Kato-Katz, STH prevalence using qPCR was almost 3-fold higher for hookworm species and nearly 2-fold higher for Trichuris trichiura. Ascaris lumbricoides prevalence was lower using qPCR, and 26% of samples classified as A. lumbricoides positive by Kato-Katz were negative by qPCR. Amplicon sequencing of the 18S rDNA from 10 samples confirmed that A. lumbricoides was absent in samples classified as positive by Kato-Katz and negative by qPCR. The sensitivity of Kato-Katz was 49% for A. lumbricoides, 32% for hookworm, and 52% for T. trichiura; the sensitivity of qPCR was 79% for A. lumbricoides, 93% for hookworm, and 90% for T. trichiura. Specificity was � 97% for both tests for all STH except for Kato-Katz for A. lumbricoides (specificity = 68%). There were moderate negative, monotonic correlations between qPCR cycle quantification values and eggs per gram quantified by Kato-Katz. While it is widely assumed that double-slide Kato-Katz has few false positives, our results indicate otherwise and highlight inherent limitations of the Kato-Katz technique. qPCR had higher sensitivity than Kato-Katz in this low intensity infection setting.