Antibiotic discovery in the artificial intelligence era

As the global burden of antibiotic resistance continues to grow, creative approaches to antibiotic discovery are needed to accelerate the development of novel medicines. A rapidly progressing computational revolution-artificial intelligence-offers an optimistic path forward due to its ability to alleviate bottlenecks in the antibiotic discovery pipeline. In this review, we discuss how advancements in artificial intelligence are reinvigorating the adoption of past antibiotic discovery models-namely natural product exploration and small molecule screening. We then explore the application of contemporary machine learning approaches to emerging areas of antibiotic discovery, including antibacterial systems biology, drug combination development, antimicrobial peptide discovery, and mechanism of action prediction. Lastly, we propose a call to action for open access of high-quality screening datasets and interdisciplinary collaboration to accelerate the rate at which machine learning models can be trained and new antibiotic drugs can be developed.

A brief guide to machine learning for antibiotic discovery

Rising antibiotic resistance and an alarmingly lean antibiotic pipeline require the adoption of novel approaches to rapidly discover new structural and functional classes of antibiotics. Excitingly, algorithmic approaches to antibiotic discovery are sufficiently advanced to meaningfully influence the antibiotic discovery process. Indeed, once trained on high-quality datasets, contemporary machine-learning and deep-learning models can be used to perform predictions for new antibiotics across vast chemical spaces, orders of magnitude more rapidly than compounds can be screened in the laboratory. This increases the probability of discovering new antibiotics with desirable properties. In this short review, we briefly describe the utility of contemporary machine-learning and deep-learning approaches to guide the discovery of new small-molecule antibiotics and unidentified natural products. We then propose a call to action for more open sharing of high-quality screening datasets to accelerate the rate at which forthcoming antibiotic-prediction models can be trained. Together, we aim to introduce antibiotic discoverers to a sample of recent applications of contemporary algorithmic methods to facilitate the wider adoption of these powerful computational approaches.

Identification of new building blocks by fragment screening for discovering GyrB inhibitors

DNA gyrase is an essential DNA topoisomerase that exists only in bacteria. Since novobiocin was withdrawn from the market, new scaffolds and new mechanistic GyrB inhibitors are urgently needed. In this study, we employed fragment screening and X-ray crystallography to identify new building blocks, as well as their binding mechanisms, to support the discovery of new GyrB inhibitors. In total, 84 of the 618 chemical fragments were shown to either thermally stabilize the ATPase domain of Escherichia coli GyrB or inhibit the ATPase activity of E. coli gyrase. Among them, the IC₅₀ values of fragments 10 and 23 were determined to be 605.3 μM and 446.2 μM, respectively. Cocrystal structures of the GyrB ATPase domain with twelve fragment hits were successfully determined at a high resolution. All twelve fragments were deeply inserted in the pocket and formed H-bonds with Asp73 and Thr165, and six fragments formed an additional H-bond with the backbone oxygen of Val71. Fragment screening further highlighted the capability of Asp73, Thr165 and Val71 to bind chemicals and provided diverse building blocks for the design of GyrB inhibitors.

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

INTRODUCTION

After the initial wave of antibiotic discovery, few novel classes of antibiotics have emerged, with the latest dating back to the 1980's. Furthermore, the pace of antibiotic drug discovery is unable to keep up with the increasing prevalence of antibiotic drug resistance. However, the increasing amount of available data promotes the use of machine learning techniques (MLT) in drug discovery projects (e.g. construction of regression/classification models and ranking/virtual screening of compounds).

AREAS COVERED

In this review, the authors cover some of the applications of MLT in medicinal chemistry, focusing on the development of new antibiotics, the prediction of resistance and its mechanisms. The aim of this review is to illustrate the main advantages and disadvantages and the major trends from studies over the past 5 years.

EXPERT OPINION

The application of MLT to antibacterial drug discovery can aid the selection of new and potent lead compounds, with desirable pharmacokinetic and toxic profiles for further optimization. The increasing volume of available data along with the constant improvement in computational power and algorithms has meant that we are experiencing a transition in the way we face modern issues such as drug resistance, where our decisions are data-driven and experiments can be focused by data-suggested hypotheses.

Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery

Artificial intelligence (AI), and, in particular, deep learning as a subcategory of AI, provides opportunities for the discovery and development of innovative drugs. Various machine learning approaches have recently (re)emerged, some of which may be considered instances of domain-specific AI which have been successfully employed for drug discovery and design. This review provides a comprehensive portrayal of these machine learning techniques and of their applications in medicinal chemistry. After introducing the basic principles, alongside some application notes, of the various machine learning algorithms, the current state-of-the art of AI-assisted pharmaceutical discovery is discussed, including applications in structure- and ligand-based virtual screening, de novo drug design, physicochemical and pharmacokinetic property prediction, drug repurposing, and related aspects. Finally, several challenges and limitations of the current methods are summarized, with a view to potential future directions for AI-assisted drug discovery and design.

Identification of an auxiliary druggable pocket in the DNA gyrase ATPase domain using fragment probes

Discovery of new drug binding sites on well-established targets is of great interest as it facilitates the design of new mechanistic inhibitors to overcome the acquired drug resistance. Small chemical fragments can easily enter and bind to the cavities on the protein surface. Thus, they can be used to probe new druggable pockets in proteins. DNA gyrase plays indispensable roles in DNA replication, and both its GyrA and GyrB subunits are clinically validated antibacterial targets. New mechanistic GyrB inhibitors are urgently desired since the withdrawal of novobiocin from the market by the FDA due to its reduced efficiency and other reasons. Here, a fragment library was screened against the E. coli GyrB ATPase domain by combining affinity- and bioactivity-based approaches. The following X-ray crystallographic efforts were made to determine the cocrystal structures of GyrB with ten fragment hits, and three different binding modes were disclosed. Fortunately, a hydrophobic pocket which is previously unknown was identified by two fragments. Fragments that bind to this pocket were shown to inhibit the ATPase activity as well as the DNA topological transition activity of DNA gyrase in vitro. A set of fragment analogs were screened to explore the binding capacity of this pocket and identify the better starting fragments for lead development. Phylogenetic analysis revealed that this pocket is conserved in most Gram-negative and also many Gram-positive human pathogenic bacteria, implying a broad-spectrum antibacterial potential and a lower risk of mutation. Thus, the novel druggable pocket and the starting fragments provide a novel basis for designing new GyrB-targeting therapeutics.