Applications of machine‐learning methods for the discovery of NDM‐1 inhibitors

Shi, Cheng; Dong, Fanyi; Zhao, Guiling; Zhu, Ning; Lao, Xingzhen; Zheng, Heng

doi:10.1111/cbdd.13708

Cited by 9 publications

(23 citation statements)

References 34 publications

(38 reference statements)

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“…Shi et al collected a database of New Delhi metallo beta-lactamase (NDM-1) inhibitors (511 compounds) from the literature ( Shi et al, 2020 ). This was followed by the calculation of molecular descriptors (34 descriptors, MOE software) and the representation of SMILES strings padded with zeros up to a length of 550.…”

Section: Small Moleculesmentioning

confidence: 99%

“…Upon reviewing literature on novel antibacterial design supported by machine learning, RF models were found to be one of the most commonly applied for classification, regression, and other tasks and represent a performance and computationally lean approach. In this review, a number of RF applications are presented, for small molecules, peptides ( Bhadra et al, 2018 ), natural product–based antibacterial design, and studying antibacterial drug resistance ( Dias et al, 2019 ; Maltarollo et al, 2019 ; Shi et al, 2020 ; Li et al, 2021 ; Wani et al, 2021 ). A good example of underlying supervised learning DT method was reported by Suay-Garcia et al (2020) .…”

Section: Modern Approachesmentioning

confidence: 99%

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Machine Learning in Antibacterial Drug Design

Jukič

Bren

2022

Front. Pharmacol.

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Advances in computer hardware and the availability of high-performance supercomputing platforms and parallel computing, along with artificial intelligence methods are successfully complementing traditional approaches in medicinal chemistry. In particular, machine learning is gaining importance with the growth of the available data collections. One of the critical areas where this methodology can be successfully applied is in the development of new antibacterial agents. The latter is essential because of the high attrition rates in new drug discovery, both in industry and in academic research programs. Scientific involvement in this area is even more urgent as antibacterial drug resistance becomes a public health concern worldwide and pushes us increasingly into the post-antibiotic era. In this review, we focus on the latest machine learning approaches used in the discovery of new antibacterial agents and targets, covering both small molecules and antibacterial peptides. For the benefit of the reader, we summarize all applied machine learning approaches and available databases useful for the design of new antibacterial agents and address the current shortcomings.

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Section: Small Moleculesmentioning

confidence: 99%

Section: Modern Approachesmentioning

confidence: 99%

Machine Learning in Antibacterial Drug Design

Jukič

Bren

2022

Front. Pharmacol.

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“…Some examples are represented by QSAR-ML models [40][41][42][43], multi-and combi-QSAR approaches [44][45][46][47][48][49][50]. Furthermore, in drug discovery field, advanced computational models, based on ML technology, hve demonstrated strong potential in selecting effective hit compounds [51][52][53][54][55][56][57][58]. Moreover, ML-based approaches represent a valuable resource also in drug repurposing field [59,60].…”

Section: Drug Discovery and Developmentmentioning

confidence: 99%

Artificial Intelligence in Translational Medicine

Brogi¹,

Calderone²

2021

Preprint

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The huge advancement of Internet web facilities as well as the progress in computing and algorithm development, along with current innovations regarding high-throughput techniques enables the scientific community to gain access to biological datasets, clinical data, and several databases containing billions of information concerning scientific knowledge. Consequently, during the last decade the system for managing, analyzing, processing and extrapolating information from scientific data has been considerably modified in several fields including the medical one. As a consequence of the mentioned scenario, scientific vocabulary was enriched by novel lexicons such as Machine Learning (ML)/Deep Learning (DL) and overall Artificial Intelligence (AI). Beyond the terminology, these computational techniques are revolutionizing the scientific research in drug discovery pitch, from the preclinical studies to clinical investigation. Interestingly, between preclinical and clinical research, the translational research is benefitting from computer-based approaches, transforming the design and execution of the translational research, resulting in breakthroughs for advancing human health. Accordingly, in this review article, we analyze the most advanced applications of AI in translational medicine, providing an up-to-date outlook regarding this emerging field.

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“…Besides, most existing DTI models propose universal frameworks that can be used to exploit various drug-target interactions, rather than focusing on a specific target class. 9 As for β-lactamases, until now there is no report of deep neural network architecture used in searching for the inhibitors. Nevertheless, an unavoidable challenge to build a specific model for β-lactamases and inhibitors is the limited size of data availability.…”

Section: ■ Introductionmentioning

confidence: 99%

DeepBLI: A Transferable Multichannel Model for Detecting β-Lactamase-Inhibitor Interaction

Dong

Yang

et al. 2022

J. Chem. Inf. Model.

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Pathogens producing β-lactamase pose a great challenge to antibiotic-resistant infection treatment; thus, it is urgent to discover novel β-lactamase inhibitors for drug development. Conventional high-throughput screening is very costly, and structure-based virtual screening is limited with mechanisms. In this study, we construct a novel multichannel deep neural network (DeepBLI) for β-lactamase inhibitor screening, pretrained with a label reversal KIBA data set and fine-tuned on β-lactamase-inhibitor pairs from BindingDB. First, the pairs of encoders (Conv and Att) fuse the information spatially and sequentially for both enzymes and inhibitors. Then, a co-attention module creates the connection between the inhibitor and enzyme embeddings. Finally, multichannel outputs fuse with an element-wise product and then are fed into 3-layer fully connected networks to predict interactions. Comparing the state-of-the-art methods, DeepBLI yields an AUROC of 0.9240 and an AUPRC of 0.9715, which indicates that it can identify new β-lactamase-inhibitor interactions. To demonstrate its prediction ability, an application of DeepBLI is described to screen potential inhibitor compounds for metallo-β-lactamase AIM-1 and repurpose rottlerin for four classes of β-lactamase targets, showing the possibility of being a broad-spectrum inhibitor. DeepBLI provides an effective way for antibacterial drug development, contributing to antibiotic-resistant therapeutics.

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Applications of machine‐learning methods for the discovery of NDM‐1 inhibitors

Cited by 9 publications

References 34 publications

Machine Learning in Antibacterial Drug Design

Machine Learning in Antibacterial Drug Design

Artificial Intelligence in Translational Medicine

DeepBLI: A Transferable Multichannel Model for Detecting β-Lactamase-Inhibitor Interaction

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