Deep-learning- and pharmacophore-based prediction of RAGE inhibitors

Huang, David; Kouznetsova, Valentina L.; Tsigelny, Igor F.

doi:10.1088/1478-3975/ab6819

Cited by 13 publications

(8 citation statements)

References 33 publications

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“…In our recent study [ 27 ], we attempted to compare the results of pharmacophore-based and ML-based drug design, and confirmed that results of pharmacophore-based and deep learning (DL)-based drug selection were similar in a significant part of the predicted drug candidates.…”

Section: Introductionmentioning

confidence: 78%

Prediction of Premature Termination Codon Suppressing Compounds for Treatment of Duchenne Muscular Dystrophy Using Machine Learning

Wang

Romm²,

Kouznetsova

et al. 2020

Molecules

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A significant percentage of Duchenne muscular dystrophy (DMD) cases are caused by premature termination codon (PTC) mutations in the dystrophin gene, leading to the production of a truncated, non-functional dystrophin polypeptide. PTC-suppressing compounds (PTCSC) have been developed in order to restore protein translation by allowing the incorporation of an amino acid in place of a stop codon. However, limitations exist in terms of efficacy and toxicity. To identify new compounds that have PTC-suppressing ability, we selected and clustered existing PTCSC, allowing for the construction of a common pharmacophore model. Machine learning (ML) and deep learning (DL) models were developed for prediction of new PTCSC based on known compounds. We conducted a search of the NCI compounds database using the pharmacophore-based model and a search of the DrugBank database using pharmacophore-based, ML and DL models. Sixteen drug compounds were selected as a consensus of pharmacophore-based, ML, and DL searches. Our results suggest notable correspondence of the pharmacophore-based, ML, and DL models in prediction of new PTC-suppressing compounds.

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Section: Introductionmentioning

confidence: 78%

Prediction of Premature Termination Codon Suppressing Compounds for Treatment of Duchenne Muscular Dystrophy Using Machine Learning

Wang

Romm²,

Kouznetsova

et al. 2020

Molecules

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“…So we calculate the logarithmic values as eqs – for each, where the concentration unit of IC 50 , K d , and K i is nM. We regard a pair as positive when pIC 50 ≥ 6.0, or p K i ≥ 7.6, or p K d ≥ 7.0 normalp normalI normalC 50 = prefix− log ( I C 50 10 9 ) normalp K normali = prefix− log ( K i 10 9 ) normalp K normald = prefix− log ( K d 10 9 ) To demonstrate the effectiveness of our proposed model, we use the values of area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve (AUPRC), accuracy, precision, and recall as evaluation metrics.…”

Section: Resultsmentioning

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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“…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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Deep-learning- and pharmacophore-based prediction of RAGE inhibitors

Cited by 13 publications

References 33 publications

Prediction of Premature Termination Codon Suppressing Compounds for Treatment of Duchenne Muscular Dystrophy Using Machine Learning

Prediction of Premature Termination Codon Suppressing Compounds for Treatment of Duchenne Muscular Dystrophy Using Machine Learning

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

Artificial Intelligence in Translational Medicine

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