DeepMalaria: Artificial Intelligence Driven Discovery of Potent Antiplasmodials

Arshadi, Arash Keshavarzi; Salem, Milad; Collins, Jennifer; Yuan, J.S.; Chakrabarti, Debopam

doi:10.3389/fphar.2019.01526

Cited by 52 publications

(43 citation statements)

References 54 publications

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“…2 Resistance to the currently marketed antimalarial drugs, including standard artemisinin-combination therapies, is spreading, making the discovery of new antimalarials ever more crucial. 3 New structural classes of antiplasmodials with a novel mechanism of action are particularly desirable to minimize cross-resistance. The antibacterial and antifungal activities of pantothenamides (amides of pantothenate; Figure 1) were recognized in the 1970's; 4 whereas their antiplasmodial activity was demonstrated more recently.…”

Section: Introductionmentioning

confidence: 99%

Exploring heteroaromatic rings as a replacement for the labile amide of antiplasmodial pantothenamides

Guan

Spry

Tjhin

et al. 2020

Preprint

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The Plasmodium parasites that cause malaria are adept at developing resistance to antimalarial drugs, necessitating the search for new antiplasmodials. Although several amide analogs of pantothenate (pantothenamides) show potent antiplasmodial activity, hydrolysis by pantetheinases (or vanins) present in blood rapidly inactivates them. We report herein the facile synthesis and biological activity of a small library of pantothenamide analogs in which the labile amide group is replaced with a variety of heteroaromatic rings. Several of the new analogs display antiplasmodial activity in the nanomolar range against P. falciparum and/or P. knowlesi in the presence of pantetheinase. A previously reported triazole and an isoxazole derivative presented here were further characterized and found to possess high selectivity indices, medium or high Caco-2 permeability, and medium or low microsomal clearance in vitro. Although we show here that the two compounds fail to suppress proliferation of P. berghei in vivo, pharmacokinetic and contact time data presented provide a benchmark for the compound profile required to achieve antiplasmodial activity in mice and should facilitate lead optimization.

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Section: Introductionmentioning

confidence: 99%

Exploring heteroaromatic rings as a replacement for the labile amide of antiplasmodial pantothenamides

Guan

Spry

Tjhin

et al. 2020

Preprint

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“…VS for rational drug discovery is essentially an approach that involves computationally targeting a specific biomolecule (e.g., DNA, protein, RNA, lipid) of a cell to inhibit its growth and/or activation (Shoichet, 2004 ; Lionta et al, 2014 ). Structure-based and ligand-based drug discovery and design are two important subgroups of this type of screening (Lionta et al, 2014 ; Yu and Mackerell, 2017 ; Arshadi et al, 2020 ; Broom et al, 2020 ). Given our access to computationally and experimentally determined viral protein structures (Senior et al, 2020 ; Zhang L. et al, 2020 ), VS provides a rapid and cost-effective strategy for identifying antiviral candidates.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence for COVID-19 Drug Discovery and Vaccine Development

Arshadi

Webb

Salem

et al. 2020

Front. Artif. Intell.

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163

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SARS-COV-2 has roused the scientific community with a call to action to combat the growing pandemic. At the time of this writing, there are as yet no novel antiviral agents or approved vaccines available for deployment as a frontline defense. Understanding the pathobiology of COVID-19 could aid scientists in their discovery of potent antivirals by elucidating unexplored viral pathways. One method for accomplishing this is the leveraging of computational methods to discover new candidate drugs and vaccines in silico. In the last decade, machine learning-based models, trained on specific biomolecules, have offered inexpensive and rapid implementation methods for the discovery of effective viral therapies. Given a target biomolecule, these models are capable of predicting inhibitor candidates in a structural-based manner. If enough data are presented to a model, it can aid the search for a drug or vaccine candidate by identifying patterns within the data. In this review, we focus on the recent advances of COVID-19 drug and vaccine development using artificial intelligence and the potential of intelligent training for the discovery of COVID-19 therapeutics. To facilitate applications of deep learning for SARS-COV-2, we highlight multiple molecular targets of COVID-19, inhibition of which may increase patient survival. Moreover, we present CoronaDB-AI, a dataset of compounds, peptides, and epitopes discovered either in silico or in vitro that can be potentially used for training models in order to extract COVID-19 treatment. The information and datasets provided in this review can be used to train deep learning-based models and accelerate the discovery of effective viral therapies.

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“…A new AI system, DeepMalaria, for discovery of antiplasmodial drugs, has recently been reported by Keshavarzi Arshadi and coworkers ( Keshavarzi Arshadi et al, 2020 ). A graph-based model was trained on 13,446 antiplasmodial hit compounds from GlaxoSmithKline dataset.…”

Section: Introductionmentioning

confidence: 99%

Use of Artificial Intelligence and Machine Learning for Discovery of Drugs for Neglected Tropical Diseases

Winkler

2021

Front. Chem.

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Neglected tropical diseases continue to create high levels of morbidity and mortality in a sizeable fraction of the world’s population, despite ongoing research into new treatments. Some of the most important technological developments that have accelerated drug discovery for diseases of affluent countries have not flowed down to neglected tropical disease drug discovery. Pharmaceutical development business models, cost of developing new drug treatments and subsequent costs to patients, and accessibility of technologies to scientists in most of the affected countries are some of the reasons for this low uptake and slow development relative to that for common diseases in developed countries. Computational methods are starting to make significant inroads into discovery of drugs for neglected tropical diseases due to the increasing availability of large databases that can be used to train ML models, increasing accuracy of these methods, lower entry barrier for researchers, and widespread availability of public domain machine learning codes. Here, the application of artificial intelligence, largely the subset called machine learning, to modelling and prediction of biological activities and discovery of new drugs for neglected tropical diseases is summarized. The pathways for the development of machine learning methods in the short to medium term and the use of other artificial intelligence methods for drug discovery is discussed. The current roadblocks to, and likely impacts of, synergistic new technological developments on the use of ML methods for neglected tropical disease drug discovery in the future are also discussed.

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DeepMalaria: Artificial Intelligence Driven Discovery of Potent Antiplasmodials

Cited by 52 publications

References 54 publications

Exploring heteroaromatic rings as a replacement for the labile amide of antiplasmodial pantothenamides

Exploring heteroaromatic rings as a replacement for the labile amide of antiplasmodial pantothenamides

Artificial Intelligence for COVID-19 Drug Discovery and Vaccine Development

Use of Artificial Intelligence and Machine Learning for Discovery of Drugs for Neglected Tropical Diseases

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