Machine learning, artificial intelligence, and data science breaking into drug design and neglected diseases

Peña-Guerrero, José; Nguewa, Paul A.; García-Sosa, Alfonso T.

doi:10.1002/wcms.1513

Cited by 28 publications

(21 citation statements)

References 166 publications

(310 reference statements)

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“…It is well known that confirmatory diagnosis based on specific diagnostic biomarkers remains a great challenge allowing the further control and eradication of infections including COVID-19 [11]. Therefore, this novel method may lead to specifically identify individuals carrying the Δ69/ Δ70 and the Δ106/Δ107/Δ108 mutations.…”

Section: Introductionmentioning

confidence: 99%

A rapid and low-cost protocol for the detection of B.1.1.7 lineage of SARS-CoV-2 by using SYBR Green-based RT-qPCR

Abdel-Sater

Younes

Nassar³

et al. 2021

Mol Biol Rep

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BackgroundThe new SARS-CoV-2 variant VOC (202012/01), identified recently in the United Kingdom (UK), exhibits a higher transmissibility rate compared to other variants, and a reproductive number 0.4 higher. In the UK, scientists were able to identify the increase of this new variant through the rise of false negative results for the spike (S) target using a three-target RT-PCR assay (TaqPath kit). Methods To control and study the current coronavirus pandemic, it is important to develop a rapid and low-cost molecular test to identify the aforementioned variant. In this work, we designed primer sets specific to the VOC (202012/01) to be used by SYBR Green-based RT-PCR. These primers were specifically designed to confirm the deletion mutations Δ69/Δ70 in the spike and the Δ106/Δ107/Δ108 in the NSP6 gene. We studied 20 samples from positive patients, detected by using the Applied Biosystems TaqPath RT-PCR COVID-19 kit (Thermo Fisher Scientific, Waltham, USA) that included the ORF1ab, S, and N gene targets. 16 samples displayed an S-negative profile (negative for S target and positive for N and ORF1ab targets) and four samples with S, N and ORF1ab positive profile. Results Our results emphasized that all S-negative samples harbored the mutations Δ69/Δ70 and Δ106/Δ107/Δ108. This protocol could be used as a second test to confirm the diagnosis in patients who were already positive to COVID-19 but showed false negative results for S-gene. Conclusions This technique may allow to identify patients carrying the VOC (202012/01) or a closely related variant, in case of shortage in sequencing.

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Section: Introductionmentioning

confidence: 99%

A rapid and low-cost protocol for the detection of B.1.1.7 lineage of SARS-CoV-2 by using SYBR Green-based RT-qPCR

Abdel-Sater

Younes

Nassar³

et al. 2021

Mol Biol Rep

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“…Over the last decade, there are many examples of applications of machine learning (ML) and artificial intelligence (AI) regarding NTDs [ 124 ]. As the estimated cost of developing new drugs increases and problems such as significant toxicity, low efficacy, and emerging resistance of the current regimen against NTDs remain unresolved, ML and AI are emerging as a new approach to overcome these challenges [ 125 ].…”

Section: Drug Targets and Inhibitorsmentioning

confidence: 99%

An Overview on Target-Based Drug Design against Kinetoplastid Protozoan Infections: Human African Trypanosomiasis, Chagas Disease and Leishmaniases

et al. 2021

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The protozoan diseases Human African Trypanosomiasis (HAT), Chagas disease (CD), and leishmaniases span worldwide and therefore their impact is a universal concern. The present regimen against kinetoplastid protozoan infections is poor and insufficient. Target-based design expands the horizon of drug design and development and offers novel chemical entities and potential drug candidates to the therapeutic arsenal against the aforementioned neglected diseases. In this review, we report the most promising targets of the main kinetoplastid parasites, as well as their corresponding inhibitors. This overview is part of the Special Issue, entitled “Advances of Medicinal Chemistry against Kinetoplastid Protozoa (Trypanosoma brucei, Trypanosoma cruzi and Leishmania spp.) Infections: Drug Design, Synthesis and Pharmacology”.

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“…2 Artificial neural networks providing diagnostic, identification, and organizational potential, especially for large clinical and biological datasets, are becoming increasingly used in medical science. Drug discovery, [3][4][5][6][7][8][9][10] lead optimization 11 and synthesis, 12,13 cardiological and cardiovascular diseases, [14][15][16][17][18] medical image analysis, [19][20][21][22] diabetic diseases, 23,24 oncology research, 25,26 diagnosis, for example, alteration of oscillatory brain activity as a possible biomarker for use in Alzheimer's disease diagnosis, 27 are some of the examples of AI in service of medical science (Figure 1). Computer-aided drug design is not only an interesting concept but also a business requirement.…”

Section: Introductionmentioning

confidence: 99%

Machine learning in drug design: Use of artificial intelligence to explore the chemical structure–biological activity relationship

Staszak

Wieszczycka

et al. 2021

WIREs Comput Mol Sci

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The paper presents a comprehensive overview of the use of artificial intelligence (AI) systems in drug design. Neural networks, which are one of the systems employed in AI, are used to identify chemical structures that can have medical relevance. Successful training of neural networks must be preceded by the acquisition of relevant information about chemical compounds, functional groups, and their possible biological activity. In general, a neural network requires a large set of training data, which must contain information about the chemical structure-biological activity relationship.The data can come from experimental measurements, but can also be generated using appropriate quantum models. In many of the studies presented below, authors showed a significant potential of neural networks to produce generalizations based on even relatively narrow training data. Despite the fact that neural network systems have been known for more than 40 years, it is only recently that they have seen rapid development due to the wider availability of computing power. In recent years, there has been a growing interest in deep learning techniques, bringing network modeling to a new level of abstraction. Deep learning allows combining what seems to be causally distant phenomena and effects, and to associate facts in a way resembling the human mind.

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Machine learning, artificial intelligence, and data science breaking into drug design and neglected diseases

Cited by 28 publications

References 166 publications

A rapid and low-cost protocol for the detection of B.1.1.7 lineage of SARS-CoV-2 by using SYBR Green-based RT-qPCR

A rapid and low-cost protocol for the detection of B.1.1.7 lineage of SARS-CoV-2 by using SYBR Green-based RT-qPCR

An Overview on Target-Based Drug Design against Kinetoplastid Protozoan Infections: Human African Trypanosomiasis, Chagas Disease and Leishmaniases

Machine learning in drug design: Use of artificial intelligence to explore the chemical structure–biological activity relationship

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