Scaling machine learning for target prediction in drug discovery using Apache Spark

Harnie, Dries; Saey, Mathijs; Vapirev, A.; Wegner, Jörg K.; Gedich, Andrey; Steijaert, Marvin; Ceulemans, Hugo; Wuyts, Roel; Meuter, Wolfgang De

doi:10.1016/j.future.2016.04.023

Cited by 43 publications

(10 citation statements)

References 19 publications

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“…These models are generated by learning from already tested chemical substances; the results showed the effectiveness of Spark to create pipelines based on machine learning techniques with a good scaling behavior in a distributed environment. Dries Harnie and his teamwork [18] re-implemented the Chemogenomics pipeline using Apache Spark (S-CHEMO). The Chemogenomics project attempts to derive new candidate drugs from existing experiments through a set of machine learning predictor programs.…”

Section: Related Workmentioning

confidence: 99%

Ensemble Learning for Large Scale Virtual Screening on Apache Spark

Sid

Batouche

2018

Computational Intelligence and Its Applications

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Virtual screening (VS) is an in-silico tool for drug discovery that aims to identify the candidate drugs through computational techniques by screening large libraries of small molecules. Various ligand and structure-based virtual screening approaches have been proposed in the last decades. Machine learning (ML) techniques have been widely applied in drug discovery and development process, predominantly in ligand based virtual screening approaches. Ensemble learning is a very common paradigm in ML field, where many models are trained on the same problem's data, to combine in the end the results in one improved prediction. Applying VS to massive molecular libraries (Big Data) is computationally intensive; so the split of these data to chunks to parallelize and distribute the task became necessary. For many years, MapReduce has been successfully applied on clusters to solve the problems with very large datasets, but with some limitations. Apache Spark is an open source framework for Big Data processing, which overcomes the shortcomings of MapReduce. In this paper, we propose a new approach based on ensemble learning paradigm in Apache Spark to improve in terms of execution time and precision the large-scale virtual screening. We generate a new training dataset to evaluate our approach. The experimental results show a good predictive performance up to 92% precision with an acceptable execution time.

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Section: Related Workmentioning

confidence: 99%

Ensemble Learning for Large Scale Virtual Screening on Apache Spark

Sid

Batouche

2018

Computational Intelligence and Its Applications

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“…If the service provider is unaware of a customer who is about to churn, no action can be taken for that customer. Business must consider risk, the level, and cost of intervention and plausible customer segmentation [6]. In telecommunication industry, subscribers frequently switch over from one industry to another which is a prime concern.…”

Section: Causes Of Customer Churnmentioning

confidence: 99%

Predicting customer churn using targeted proactive retention

Mishachandar¹,

Kumar²

2018

IJET

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With the advent of innovative technologies and fierce competition, the choices for customers to choose from have increased tremendously in number. Especially in the case of a telecommunication industry, where deregulation is at its peak. Every year a new company springs up offering fitter options for its customers. This has turned the concentration of the business doers on churn prediction and business management models to sustain their places. Businesses approach churn in two ways, one is through targeted customer retention and through cause identification strategy. The literature of this paper provides a comprehensible understanding of the so far employed techniques in predicting customer churn. From that, it is quite evident that less attention has been given to the accuracy and the intuitiveness of churn models developed. Therefore, a novel approach of combining the models of Machine Learning and Big Data Analytics tools was proposed to deal churn prediction effectively. The purpose of this proposed work is to apply a novel retention technique called the targeted proactive retention to predict customer churning behavior in advance and help in their retention. This proposed work will help telecom companies to comprehend the risk associated with customer churn by predicting the possibility and the time of occurrence.

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“…During this pneumonia epidemic, studies have utilized the favored approaches that target SARS-CoV-2 with high-throughput screening of large-scale molecular databases and obtaining potential antiviral drugs [20][21]. With the advancement of computer technology, the combination of computer-aided drug design (CADD) and arti cial intelligence (AI) research has become a valuable tool to accelerate the slow process of drug discovery and restraint the expansion of R&D costs, expand the applicable system and improve the level of automation, followed by the development of CADD-based multithreaded in silico screening technology [22][23][24][25]. Within the framework of above idea, we proposed a multimodule integrated approach aimed at improving the lead compound screening accuracy and greatly reducing the time cost by fully maximizing the advantages of each module to achieve a semiautomatic pipeline.…”

Section: Introductionmentioning

confidence: 99%

An integrated method for optimized identification of effective natural inhibitors against SARS-CoV-2 3CLpro

Liao

Chen

Tao

et al. 2021

Preprint

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The current severe situation of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has not been reversed and posed great threats to global health. Therefore, there is an urgent need to find out effective antiviral drugs. The 3-chymotrypsin-like protease (3CLpro) in SARS-CoV-2 serve as a promising anti-virus target due to its essential role in the regulation of virus reproduction. Here, we report an improved integrated approach to identify effective 3CLpro inhibitors from effective Chinese herbal formulas. With this approach, we identified the 5 natural products (NPs) including narcissoside, kaempferol-3-O-gentiobioside, rutin, vicenin-2 and isoschaftoside as potential anti-SARS-CoV-2 candidates. Subsequent molecular dynamics simulation additionally revealed that these molecules can be tightly bound to 3CLpro and confirmed effectiveness against COVID-19. Moreover, kaempferol-3-o-gentiobioside, vicenin-2 and isoschaftoside were first reported to have SARS-CoV-2 3CLpro inhibitory activity. In summary, this optimized integrated strategy for drug screening can be utilized in the discovery of antiviral drugs to achieve rapid acquisition of drugs with specific effects on antiviral targets.

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Scaling machine learning for target prediction in drug discovery using Apache Spark

Cited by 43 publications

References 19 publications

Ensemble Learning for Large Scale Virtual Screening on Apache Spark

Ensemble Learning for Large Scale Virtual Screening on Apache Spark

Predicting customer churn using targeted proactive retention

An integrated method for optimized identification of effective natural inhibitors against SARS-CoV-2 3CLpro

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