Big Data Analytics Techniques in Virtual Screening for Drug Discovery

Sid, Karima; Batouche, Mohamed

doi:10.1145/3090354.3090363

Cited by 4 publications

(3 citation statements)

References 23 publications

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“…Each vector is represented as a support vector composed by its attributes (molecular descriptors), SVM search for one target known as the optimal hyperplane ρ that separates the support vectors. The optimal hyperplane ρ maximizes the margin of separation between the hyperplane and the closest data points (support vectors) on both sides of the hyperplane [21,22]. Decision Trees.…”

Section: Machine Learning Algorithmsmentioning

confidence: 99%

“…The Hadoop MapReduce provides many benefits such as flexibility, scalability and fault-tolerance, but at the same time, it has some limitations that make it not suitable for some applications such as iterative jobs (e.g. Machine learning algorithms), and interactive analytics [1,13,22]. Apache Spark [12] is a highly scalable, fast and in-memory Big Data processing engine [9]; it overcomes the shortcomings of Hadoop MapReduce model, while retaining scalability and fault tolerance [13]; it offers an ability to develop distributed applications using Java, Python, Scala, and R programming languages [9].…”

Section: Apache Spark For Big Data Machine Learningmentioning

confidence: 99%

“…In cluster mode, Spark supports three cluster managers, standalone, YARN of Hadoop and Mesos. It can access diverse data sources including HDFS, Cassandra, HBase, and S3 [12,22]. The main abstraction in Spark is the Resilient Distributed Dataset (RDD) [25], which is defined as an immutable collection of objects partitioned across the nodes in the cluster, it can be cached in memory (as reusable data) and rebuilt if a partition is lost through a notion of lineage [13,22].…”

Section: Apache Spark For Big Data Machine Learningmentioning

confidence: 99%

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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: Machine Learning Algorithmsmentioning

confidence: 99%