Advances in distributed computing with modern drug discovery

Banegas-Luna, Antonio Jesús; Imbernón, Baldomero; Llanes, Antonio; Pérez‐Garrido, A.; Cerón‐Carrasco, José P.; Gesing, Sandra; Merelli, Ivan; D’Agostino, Daniele; Pérez‐Sánchez, Horacio

doi:10.1080/17460441.2019.1552936

Cited by 17 publications

(12 citation statements)

References 112 publications

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“…Establishing good MLOps practices helps an organization make sure that model can be continuously updated and deployed as data is being added, and it can enable model sharing between teams by making sure that artifacts are continuously updated and deployed. [51]. If there is a need or opportunity to collaborate across organizations, such as between two or more pharmaceutical companies, the situation becomes even more complicated.…”

Section: Machine Learning Using Private Sensitive and Regulated Datamentioning

confidence: 99%

The machine learning life cycle and the cloud: implications for drug discovery

Spjuth

Frid²,

Hellander

2021

Expert Opinion on Drug Discovery

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Introduction: Artificial intelligence (AI) and machine learning (ML) are increasingly used in many aspects of drug discovery. Larger data sizes and methods such as Deep Neural Networks contribute to challenges in data management, the required software stack, and computational infrastructure. There is an increasing need in drug discovery to continuously re-train models and make them available in production environments.Areas covered: This article describes how cloud computing can aid the ML life cycle in drug discovery. The authors discuss opportunities with containerization and scientific workflows and introduce the concept of MLOps and describe how it can facilitate reproducible and robust ML modeling in drug discovery organizations. They also discuss ML on private, sensitive and regulated data. Expert opinion: Cloud computing offers a compelling suite of building blocks to sustain the ML life cycle integrated in iterative drug discovery. Containerization and platforms such as Kubernetes together with scientific workflows can enable reproducible and resilient analysis pipelines, and the elasticity and flexibility of cloud infrastructures enables scalable and efficient access to compute resources. Drug discovery commonly involves working with sensitive or private data, and cloud computing and federated learning can contribute toward enabling collaborative drug discovery within and between organizations.

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Section: Machine Learning Using Private Sensitive and Regulated Datamentioning

confidence: 99%

The machine learning life cycle and the cloud: implications for drug discovery

Spjuth

Frid²,

Hellander

2021

Expert Opinion on Drug Discovery

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“…The consequence is that there is the need to apply the most advanced HPC techniques and platforms to process the data and to turn them into real knowledge. This explains why all the available state‐of‐the‐art parallel and distributed solutions have been exploited in these fields …”

Section: Themes Of This Special Issuementioning

confidence: 99%

Latest advances in parallel, distributed, and network‐based processing

Merelli

Lió

Kotenko

et al. 2020

Concurrency and Computation

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This editorial introduces the articles selected for the special issue concerning the International Conferences on Parallel, Distributed, and Network-Based Processing, which provided insights related to the efficient exploitation of parallel and distributed architectures, including power-aware computing, application scheduling, and application development for GPUs. K E Y W O R D SComputing, gpu computing, network-based computing, parallel Computing

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“…Recent reviews [97,98] and the "Click2Drug" catalog [99] give a vivid expression on describing many different sources for many partial solutionsavailable as semiautomated web services or as instances that may be started at one's independent disposal. How exactly a transfer of information between these tools should be performed, or how these tools should be run to generate redundant findings with then higher confidenceall this is yet to be clarifiedboth semantically and technically.…”

Section: Cloud-based Solutionsmentioning

confidence: 99%

Cloud-Based High Throughput Virtual Screening in Novel Drug Discovery

Olğaç

Türe

Olğaç

et al. 2019

Lecture Notes in Computer Science

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Drug discovery and development requires the integration of multiple scientific and technological disciplines in chemistry, biology and extensive use of information technology. Computer Aided Drug Discovery (CADD) methods are being used in this work area with several different workflows. Virtual screening (VS) is one of the most often applied CADD methods used in rational drug design, which may be applied in early stages of drug discovery pipeline. The increasing number of modular and scalable cloud-based computational platforms can assist the needs in VS studies. Such platforms are being developed to try to help researchers with various types of applications to prepare and guide the drug discovery and development pipeline. They are designed to perform VS efficiently, aimed to identify commercially available lead-like and drug-like compounds to be acquired and tested. Chemical datasets can be built, libraries can be analyzed, and structure-based or ligand-based VS studies can be performed with cloud technologies. Such platforms could also be adapted to be included in different stages of the pharmaceutical R&D process to rationalize the needs, e.g. to repurpose drugs, with various computational scalability options. This chapter introduces basic concepts and tools by outlining the general workflows of VS, and their integration to the cloud platforms. This may be a seed for further inter-disciplinary development of VS to be applied by drug hunters.

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Advances in distributed computing with modern drug discovery

Cited by 17 publications

References 112 publications

The machine learning life cycle and the cloud: implications for drug discovery

The machine learning life cycle and the cloud: implications for drug discovery

Latest advances in parallel, distributed, and network‐based processing

Cloud-Based High Throughput Virtual Screening in Novel Drug Discovery

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