Titlebook: High Performance Computing for Drug Discovery and Biomedicine; Alexander Heifetz Book 2024 The Editor(s) (if applicable) and The Author(s)

FIR

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Automated Virtual Screening, identification to lead optimization. The overall aim of these computational methods is to obtain a more efficient discovery process, by reducing the number of “wet” experiments required to produce therapeutics that have higher probability of succeeding in clinical development and subsequently benef

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LUMEN

Edge, Fog, and Cloud Against Disease: The Potential of High-Performance Cloud Computing for Pharma ance, resource management, and running costs. The rapid growth in computing hardware has made it possible to provide cost-effective, robust, secure, and scalable alternatives to the on-premise (on-prem) HPC via Cloud, Fog, and Edge computing. It has enabled recent state-of-the-art machine learning (

MUTED

Knowledge Graphs and Their Applications in Drug Discovery,applications in drug discovery, including democratizing access to biomedical data, contextualizing or visualizing that data, and generating novel insights through the application of machine learning approaches. Knowledge graphs put data into context and therefore offer the opportunity to generate ex

Dedication

Natural Language Processing for Drug Discovery Knowledge Graphs: Promises and Pitfalls,e many heterogeneous data sources in a format that facilitates discovering connections. The utility of KGs has been exemplified in areas such as drug repurposing, with insights made through manual exploration and modeling of the data. In this chapter, we discuss promises and pitfalls of using natura

ACME

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High-Throughput Structure-Based Drug Design (HT-SBDD) Using Drug Docking, Fragment Molecular OrbitaD aims to offer a computational replacement to traditional high-throughput screening (HTS) methods of drug discovery. This “virtual screening” technique utilizes the structural data of a target protein in conjunction with large databases of potential drug candidates and then applies a range of diffe

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HPC Framework for Performing in Silico Trials Using a 3D Virtual Human Cardiac Population as Means nerate a platform to perform human cardiac in-silico clinical trials as means to assess the pro-arrhythmic risk after the administrations of one or combination of two potentially arrhythmic drugs. The drugs assessed in this study were hydroxychloroquine and azithromycin. The framework employs electr