IMPECCABLE: Integrated Modeling PipelinE for COVID Cure by Assessing Better LEads

@article{Saadi2021IMPECCABLEIM,
  title={IMPECCABLE: Integrated Modeling PipelinE for COVID Cure by Assessing Better LEads},
  author={Aymen Al Saadi and Dario Alf{\`e} and Y. Babuji and Agastya P. Bhati and Benjamin J. Blaiszik and Thomas S. Brettin and Kyle Chard and Ryan Chard and Peter V. Coveney and Anda Trifan and Alexander Brace and Austin R. Clyde and Ian T. Foster and Tom Gibbs and Shantenu Jha and Kristopher Keipert and Thorsten Kurth and Dieter August Kranzlm{\"u}ller and Hyungro Lee and Zhuozhao Li and Heng Ma and Andr{\'e} Merzky and Gerald Mathias and Alexander Partin and Junqi Yin and Arvind Ramanathan and Ashka Shah and Abraham Stern and Rick L. Stevens and Li Tan and Mikhail Titov and Aristeidis Tsaris and Matteo Turilli and Huub J. J. Van Dam and Shunzhou Wan and David Wifling},
  journal={50th International Conference on Parallel Processing},
  year={2021}
}
  • A. Saadi, Dario Alfè, +33 authors D. Wifling
  • Published 13 October 2020
  • Computer Science, Biology
  • 50th International Conference on Parallel Processing
The drug discovery process currently employed in the pharmaceutical industry typically requires about 10 years and $2–3 billion to deliver one new drug. This is both too expensive and too slow, especially in emergencies like the COVID-19 pandemic. In silico methodologies need to be improved both to select better lead compounds, so as to improve the efficiency of later stages in the drug discovery protocol, and to identify those lead compounds more quickly. No known methodological approach can… Expand

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References

SHOWING 1-10 OF 84 REFERENCES
COVID‐19 drug repurposing: A review of computational screening methods, clinical trials, and protein interaction assays
TLDR
This review divides the COVID‐19 drug repurposing research into three large groups, including clinical trials, computational research, and in vitro protein‐binding experiments to facilitate future drug discovery and the creation of effective drug combinations. Expand
Targeting SARS-CoV-2 with AI- and HPC-enabled Lead Generation: A First Data Release
TLDR
This data release encompasses structural information on the 4.2 B molecules enriched with pre-computed data to enable exploration and application of image-based deep learning methods, and 2D and 3D molecular descriptors to speed development of machine learning models. Expand
AMPL: A Data-Driven Modeling Pipeline for Drug Discovery
TLDR
The ATOM Modeling PipeLine, or AMPL, extends the functionality of the open source library DeepChem and supports an array of ML and molecular featurization tools and indicates that traditional molecular fingerprints underperform other feature representation methods. Expand
Artificial intelligence in COVID-19 drug repurposing
TLDR
This Review provides a strong rationale for using AI-based assistive tools for drug repurposing medications for human disease, including during the COVID-19 pandemic. Expand
High Throughput Virtual Screening and Validation of a SARS-CoV-2 Main Protease Non-Covalent Inhibitor
TLDR
A novel non-covalent small-molecule inhibitor that binds to and inhibits the SARS-Cov-2 main protease (Mpro) is discovered by employing a scalable high throughput virtual screening (HTVS) framework and a targeted compound library of over 6.5 million molecules that could be readily ordered and purchased. Expand
Scalable HPC and AI Infrastructure for COVID-19 Therapeutics
TLDR
This work describes several methods that integrate artificial intelligence and simulation-based approaches, and the design of computational infrastructure to support these methods at scale, and discusses their implementation and characterize their performance. Expand
Enabling Trade-offs Between Accuracy and Computational Cost: Adaptive Algorithms to Reduce Time to Clinical Insight
TLDR
This work reproduces results from a collaborative project between UCL and GlaxoSmithKline to study a congeneric series of drug candidates binding to the BRD4 protein – inhibitors of which have shown promising preclinical efficacy in pathologies ranging from cancer to inflammation. Expand
Principles of early drug discovery
TLDR
This review will look at key preclinical stages of the drug discovery process, from initial target identification and validation, through assay development, high throughput screening, hit identification, lead optimization and finally the selection of a candidate molecule for clinical development. Expand
DrugBank 5.0: a major update to the DrugBank database for 2018
TLDR
This year’s update, DrugBank 5.0, represents the most significant upgrade to the database in more than 10 years and significant improvements have been made to the quantity, quality and consistency of drug indications, drug binding data as well as drug-drug and drug-food interactions. Expand
Towards the development of universal, fast and highly accurate docking/scoring methods: a long way to go
TLDR
This review presents the current status of docking and scoring methods, with exhaustive lists of these and describes some of the remaining developments that would potentially lead to a universally applicable docking/scoring method. Expand
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4
5
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