Evidence map›Paper›PMID 37702944›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2024

Molecular Dynamics and Other HPC Simulations for Drug Discovery.

Martin Kotev, Constantino Diaz Gonzalez

Abstract read
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In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Martin KotevEvotec SE, Integrated Drug Discovery, Molecular Architects, Campus Curie, Toulouse, France.ORCID 0000-0001-6178-6404
Constantino Diaz GonzalezEvotec SE, Integrated Drug Discovery, Molecular Architects, Campus Curie, Toulouse, France. Constantino.Diaz@evotec.com.ORCID 0000-0002-3433-8478

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

High performance computing (HPC) is taking an increasingly important place in drug discovery. It makes possible the simulation of complex biochemical systems with high precision in a short time, thanks to the use of sophisticated algorithms. It promotes the advancement of knowledge in fields that are inaccessible or difficult to access through experimentation and it contributes to accelerating the discovery of drugs for unmet medical needs while reducing costs. Herein, we report how computational performance has evolved over the past years, and then we detail three domains where HPC is essential. Molecular dynamics (MD) is commonly used to explore the flexibility of proteins, thus generating a better understanding of different possible approaches to modulate their activity. Modeling and simulation of biopolymer complexes enables the study of protein-protein interactions (PPI) in healthy and disease states, thus helping the identification of targets of pharmacological interest. Virtual screening (VS) also benefits from HPC to predict in a short time, among millions or billions of virtual chemical compounds, the best potential ligands that will be tested in relevant assays to start a rational drug design process.

Indexed as

Drug DiscoveryMolecular Dynamics SimulationAlgorithmsBiological AssayDrug DesignDrug designDrug discoveryGPUHigh Performance ComputingHPCMCMDMolecular dynamicsMonte CarloProtein complexesSimulationSupercomputerVirtual screeningVS

Identifiers

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.