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Combined Physics- and Machine-Learning-Based Method to Identify Druggable Binding Sites Using SILCS-Hotspots

Journal of Chemical Information and Modeling(2024)10.1021/acs.jcim.4c01189Source: DataRank Database

Combined Physics- and Machine-Learning-Based Method to Identify Druggable Binding Sites Using SILCS-Hotspots is a research paper published in Journal of Chemical Information and Modeling (2024). On theSindex it has a DataRank of 0. It has been cited 10 times.

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0DataRank · unranked

Scored on demand from live citation data

Open Access10 citations

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datarank_citation_only_1hop_v6· scope data_onlyMethodology

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Funding (3 grants)OA: greenTopics (11)IDs (PubMed)

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Findable (1/2)

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Accessible (1/2)

Open Access

Interoperable (0/2)

Reusable (0/3)

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Authors (4)

Mingtian Zhao ORCID,Anmol Kumar ORCID,Alexander D. MacKerell ORCID,Erik B. Nordquist ORCID

Live enrichment

PubMed:21097893

National Institute of General Medical Sciences

Grant: R35 GM131710

National Institutes of Health

Grant: 5R35GM131710-02

Macromolecular Conformational Heterogeneity

National Institutes of Health

Grant: 5T32CA154274-14

Training Grant in Cancer Biology

greenLicense: STM Policy #29

repositoryhttps://pmc.ncbi.nlm.nih.gov/articles/PMC11473228/pdf/nihms-2023227.pdf

publisherhttps://pubs.acs.org/doi/pdf/10.1021/acs.jcim.4c01189

repositoryhttps://pmc.ncbi.nlm.nih.gov/articles/PMC11473228/

https://doi.org/10.1021/acs.jcim.4c01189

https://doi.org/10.26434/chemrxiv-2024-hrqq9-v2

Fields of Study

0206 medical engineering02 engineering and technology

Keywords

Machine LearningBinding SitesProtein ConformationDrug DiscoveryProteinsMolecular Dynamics SimulationLigandsArticleProtein Binding