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Evolutionary druggability: leveraging low-dimensional fitness landscapes towards new metrics for antimicrobial applications

bioRxiv (Cold Spring Harbor Laboratory)(2023)10.1101/2023.04.08.536116Source: DataRank Database

Evolutionary druggability: leveraging low-dimensional fitness landscapes towards new metrics for antimicrobial applications is a research paper published in bioRxiv (Cold Spring Harbor Laboratory) (2023). On theSindex it has a DataRank of 0.

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datarank_citation_only_1hop_v6· scope data_onlyMethodology

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Enrichment:Funding (3 grants)OA: greenTopics (18)IDs (PubMed, OpenAlex)

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Open Access

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Authors (5)

Tandin Dorji ORCID,Ra’Mal M. Harris ORCID,Matthew D. Shoulders,C. Brandon Ogbunugafor ORCID,Rafael F. Guerrero ORCID

Live enrichment

PubMed:37066376 OpenAlex:https://openalex.org/W4362736167

NIAID NIH HHS

Grant: R01 AI168166

NIGMS NIH HHS

Grant: R35 GM136354

NIGMS NIH HHS

Grant: R35 GM147107

greenLicense: cc-by-nc-nd

repositoryhttps://www.biorxiv.org/content/biorxiv/early/2023/04/08/2023.04.08.536116.full.pdf

publisherhttps://syndication.highwire.org/content/doi/10.1101/2023.04.08.536116

repositoryhttps://doi.org/10.1101/2023.04.08.536116

repositoryhttps://pubmed.ncbi.nlm.nih.gov/37066376

Fields of Study

Evolution and Genetic DynamicsGene Regulatory Network AnalysisEvolutionary Game Theory and Cooperation

Keywords

DruggabilityComputational biologyFitness landscapeDrugComputer scienceDrug discoveryDrug developmentBiologyData scienceBioinformaticsMedicineGeneticsPharmacologyPopulationGene