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Machine learning-based predictive models for identifying high active compounds against HIV-1 integrase.
Parvez, M K; Al-Dosari, M S; Sinha, G P.
Affiliation
  • Parvez MK; Department of Pharmacognosy, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia.
  • Al-Dosari MS; Department of Pharmacognosy, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia.
  • Sinha GP; Department of Neurobiology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.
SAR QSAR Environ Res ; 33(5): 387-402, 2022 May.
Article in En | MEDLINE | ID: mdl-35410555
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: HIV Infections / HIV Integrase Inhibitors / HIV Integrase Type of study: Prognostic_studies / Risk_factors_studies Limits: Humans Language: En Journal: SAR QSAR Environ Res Journal subject: SAUDE AMBIENTAL Year: 2022 Document type: Article Affiliation country: Country of publication:
Fulltext
Add to My VHL
Print
XML
PubMed Links
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: HIV Infections / HIV Integrase Inhibitors / HIV Integrase Type of study: Prognostic_studies / Risk_factors_studies Limits: Humans Language: En Journal: SAR QSAR Environ Res Journal subject: SAUDE AMBIENTAL Year: 2022 Document type: Article Affiliation country: Country of publication: