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Auto QSAR-based active learning docking for hit identification of potential inhibitors of Plasmodium falciparum Hsp90 as antimalarial agents.

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Title: Auto QSAR-based active learning docking for hit identification of potential inhibitors of Plasmodium falciparum Hsp90 as antimalarial agents.
Authors: Matlhodi, Thato1 (AUTHOR), Makatsela, Lisema Patrick1 (AUTHOR), Dongola, Tendamudzimu Harmfree2 (AUTHOR), Simelane, Mthokozisi Blessing Cedric3 (AUTHOR), Shonhai, Addmore2 (AUTHOR), Gumede, Njabulo Joyfull4 (AUTHOR), Mokoena, Fortunate1 (AUTHOR) Fortunate.Mokoena@nwu.ac.za
Source: PLoS ONE. 11/25/2024, Vol. 19 Issue 11, p1-23. 23p.
Database: Academic Search Ultimate
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An: 181085086
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  Data: Auto QSAR-based active learning docking for hit identification of potential inhibitors of Plasmodium falciparum Hsp90 as antimalarial agents.
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  Data: <searchLink fieldCode="JN" term="%22PLoS+ONE%22">PLoS ONE</searchLink>. 11/25/2024, Vol. 19 Issue 11, p1-23. 23p.
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=asn&AN=181085086
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        Value: 10.1371/journal.pone.0308969
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        Text: English
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      – TitleFull: Auto QSAR-based active learning docking for hit identification of potential inhibitors of Plasmodium falciparum Hsp90 as antimalarial agents.
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            NameFull: Makatsela, Lisema Patrick
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              Text: 11/25/2024
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              Y: 2024
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