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Insilico evaluation on potential Mt-Sp1/matriptase inhibitors data: DFT and molecular modelling approaches.
Oyebamiji, Abel Kolawole; Akintelu, Sunday Adewale; Adekunle, David O; Oke, David Gbenga; Olanrewaju, Adesoji Alani; Akinola, Omowumi Temitayo.
Afiliación
  • Oyebamiji AK; Industrial Chemistry Programme, Bowen University, PMB 284, Iwo, Osun State, Nigeria.
  • Akintelu SA; Department of pure and Applied Chemistry, Ladoke Akintola University of Technology, PMB 4000, Ogbomoso, Oyo State, Nigeria.
  • Adekunle DO; Industrial Chemistry Programme, Bowen University, PMB 284, Iwo, Osun State, Nigeria.
  • Oke DG; Industrial Chemistry Programme, Bowen University, PMB 284, Iwo, Osun State, Nigeria.
  • Olanrewaju AA; Industrial Chemistry Programme, Bowen University, PMB 284, Iwo, Osun State, Nigeria.
  • Akinola OT; Microbiology Programme, Bowen University, PMB 284 Iwo, Osun State, Nigeria.
Data Brief ; 55: 110565, 2024 Aug.
Article en En | MEDLINE | ID: mdl-38952955
ABSTRACT
Nine heterocyclic compounds were investigated using density functional theory, molecular operating environment software, material studio, swissparam (Swiss drug design) software. In this work, the descriptors generated from the optimized compounds proved to be efficient and explain the level of reactivity of the investigated compound. The developed quantitative structure activity relationship (QSAR) model was predictive and reliable. Also, compound 9 proved to be capable of inhibiting Mt-Sp1/Matriptase (pdb id 1eax) than other examined heterocyclic compounds. Target prediction analysis was carried out on the compound with highest binding affinity (Compound 9) and the results were reported.
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Texto completo: 1 Base de datos: MEDLINE Idioma: En Revista: Data Brief Año: 2024 Tipo del documento: Article

Texto completo: 1 Base de datos: MEDLINE Idioma: En Revista: Data Brief Año: 2024 Tipo del documento: Article