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• W4313467522 abstract "In silico prediction of the in vivo efficacy of siRNA ionizable-lipid nanoparticles is desirable as it can save time and resources dedicated to wet-lab experimentation. This study aims to computationally predict siRNA nanoparticles in vivo efficacy. A data set containing 120 entries was prepared by combining molecular descriptors of the ionizable lipids together with two nanoparticles formulation characteristics. Input descriptor combinations were selected by an evolutionary algorithm. Artificial neural networks, support vector machines and partial least squares regression were used for QSAR modeling. Depending on how the data set is split, two training sets and two external validation sets were prepared. Training and validation sets contained 90 and 30 entries respectively. The results showed the successful predictions of validation set log (siRNA dose) with Rval2= 0.86-0.89 and 0.75-80 for validation sets one and two, respectively. Artificial neural networks resulted in the best Rval2 for both validation sets. For predictions that have high bias, improvement of Rval2 from 0.47 to 0.96 was achieved by selecting the training set lipids lying within the applicability domain. In conclusion, in vivo performance of siRNA nanoparticles was successfully predicted by combining cheminformatics with machine learning techniques." @default.
• W4313467522 created "2023-01-06" @default.
• W4313467522 creator A5023144542 @default.
• W4313467522 creator A5055265483 @default.
• W4313467522 creator A5056827706 @default.
• W4313467522 date "2022-12-21" @default.
• W4313467522 modified "2023-09-26" @default.
• W4313467522 title "In silico prediction of siRNA ionizable-lipid nanoparticles In vivo efficacy: Machine learning modeling based on formulation and molecular descriptors" @default.
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• W4313467522 doi "https://doi.org/10.3389/fmolb.2022.1042720" @default.
• W4313467522 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36619167" @default.
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