Artificial Neural Networks for In-silico Experiments on Perception

In silico prediction of the in vivo efficacy of siRNA ionizable-lipid nanoparticles is desirable yet never achieved before. This study aims to computationally predict siRNA nanoparticles in vivo efficacy, which saves time and resources. 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(dose) with R2val = 0.86 – 0.89 and 0.75 – 80 for validation sets one and two respectively. Artificial neural networks resulted in the best R2val for both validation sets. For predictions that have high bias, improvement of R2val 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.

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In silico modeling of pharmaceutical formulation using artificial neural networks

2009 International Conference on Biomedical and Pharmaceutical Engineering ◽

10.1109/icbpe.2009.5384085 ◽

2009 ◽

Cited By ~ 1

Author(s):

S. Piriyaprasarth ◽

V. Patomchaiviwat ◽

P. Sriamonsak

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

In Silico ◽

Pharmaceutical Formulation ◽

In Silico Modeling ◽

Artificial Neural

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Assessment of Drug Proarrhythmicity Using Artificial Neural Networks With in silico Deterministic Model Outputs

Frontiers in Physiology ◽

10.3389/fphys.2021.761691 ◽

2021 ◽

Vol 12 ◽

Author(s):

Yedam Yoo ◽

Aroli Marcellinus ◽

Da Un Jeong ◽

Ki-Suk Kim ◽

Ki Moo Lim

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

High Risk ◽

In Silico ◽

Drug Toxicity ◽

Risk Group ◽

Low Risk ◽

Intermediate Risk ◽

Ann Model ◽

Artificial Neural

As part of the Comprehensive in vitro Proarrhythmia Assay initiative, methodologies for predicting the occurrence of drug-induced torsade de pointes via computer simulations have been developed and verified recently. However, their predictive performance still requires improvement. Herein, we propose an artificial neural networks (ANN) model that uses nine multiple input features, considering the action potential morphology, calcium transient morphology, and charge features to further improve the performance of drug toxicity evaluation. The voltage clamp experimental data for 28 drugs were augmented to 2,000 data entries using an uncertainty quantification technique. By applying these data to the modified O’Hara Rudy in silico model, nine features (dVm/dtmax, APresting, APD90, APD50, Caresting, CaD90, CaD50, qNet, and qInward) were calculated. These nine features were used as inputs to an ANN model to classify drug toxicity into high-risk, intermediate-risk, and low-risk groups. The model was trained with data from 12 drugs and tested using the data of the remaining 16 drugs. The proposed ANN model demonstrated an AUC of 0.92 in the high-risk group, 0.83 in the intermediate-risk group, and 0.98 in the low-risk group. This was higher than the classification performance of the method proposed in previous studies.

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