Volume 8, Issue 4 (2022)
Pharm Biomed Res 2022, 8(4): 311-322 |
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Khaouane A, Ferhat S, Hanini S. A Novel Methodology for Human Plasma Protein Binding: Prediction, Validation, and Applicability Domain. Pharm Biomed Res 2022; 8 (4) :311-322
URL: http://pbr.mazums.ac.ir/article-1-481-en.html
URL: http://pbr.mazums.ac.ir/article-1-481-en.html
1- Laboratory of Biomaterial and Transport Phenomena, University of Médéa, Médéa, Algeria.
Abstract: (746 Views)
Background: Plasma protein binding is a key component in drug therapy as it affects the pharmacokinetics and pharmacodynamics of drugs.
Objectives: This study aimed to predict the fraction of plasma protein binding.
Methods: A quantitative structure-activity relationship, convolutional neural network, and feed-forward neural network (QSAR-CNN-FFNN) methodology was used. CNN was used for feature selection, which is known as a difficult task in QSAR studies. The values of the descriptors acquired without the preprocessing procedures were rearranged into matrices, and features from a deep fully connected layer of a pre-trained CNN (ALEXNET) were extracted. Then, the latest features learned from the CNN layers were flattened out and passed through an FFNN to make predictions.
Results: The external accuracy of the validation set (Q2=0.945, RMSE=0.085) showed the performance of this methodology. Another extremely favorable circumstance of this method is that it does not take a lot of time (only a few minutes) compared to the QSAR-Wrapper-FFNN method (days of hard work and concentration) and it automatically gives us the characteristics that are the best representations of our input.
Conclusion: We can say that this model can be used to predict the fraction of human plasma protein binding for drugs that have not been tested to avoid chemical synthesis and reduce expansive laboratory tests.
Objectives: This study aimed to predict the fraction of plasma protein binding.
Methods: A quantitative structure-activity relationship, convolutional neural network, and feed-forward neural network (QSAR-CNN-FFNN) methodology was used. CNN was used for feature selection, which is known as a difficult task in QSAR studies. The values of the descriptors acquired without the preprocessing procedures were rearranged into matrices, and features from a deep fully connected layer of a pre-trained CNN (ALEXNET) were extracted. Then, the latest features learned from the CNN layers were flattened out and passed through an FFNN to make predictions.
Results: The external accuracy of the validation set (Q2=0.945, RMSE=0.085) showed the performance of this methodology. Another extremely favorable circumstance of this method is that it does not take a lot of time (only a few minutes) compared to the QSAR-Wrapper-FFNN method (days of hard work and concentration) and it automatically gives us the characteristics that are the best representations of our input.
Conclusion: We can say that this model can be used to predict the fraction of human plasma protein binding for drugs that have not been tested to avoid chemical synthesis and reduce expansive laboratory tests.
Keywords: Quantitative structure-activity relationship, Convolutional neural network, Feed-forward neural network, Protein binding.
Type of Study: Original Research |
Subject:
Mollecular Modeling
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