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dc.contributor.author Kumar, Rajnish
dc.contributor.author Sharma, Anju
dc.contributor.author Alexiou, Athanasios
dc.contributor.author Bilgrami, Anwar L.
dc.contributor.author Kamal, Mohammad Amjad
dc.contributor.author Ashraf, Ghulam Md
dc.date.accessioned 2024-02-22T07:09:10Z
dc.date.available 2024-02-22T07:09:10Z
dc.date.issued 2022-05-03
dc.identifier.uri http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/11499
dc.description.abstract The blood-brain barrier (BBB) is a selective and semipermeable boundary that maintains homeostasis inside the central nervous system (CNS). The BBB permeability of compounds is an important consideration during CNS-acting drug development and is difficult to formulate in a succinct manner. Clinical experiments are the most accurate method of measuring BBB permeability. However, they are time taking and labor-intensive. Therefore, numerous efforts have been made to predict the BBB permeability of compounds using computational methods. However, the accuracy of BBB permeability prediction models has always been an issue. To improve the accuracy of the BBB permeability prediction, we applied deep learning and machine learning algorithms to a dataset of 3,605 diverse compounds. Each compound was encoded with 1,917 features containing 1,444 physicochemical (1D and 2D) properties, 166 molecular access system fingerprints (MACCS), and 307 substructure fingerprints. The prediction performance metrics of the developed models were compared and analyzed. The prediction accuracy of the deep neural network (DNN), one-dimensional convolutional neural network, and convolutional neural network by transfer learning was found to be 98.07, 97.44, and 97.61%, respectively. The best performing DNN-based model was selected for the development of the “DeePred-BBB” model, which can predict the BBB permeability of compounds using their simplified molecular input line entry system (SMILES) notations. It could be useful in the screening of compounds based on their BBB permeability at the preliminary stages of drug development. The DeePred-BBB is made available at https://github.com/12rajnish/DeePred-BBB. en_US
dc.language.iso en_US en_US
dc.publisher Daffodil International University en_US
dc.subject Machine learning en_US
dc.subject Algorithms en_US
dc.title DeePred-BBB en_US
dc.title.alternative A Blood Brain Barrier Permeability Prediction Model With Improved Accuracy en_US
dc.type Article en_US


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