DeePred-BBB: A Blood Brain Barrier Permeability Prediction Model With Improved Accuracy

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.

Alvascience: A New Software Suite for the QSAR Workflow Applied to the Blood–Brain Barrier Permeability

“…The BBB permeability is a relevant endpoint for drug discovery when designing compounds to target or avoid the CNS [ 60 ]. Many models for BBB permeation prediction have been developed over the years [ 35 , 36 , 61 ] also because computational methods are considered an interesting alternative to the experimental determination, which is difficult and expensive [ 62 ]. The selected study, to the best of our knowledge, presented the biggest dataset related to BBB containing 7807 molecules gathered from 50 published resources.…”

Drug repurposing using meta-analysis of gene expression in Alzheimer’s disease

“…The blood-brain barrier (BBB) permeability of the compounds in each case was predicted by the DeepPred-BBB model ( Kumar et al, 2022 ) using their canonical simplified molecular input line entry system (SMILES). The SMILES strings were obtained from PubChem ( Kim et al, 2021 ).…”

Re-Routing Drugs to Blood Brain Barrier: A Comprehensive Analysis of Machine Learning Approaches With Fingerprint Amalgamation and Data Balancing

“…BBB acts as a physical and metabolic barrier with simultaneous transport and secretory functions. Clinical determination of the BBB permeability of compounds, though accurate is time-consuming, costineffective and impractical for diverse drug candidates [4], [5], [6]. Alternatively, in vitro and in vivo animal models are employed to identify the permeability properties.…”

“…Recent years have seen unprecedented applications of AI/ML methods in addressing diverse problems ranging from medical image analysis [14], [15], [16] to drug discovery [17]. Several AI/ML-based models have been proposed to facilitate expeditious CNS drug discovery/repurposing by minimizing the number of laborious and time-consuming BBB permeability studies [4], [18], [19], [20], [21]. Several approaches for the identification and optimal generation of key molecular properties that are involved in BBB permeability have been reported.…”