AIMC Topic: Blood-Brain Barrier

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Machine learning based dynamic consensus model for predicting blood-brain barrier permeability.

Computers in biology and medicine Apr 28, 2023
The blood-brain barrier (BBB) is an important defence mechanism that restricts disease-causing pathogens and toxins to enter the brain from the bloodstream. In recent years, many in silico methods were proposed for predicting BBB permeability, howeve...
Blood-Brain Barrier Consensus Permeability Machine Learning Reproducibility of Results
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DeepBBBP: High Accuracy Blood-brain-barrier Permeability Prediction with a Mixed Deep Learning Model.

Molecular informatics Apr 25, 2022
Blood-brain-barrier permeability (BBBP) is an important property that is used to establish the drug-likeness of a molecule, as it establishes whether the molecule can cross the BBB when desired. It also eliminates those molecules which are not suppos...
Blood-Brain Barrier Neural Networks, Computer Deep Learning Machine Learning Permeability
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Machine learning and deep learning in data-driven decision making of drug discovery and challenges in high-quality data acquisition in the pharmaceutical industry.

Future medicinal chemistry Dec 23, 2021
Predicting novel small molecule bioactivities for the target deconvolution, hit-to-lead optimization in drug discovery research, requires molecular representation. Previous reports have demonstrated that machine learning (ML) and deep learning (DL) h...
Machine Learning Humans Blood-Brain Barrier Drug Repositioning Artificial Intelligence Drug Delivery Systems Decision Making Drug Industry Drug Discovery Deep Learning
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Prediction of Blood-Brain Barrier Penetration (BBBP) Based on Molecular Descriptors of the Free-Form and In-Blood-Form Datasets.

Molecules (Basel, Switzerland) Dec 7, 2021
The blood-brain barrier (BBB) controls the entry of chemicals from the blood to the brain. Since brain drugs need to penetrate the BBB, rapid and reliable prediction of BBB penetration (BBBP) is helpful for drug development. In this study, free-form ...
Humans Neural Networks, Computer Machine Learning Molecular Structure Biological Transport Amines Databases, Factual Blood-Brain Barrier
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Quantum Artificial Neural Network Approach to Derive a Highly Predictive 3D-QSAR Model for Blood-Brain Barrier Passage.

International journal of molecular sciences Oct 12, 2021
A successful passage of the blood-brain barrier (BBB) is an essential prerequisite for the drug molecules designed to act on the central nervous system. The logarithm of blood-brain partitioning (LogBB) has served as an effective index of molecular B...
Quantum Theory Quantitative Structure-Activity Relationship Neural Networks, Computer Blood-Brain Barrier Pharmaceutical Preparations Biological Transport Models, Molecular
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Algebraic graph-assisted bidirectional transformers for molecular property prediction.

Nature communications Jun 10, 2021
The ability of molecular property prediction is of great significance to drug discovery, human health, and environmental protection. Despite considerable efforts, quantitative prediction of various molecular properties remains a challenge. Although s...
Databases, Chemical Molecular Conformation Drug-Related Side Effects and Adverse Reactions Hydrophobic and Hydrophilic Interactions Algorithms Neural Networks, Computer Computer Simulation Drug Discovery Machine Learning Blood-Brain Barrier Pharmaceutical Preparations
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Prediction of the Blood-Brain Barrier (BBB) Permeability of Chemicals Based on Machine-Learning and Ensemble Methods.

Chemical research in toxicology May 28, 2021
The ability of chemicals to enter the blood-brain barrier (BBB) is a key factor for central nervous system (CNS) drug development. Although many models for BBB permeability prediction have been developed, they have insufficient accuracy (ACC) and sen...
Blood-Brain Barrier Organic Chemicals Permeability Humans Machine Learning
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Direct Comparison of the Prediction of the Unbound Brain-to-Plasma Partitioning Utilizing Machine Learning Approach and Mechanistic Neuropharmacokinetic Model.

The AAPS journal May 18, 2021
The mechanistic neuropharmacokinetic (neuroPK) model was established to predict unbound brain-to-plasma partitioning (K) by considering in vitro efflux activities of multiple drug resistance 1 (MDR1) and breast cancer resistance protein (BCRP). Herei...
ATP Binding Cassette Transporter, Subfamily G, Member 2 Models, Animal Animals Machine Learning Male ATP Binding Cassette Transporter, Subfamily B Predictive Value of Tests Blood-Brain Barrier Rats Models, Biological Dogs Madin Darby Canine Kidney Cells Datasets as Topic
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Predicting Absorption-Distribution Properties of Neuroprotective Phosphine-Borane Compounds Using In Silico Modeling and Machine Learning.

Molecules (Basel, Switzerland) Apr 25, 2021
Phosphine-borane complexes are novel chemical entities with preclinical efficacy in neuronal and ophthalmic disease models. In vitro and in vivo studies showed that the metabolites of these compounds are capable of cleaving disulfide bonds implicated...
Neuroprotection Humans Phosphines Machine Learning Protein Binding Computer Simulation Boranes Neuroprotective Agents Blood-Brain Barrier
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Artificial neural network approach for predicting blood brain barrier permeability based on a group contribution method.

Computer methods and programs in biomedicine Jan 15, 2021
BACKGROUND AND OBJECTIVE: The purpose of this study was to develop a quantitative structure-activity relationship (QSAR) model for the prediction of blood brain barrier (BBB) permeability by using artificial neural networks (ANN) in combination with ...
Quantitative Structure-Activity Relationship Blood-Brain Barrier Permeability Neural Networks, Computer Databases, Factual
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