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A Bayesian Approach to in Silico Blood-Brain Barrier Penetration Modeling

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Molecular Descriptors for Chemoinformatics

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Best Practices for QSAR Model Development, Validation, and Exploitation

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Artificial Neural Networks in ADMET Modeling: Prediction of Blood–Brain Barrier Permeation

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Classification of Blood‐Brain Barrier Permeation by Kohonen's Self‐Organizing Neural Network (KohNN) and Support Vector Machine (SVM)

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Pattern Classification

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Virtual Computational Chemistry Laboratory – Design and Description

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Qualitative prediction of blood–brain barrier permeability on a large and refined dataset

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New Predictive Models for Blood–Brain Barrier Permeability of Drug-like Molecules

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QSAR Modeling of the Blood–Brain Barrier Permeability for Diverse Organic Compounds

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A Turning Point For Blood–Brain Barrier Modeling

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Blood–brain barrier delivery

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High throughput artificial membrane permeability assay for blood–brain barrier

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A simple model to predict blood–brain barrier permeation from 3D molecular fields

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VolSurf: a new tool for the pharmacokinetic optimization of lead compounds

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Computing chemistry on the web

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Bioinformatics: The Machine Learning Approach by P. Baldi and S. Brunak, 2nd edn, MIT Press, 452 pp., $60.00, ISBN 0-262-02506-X

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SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules

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ADME Evaluation in Drug Discovery. 3. Modeling Blood-Brain Barrier Partitioning Using Simple Molecular Descriptors

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Random Forest: A Classification and Regression Tool for Compound Classification and QSAR Modeling

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Blood-Brain Barrier Permeation Models: Discriminating between Potential CNS and Non-CNS Drugs Including P-Glycoprotein Substrates

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Constructing Optimum Blood Brain Barrier QSAR Models Using a Combination of 4D-Molecular Similarity Measures and Cluster Analysis

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Effect of Selection of Molecular Descriptors on the Prediction of Blood−Brain Barrier Penetrating and Nonpenetrating Agents by Statistical Learning Methods

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The Blue Obelisk—Interoperability in Chemical Informatics

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In Silico Prediction of Blood−Brain Barrier Permeation Using the Calculated Molecular Cross-Sectional Area as Main Parameter

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Predicting Penetration Across the Blood-Brain Barrier from Simple Descriptors and Fragmentation Schemes

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Insights for Predicting Blood-Brain Barrier Penetration of CNS Targeted Molecules Using QSPR Approaches

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Prediction of Physicochemical Parameters by Atomic Contributions

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Fast Calculation of Molecular Polar Surface Area as a Sum of Fragment-Based Contributions and Its Application to the Prediction of Drug Transport Properties

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Designing Libraries with CNS Activity

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Predicting Blood−Brain Barrier Permeation from Three-Dimensional Molecular Structure

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Predicting Blood–Brain Barrier Partitioning of Organic Molecules Using Membrane–Interaction QSAR Analysis

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Predicting CNS Permeability of Drug Molecules: Comparison of Neural Network and Support Vector Machine Algorithms

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In Silico Predictions of Blood-Brain Barrier Penetration: Considerations to “Keep in Mind”

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LIBSVM

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ADDME – Avoiding Drug Development Mistakes Early: central nervous system drug discovery perspective

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Characteristics of compounds that cross the blood-brain barrier

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The blood-brain barrier: Bottleneck in brain drug development

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A novel hypothesis of blood-brain barrier (BBB) development and in vitro BBB model: neural stem cell is the driver of BBB formation and maintenance

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gSelformer-MV: Multiview, Subgraph-Augmented Group SELFIES Transformer for Molecular Property Prediction

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Functional Groups Are All You Need for Chemically Interpretable Molecular Property Prediction

inferred← citeshttps://doi.org/10.1021/acs.jcim.6c00638openalex-snapshot

ChemLint: Conversational Cheminformatics with Large Language Models

inferred← citeshttps://doi.org/10.1021/acs.jcim.6c00981openalex-snapshot

Uncertainty Quantification with Domain Classification Models for Acetylcholinesterase Inhibition

inferred← citeshttps://doi.org/10.1021/acs.jctc.2c01235openalex-snapshot

A Perspective on Explanations of Molecular Prediction Models

inferred← citeshttps://doi.org/10.1021/acs.jmedchem.2c01824openalex-snapshot

Explaining Blood–Brain Barrier Permeability of Small Molecules by Integrated Analysis of Different Transport Mechanisms

inferred← citeshttps://doi.org/10.1021/acs.jmedchem.3c02446openalex-snapshot

Pharmacokinetics Profiler (PhaKinPro): Model Development, Validation, and Implementation as a Web Tool for Triaging Compounds with Undesired Pharmacokinetics Profiles

inferred← citeshttps://doi.org/10.1021/acs.jmedchem.5c00493openalex-snapshot

Discovery of CYP1A1 Inhibitors for Host-Directed Therapy against Sepsis

inferred← citeshttps://doi.org/10.1021/acs.jmedchem.6c00049openalex-snapshot

In Silico ADMET: From Current Practices to Novel Profilers

inferred← citeshttps://doi.org/10.1021/acs.jmedchem.9b02147openalex-snapshot

Transfer Learning for Drug Discovery

inferred← citeshttps://doi.org/10.1021/acs.jpcb.6c00810openalex-snapshot

Development and Test of Highly Accurate End Point Free Energy Methods. 4. Expanding Solvents Capability and logBB Prediction

inferred← citeshttps://doi.org/10.1021/acs.jpclett.1c03058openalex-snapshot

Extracting Predictive Representations from Hundreds of Millions of Molecules

inferred← citeshttps://doi.org/10.1021/acschemneuro.3c00840openalex-snapshot

Identifying Substructures That Facilitate Compounds to Penetrate the Blood–Brain Barrier via Passive Transport Using Machine Learning Explainer Models

inferred← citeshttps://doi.org/10.1021/acsmedchemlett.6c00347openalex-snapshot

A-iMPO: A Design-Time Prediction Toolbox for Blood Brain Barrier Permeation Using an Integrated and Interpretable Multiparameter Optimization Framework

inferred← citeshttps://doi.org/10.1021/acsomega.1c05679openalex-snapshot

Atomistic Model of Solute Transport across the Blood–Brain Barrier

inferred← citeshttps://doi.org/10.1021/acsomega.3c04073openalex-snapshot

Predictive Models Based on Molecular Images and Molecular Descriptors for Drug Screening

inferred← citeshttps://doi.org/10.1021/acsomega.3c09744openalex-snapshot

Toward Quantum-Informed Atom Pairs

inferred← citeshttps://doi.org/10.1021/acsomega.4c09884openalex-snapshot

MulAFNet: Integrating Multiple Molecular Representations for Enhanced Property Prediction

inferred← citeshttps://doi.org/10.1021/acsomega.8b03328openalex-snapshot

Predicting Blood–Brain Partitioning of Small Molecules Using a Novel Minimalistic Descriptor-Based Approach via the 3D-RISM-KH Molecular Solvation Theory

inferred← citeshttps://doi.org/10.1021/acsomega.9b01512openalex-snapshot

To Pass or Not To Pass: Predicting the Blood–Brain Barrier Permeability with the 3D-RISM-KH Molecular Solvation Theory

inferred← citeshttps://doi.org/10.1021/bk-2016-1222.ch014openalex-snapshot

Steps Toward a Virtual Rat: Predictive Absorption, Distribution, Metabolism, and Toxicity Models

inferred← citeshttps://doi.org/10.1021/jacs.4c04670openalex-snapshot

When Do Quantum Mechanical Descriptors Help Graph Neural Networks to Predict Chemical Properties?

inferred← citeshttps://doi.org/10.1038/s41467-021-23720-wopenalex-snapshot

Algebraic graph-assisted bidirectional transformers for molecular property prediction

inferred← citeshttps://doi.org/10.1038/s41467-024-55462-wopenalex-snapshot

Coverage bias in small molecule machine learning

inferred← citeshttps://doi.org/10.1038/s41467-025-59634-0openalex-snapshot

A self-conformation-aware pre-training framework for molecular property prediction with substructure interpretability

inferred← citeshttps://doi.org/10.1038/s41467-025-66685-wopenalex-snapshot

Molecular Motif Learning as a pretraining objective for molecular property prediction

inferred← citeshttps://doi.org/10.1038/s41597-021-01069-5openalex-snapshot

A curated diverse molecular database of blood-brain barrier permeability with chemical descriptors

inferred← citeshttps://doi.org/10.1038/s41597-022-01288-4openalex-snapshot

GEOM, energy-annotated molecular conformations for property prediction and molecular generation

inferred← citeshttps://doi.org/10.1038/s41597-024-03793-0openalex-snapshot

PharmaBench: Enhancing ADMET benchmarks with large language models

inferred← citeshttps://doi.org/10.1038/s41598-021-90259-7openalex-snapshot

A merged molecular representation learning for molecular properties prediction with a web-based service

inferred← citeshttps://doi.org/10.1038/s41598-024-76440-8openalex-snapshot

Comparing SMILES and SELFIES tokenization for enhanced chemical language modeling

inferred← citeshttps://doi.org/10.1038/s41598-025-33443-3openalex-snapshot

Bidirectional reinforcement learning neural network for constrained molecular design

inferred← citeshttps://doi.org/10.1038/s41598-025-92218-yopenalex-snapshot

Integrating convolutional layers and biformer network with forward-forward and backpropagation training

inferred← citeshttps://doi.org/10.1038/s42003-025-09064-xopenalex-snapshot

MoleculeFormer is a GCN-transformer architecture for molecular property prediction

inferred← citeshttps://doi.org/10.1038/s42004-024-01133-2openalex-snapshot

Efficient retrosynthetic planning with MCTS exploration enhanced A* search

inferred← citeshttps://doi.org/10.1038/s42004-024-01169-4openalex-snapshot

Fast and effective molecular property prediction with transferability map

inferred← citeshttps://doi.org/10.1038/s42256-021-00438-4openalex-snapshot

Geometry-enhanced molecular representation learning for property prediction

inferred← citeshttps://doi.org/10.1038/s42256-023-00683-9openalex-snapshot

Application of variational graph encoders as an effective generalist algorithm in computer-aided drug design

inferred← citeshttps://doi.org/10.1038/s43246-022-00315-6openalex-snapshot

Graph neural networks for materials science and chemistry

inferred← citeshttps://doi.org/10.1038/s43588-024-00699-0openalex-snapshot

The future of machine learning for small-molecule drug discovery will be driven by data

inferred← citeshttps://doi.org/10.1039/c3ob41302kopenalex-snapshot

Regio- and stereoselective synthesis of spirooxindole 1′-nitro pyrrolizidines with five concurrent stereocenters under aqueous medium and their bioprospection using the zebrafish (Danio rerio) embryo model

inferred← citeshttps://doi.org/10.1039/c7sc02664aopenalex-snapshot

MoleculeNet: a benchmark for molecular machine learning

inferred← citeshttps://doi.org/10.1039/c8sc04175jopenalex-snapshot

Learning continuous and data-driven molecular descriptors by translating equivalent chemical representations

inferred← citeshttps://doi.org/10.1039/d0cp02709jopenalex-snapshot

Machine-guided representation for accurate graph-based molecular machine learning

inferred← citeshttps://doi.org/10.1039/d1sc05259dopenalex-snapshot

Model agnostic generation of counterfactual explanations for molecules

inferred← citeshttps://doi.org/10.1039/d2dd00099gopenalex-snapshot

Chemical representation learning for toxicity prediction

inferred← citeshttps://doi.org/10.1039/d2dd00146bopenalex-snapshot

Calibration and generalizability of probabilistic models on low-data chemical datasets with DIONYSUS

inferred← citeshttps://doi.org/10.1039/d4dd00162aopenalex-snapshot

Learning on compressed molecular representations

inferred← citeshttps://doi.org/10.1039/d4sc08736dopenalex-snapshot

Multi-objective drug design with a scaffold-aware variational autoencoder

inferred← citeshttps://doi.org/10.1039/d5cp00785bopenalex-snapshot

Pre-trained language models for protein and molecular design

inferred← citeshttps://doi.org/10.1063/5.0113755openalex-snapshot

Comparison of potential compounds Saussureamine D and Saussureamine E from the plant Saussurea costus falc. as a inhibitor of SARS CoV-2 RNA-dependent RNA-polymerase (RDRP) in silico

inferred← citeshttps://doi.org/10.1080/07391102.2024.2326671openalex-snapshot

ANN multi-layer perceptron for prediction of blood–brain barrier permeable compounds for central nervous system therapeutics

inferred← citeshttps://doi.org/10.1080/10255842.2026.2661853openalex-snapshot

Multi-modal graph neural networks with cross-view attention and contrastive learning for molecular property prediction

inferred← citeshttps://doi.org/10.1080/1062936x.2019.1588160openalex-snapshot

Filter feature selectors in the development of binary QSAR models

inferred← citeshttps://doi.org/10.1080/1062936x.2023.2230868openalex-snapshot

Utilizing machine learning techniques to predict the blood-brain barrier permeability of compounds detected using LCQTOF-MS in Malaysian Kelulut honey

inferred← citeshttps://doi.org/10.1080/1062936x.2024.2446352openalex-snapshot

Uncovering blood–brain barrier permeability: a comparative study of machine learning models using molecular fingerprints, and SHAP explainability

inferred← citeshttps://doi.org/10.1080/17425255.2017.1316449openalex-snapshot

Characterisation of data resources forin silicomodelling: benchmark datasets for ADME properties

inferred← citeshttps://doi.org/10.1080/17460441.2021.1925247openalex-snapshot

Assigning confidence to molecular property prediction

inferred← citeshttps://doi.org/10.1088/2632-2153/acdb30openalex-snapshot

SELFormer: molecular representation learning via SELFIES language models

inferred← citeshttps://doi.org/10.1093/bib/bbab317openalex-snapshot

Mol2Context-vec: learning molecular representation from context awareness for drug discovery

inferred← citeshttps://doi.org/10.1093/bib/bbac303openalex-snapshot

Attention-wise masked graph contrastive learning for predicting molecular property

inferred← citeshttps://doi.org/10.1093/bib/bbac357openalex-snapshot

A merged molecular representation deep learning method for blood–brain barrier permeability prediction

inferred← citeshttps://doi.org/10.1093/bib/bbac408openalex-snapshot

FP-GNN: a versatile deep learning architecture for enhanced molecular property prediction

inferred← citeshttps://doi.org/10.1093/bib/bbac566openalex-snapshot

CasANGCL: pre-training and fine-tuning model based on cascaded attention network and graph contrastive learning for molecular property prediction

inferred← citeshttps://doi.org/10.1093/bib/bbad398openalex-snapshot

FG-BERT: a generalized and self-supervised functional group-based molecular representation learning framework for properties prediction

inferred← citeshttps://doi.org/10.1093/bib/bbad423openalex-snapshot

MESPool: Molecular Edge Shrinkage Pooling for hierarchical molecular representation learning and property prediction

inferred← citeshttps://doi.org/10.1093/bib/bbae340openalex-snapshot

Gram matrix: an efficient representation of molecular conformation and learning objective for molecular pretraining

inferred← citeshttps://doi.org/10.1093/bib/bbae474openalex-snapshot

DGCL: dual-graph neural networks contrastive learning for molecular property prediction

inferred← citeshttps://doi.org/10.1093/bib/bbag010openalex-snapshot

Task-specific pre-training for molecular property prediction

inferred← citeshttps://doi.org/10.1093/bib/bbag491openalex-snapshot

MSF-HierGNN: a multi-source substructure-fusion hierarchical GNN method and web server to predict molecular property for drug design

inferred← citeshttps://doi.org/10.1093/bib/bbz125openalex-snapshot

A novel molecular representation with BiGRU neural networks for learning atom

inferred← citeshttps://doi.org/10.1093/bioinformatics/btaa918openalex-snapshot

LightBBB: computational prediction model of blood–brain-barrier penetration based on LightGBM

inferred← citeshttps://doi.org/10.1093/bioinformatics/btac211openalex-snapshot

Relational graph convolutional networks for predicting blood–brain barrier penetration of drug molecules

inferred← citeshttps://doi.org/10.1093/bioinformatics/btac550openalex-snapshot

MultiGran-SMILES: multi-granularity SMILES learning for molecular property prediction

inferred← citeshttps://doi.org/10.1093/bioinformatics/btad577openalex-snapshot

A machine learning-based quantitative model (LogBB_Pred) to predict the blood–brain barrier permeability (logBB value) of drug compounds

inferred← citeshttps://doi.org/10.1093/bioinformatics/btae256openalex-snapshot

MolPLA: a molecular pretraining framework for learning cores, R-groups and their linker joints

inferred← citeshttps://doi.org/10.1093/bioinformatics/btag028openalex-snapshot

MMPCS: multi-view molecular pretraining based on consistency information and specific information

inferred← citeshttps://doi.org/10.1093/bioinformatics/btag550openalex-snapshot

The limits of bio-molecular modeling with large language models: a cross-scale evaluation

inferred← citeshttps://doi.org/10.1093/ijnp/pyu078openalex-snapshot

A Tool to Utilize Adverse Effect Profiles to Identify Brain-Active Medications for Repurposing

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Variational graph encoders: a surprisingly effective generalist algorithm for holistic computer-aided drug design

inferred← citeshttps://doi.org/10.1101/2024.10.02.616275openalex-snapshot

A Predictive Model for Compound-Protein Interactions Based on Concatenated Vectorization

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Improving ADMET prediction with descriptor augmentation of Mol2Vec embeddings

inferred← citeshttps://doi.org/10.1101/2025.08.09.669511openalex-snapshot

Scaling Molecular Representation Learning with Hierarchical Mixture-of-Experts

inferred← citeshttps://doi.org/10.1101/2025.08.27.672581openalex-snapshot

Predicting Organ-Specific Toxicity of Selective Androgen Receptor Modulators, using Transfer Learning on Graph Convolutional Networks

inferred← citeshttps://doi.org/10.1101/2025.09.30.679573openalex-snapshot

Ontology pre-training improves machine learning-based predictions for metabolites

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