Descriptor-Free Deep Learning QSAR Model for the Fraction Unbound in Human Plasma

Comparison of a QSAR model (here a random forest) with the deep

IJMS, Free Full-Text

In Silico Prediction of Compounds Binding to Human Plasma Proteins by QSAR Models - Sun - 2018 - ChemMedChem - Wiley Online Library

In silico prediction of brain exposure: drug free fraction, unbound brain to plasma concentration ratio and equilibrium half-life. - Abstract - Europe PMC

Unbound Brain-to-Plasma Partition Coefficient, Kp,uu,brain—a Game Changing Parameter for CNS Drug Discovery and Development

PDF) Development of QSAR models to predict blood-brain barrier permeability

In Silico Prediction of Fraction Unbound in Human Plasma from Chemical Fingerprint Using Automated Machine Learning

QSAR Development for Plasma Protein Binding: Influence of the Ionization State

Descriptor-Free Deep Learning QSAR Model for the Fraction Unbound in Human Plasma

Descriptor-Free Deep Learning QSAR Model for the Fraction Unbound in Human Plasma

Development of a 2D-QSAR Model for Tissue-to-Plasma Partition Coefficient Value with High Accuracy Using Machine Learning Method, Minimum Required Experimental Values, and Physicochemical Descriptors

Bayer's in silico ADMET platform: a journey of machine learning over the past two decades - ScienceDirect

Evaluation of quantitative structure property relationship algorithms for predicting plasma protein binding in humans - ScienceDirect

Frontiers Deep Learning-Based Structure-Activity Relationship Modeling for Multi-Category Toxicity Classification: A Case Study of 10K Tox21 Chemicals With High-Throughput Cell-Based Androgen Receptor Bioassay Data

A Novel Methodology for Human Plasma Protein Binding: Prediction Validation and Applicability Domain - Pharmaceutical and Biomedical Research