Journal of chemical information and modeling
39801290
Machine learning (ML) models now play a crucial role in predicting properties essential to drug development, such as a drug's logscale acid-dissociation constant (p). Despite recent architectural advances, these models often generalize poorly to nove...
For the purpose of developing new drugs and repositioning existing ones, accurate drug-target affinity (DTA) prediction is essential. While graph neural networks are frequently utilized for DTA prediction, it is difficult for existing single-scale gr...
Journal of chemical information and modeling
39878060
Efficient and accurate drug-target affinity (DTA) prediction can significantly accelerate the drug development process. Recently, deep learning models have been widely applied to DTA prediction and have achieved notable success. However, existing met...
Journal of chemical information and modeling
39833138
Predicting drug-target binding affinity (DTA) is a crucial task in drug discovery research. Recent studies have demonstrated that pocket features and interactions between targets and drugs significantly improve the understanding of DTA. However, chal...
Machine learning (ML) has become an important tool for predicting the pharmaceutical properties of small molecules. Recent advancements in ML algorithms enable the rapid and accurate evaluation of solubility, activity, toxicity, pharmacokinetics, and...
Drug function prediction is a crucial task in drug discovery, design, and development, which involves the prediction of the biological functions of a drug molecule based on its chemical structure. Misleading drug function is a common reason for adver...
Accurate estimation of the solubility of solid drugs (SDs) in the supercritical carbon dioxide (SC-CO) plays an essential role in the related technologies. In this study, artificial intelligence models (AIMs) by gene expression programming (GEP) and ...
INTRODUCTION: The unbound brain-to-plasma partition coefficient (K) is an essential parameter for predicting central nervous system (CNS) drug disposition using physiologically-based pharmacokinetic (PBPK) modeling. K values for specific compounds ar...
With the advancements in algorithms and increased accessibility of multi-source data, machine learning in pharmacokinetics is gaining interest. This review summarizes studies on machine learning-based pharmacokinetics analysis up to September 2024, i...
Surface-enhanced Raman spectroscopy (SERS) technology has shown broad potential in drug concentration detection, but its application in blood drug monitoring faces significant challenges. The primary difficulty lies in overcoming the interference cau...