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Quantitative Structure-Activity Relationship

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Predicting drug solubility in organic solvents mixtures: A machine-learning approach supported by high-throughput experimentation.

International journal of pharmaceutics
A novel approach based on supervised machine-learning is proposed to predict the solubility of drugs and drug-like molecules in mixtures of organic solvents. Similar to quantitative structure-property relationship (QSPR) models, different solvent typ...

Prediction of Skin Sensitization for Compounds Flexible Evidence Combination Based on Machine Learning and Dempster-Shafer Theory.

Chemical research in toxicology
Skin sensitization is increasingly becoming a significant concern in the development of drugs and cosmetics due to consumer safety and occupational health problems. methods have emerged as alternatives to traditional animal testing due to ethical a...

Antibiotic discovery with artificial intelligence for the treatment of infections.

mSystems
UNLABELLED: Global challenges presented by multidrug-resistant infections have stimulated the development of new treatment strategies. We reported that outer membrane protein W (OmpW) is a potential therapeutic target in . Here, a library of 11,648 ...

Breaking the Barriers: Machine-Learning-Based c-RASAR Approach for Accurate Blood-Brain Barrier Permeability Prediction.

Journal of chemical information and modeling
The intricate nature of the blood-brain barrier (BBB) poses a significant challenge in predicting drug permeability, which is crucial for assessing central nervous system (CNS) drug efficacy and safety. This research utilizes an innovative approach, ...

Reactivities of acrylamide warheads toward cysteine targets: a QM/ML approach to covalent inhibitor design.

Journal of computer-aided molecular design
Covalent inhibition offers many advantages over non-covalent inhibition, but covalent warhead reactivity must be carefully balanced to maintain potency while avoiding unwanted side effects. While warhead reactivities are commonly measured with assays...

Development of Novel Methods for QSAR Modeling by Machine Learning Repeatedly: A Case Study on Drug Distribution to Each Tissue.

Journal of chemical information and modeling
Artificial intelligence is expected to help identify excellent candidates in drug discovery. However, we face a lack of data, as it is time-consuming and expensive to acquire raw data perfectly for many compounds. Hence, we tried to develop a novel q...

Application of parallel artificial membrane permeability assay technique and chemometric modeling for blood-brain barrier permeability prediction of protein kinase inhibitors.

Future medicinal chemistry
This study aims to investigate the passive diffusion of protein kinase inhibitors through the blood-brain barrier (BBB) and to develop a model for their permeability prediction. We used the parallel artificial membrane permeability assay to obtain ...

An artificial intelligence-based model for predicting reproductive toxicity of bisphenol analogues mixtures to the rotifer Brachionus calyciflorus.

The Science of the total environment
The joint toxicity effects of mixtures, particularly reproductive toxicity, one of the main causes of aquatic ecosystem degradation, are often overlooked as it is impractical to test all mixtures. This study developed and evaluated the following mode...

Chemometrics-driven prediction and prioritization of diverse pesticides on chickens for addressing hazardous effects on public health.

Journal of hazardous materials
The extensive use of various pesticides in the agriculture field badly affects both chickens and humans, primarily through residues in food products and environmental exposure. This study offers the first quantitative structure-toxicity relationship ...