AI Medical Compendium Journal:
Molecular pharmaceutics

Showing 41 to 50 of 52 articles

Geometric Deep Learning Autonomously Learns Chemical Features That Outperform Those Engineered by Domain Experts.

Molecular pharmaceutics
Artificial Intelligence has advanced at an unprecedented pace, backing recent breakthroughs in natural language processing, speech recognition, and computer vision: domains where the data is euclidean in nature. More recently, considerable progress h...

Prediction of Human Cytochrome P450 Inhibition Using a Multitask Deep Autoencoder Neural Network.

Molecular pharmaceutics
Adverse side effects of drug-drug interactions induced by human cytochrome P450 (CYP450) inhibition is an important consideration in drug discovery. It is highly desirable to develop computational models that can predict the inhibitive effect of a co...

Adversarial Threshold Neural Computer for Molecular de Novo Design.

Molecular pharmaceutics
In this article, we propose the deep neural network Adversarial Threshold Neural Computer (ATNC). The ATNC model is intended for the de novo design of novel small-molecule organic structures. The model is based on generative adversarial network archi...

3D Molecular Representations Based on the Wave Transform for Convolutional Neural Networks.

Molecular pharmaceutics
Convolutional neural networks (CNN) have been successfully used to handle three-dimensional data and are a natural match for data with spatial structure such as 3D molecular structures. However, a direct 3D representation of a molecule with atoms loc...

Prediction of Multidrug-Resistant TB from CT Pulmonary Images Based on Deep Learning Techniques.

Molecular pharmaceutics
While tuberculosis (TB) disease was discovered more than a century ago, it has not been eradicated yet. Quite contrary, at present, TB constitutes one of the top 10 causes of death and has shown signs of increasing. To complement the conventional dia...

Comparison of Deep Learning With Multiple Machine Learning Methods and Metrics Using Diverse Drug Discovery Data Sets.

Molecular pharmaceutics
Machine learning methods have been applied to many data sets in pharmaceutical research for several decades. The relative ease and availability of fingerprint type molecular descriptors paired with Bayesian methods resulted in the widespread use of t...

ADMET Evaluation in Drug Discovery. 18. Reliable Prediction of Chemical-Induced Urinary Tract Toxicity by Boosting Machine Learning Approaches.

Molecular pharmaceutics
Xenobiotic chemicals and their metabolites are mainly excreted out of our bodies by the urinary tract through the urine. Chemical-induced urinary tract toxicity is one of the main reasons that cause failure during drug development, and it is a common...

ADMET Evaluation in Drug Discovery. Part 17: Development of Quantitative and Qualitative Prediction Models for Chemical-Induced Respiratory Toxicity.

Molecular pharmaceutics
As a dangerous end point, respiratory toxicity can cause serious adverse health effects and even death. Meanwhile, it is a common and traditional issue in occupational and environmental protection. Pharmaceutical and chemical industries have a strong...

ADMET Evaluation in Drug Discovery. 16. Predicting hERG Blockers by Combining Multiple Pharmacophores and Machine Learning Approaches.

Molecular pharmaceutics
Blockade of human ether-à-go-go related gene (hERG) channel by compounds may lead to drug-induced QT prolongation, arrhythmia, and Torsades de Pointes (TdP), and therefore reliable prediction of hERG liability in the early stages of drug design is qu...

Applications of Deep Learning in Biomedicine.

Molecular pharmaceutics
Increases in throughput and installed base of biomedical research equipment led to a massive accumulation of -omics data known to be highly variable, high-dimensional, and sourced from multiple often incompatible data platforms. While this data may b...