AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Quantitative Structure-Activity Relationship

Showing 121 to 130 of 476 articles

Clear Filters

Integrating QSAR modelling and deep learning in drug discovery: the emergence of deep QSAR.

Nature reviews. Drug discovery
Quantitative structure-activity relationship (QSAR) modelling, an approach that was introduced 60 years ago, is widely used in computer-aided drug design. In recent years, progress in artificial intelligence techniques, such as deep learning, the rap...

The use of simple structural parameters of organic compounds to assess their PUF-air partition coefficients.

Chemosphere
A novel approach is introduced for the reliable prediction of PUF-air partition coefficients of organic compounds, which can determine the environmental fate of organic compounds during interactions with air, soil, and water. The biggest accessible m...

Deep Learning for Drug Development: Using CNNs in MIA-QSAR to Predict Plasma Protein Binding of Drugs.

AAPS PharmSciTech
Predicting plasma protein binding (PPB) is crucial in drug development due to its profound impact on drug efficacy and safety. In our study, we employed a convolutional neural network (CNN) as a tool to extract valuable information from the molecular...

Exploring the potential of machine learning to design antidiabetic molecules: a comprehensive study with experimental validation.

Journal of biomolecular structure & dynamics
Recent advances in hardware and software algorithms have led to the rise of data-driven approaches for designing therapeutic modalities. One of the major causes of human mortality is diabetes. Thus, there is a tremendous opportunity for research into...

XGBoost odor prediction model: finding the structure-odor relationship of odorant molecules using the extreme gradient boosting algorithm.

Journal of biomolecular structure & dynamics
Determining the structure-odor relationship has always been a very challenging task. The main challenge in investigating the correlation between the molecular structure and its associated odor is the ambiguous and obscure nature of verbally defined o...

Cheminformatics and Machine Learning Approaches to Assess Aquatic Toxicity Profiles of Fullerene Derivatives.

International journal of molecular sciences
Fullerene derivatives (FDs) are widely used in nanomaterials production, the pharmaceutical industry and biomedicine. In the present study, we focused on the potential toxic effects of FDs on the aquatic environment. First, we analyzed the binding af...

Making the Case for Quantum Mechanics in Predictive Toxicology─Nearly 100 Years Too Late?

Chemical research in toxicology
The use of quantum mechanics (QM) has long been the norm to study covalent-binding phenomena in chemistry and biochemistry. The pharmaceutical industry leverages QM models explicitly in covalent drug discovery and implicitly to characterize short-ran...

Optimal selection of learning data for highly accurate QSAR prediction of chemical biodegradability: a machine learning-based approach.

SAR and QSAR in environmental research
Prior to the manufacture of new chemicals, regulations mandate a thorough review of the chemicals under risk management. This review involves evaluating their effects on the environment and human health. To assess these effects, a review report that ...

Advances in AI-Driven Retention Prediction for Different Chromatographic Techniques: Unraveling the Complexity.

Critical reviews in analytical chemistry
Retention prediction through Artificial intelligence (AI)-based techniques has gained exponential growth due to their abilities to process complex sets of data and ease the crucial task of identification and separation of compounds in most employed c...

What is the ecotoxicity of a given chemical for a given aquatic species? Predicting interactions between species and chemicals using recommender system techniques.

SAR and QSAR in environmental research
Ecotoxicological safety assessment of chemicals requires toxicity data on multiple species, despite the general desire of minimizing animal testing. Predictive models, specifically machine learning (ML) methods, are one of the tools capable of solvin...