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Quantitative Structure-Activity Relationship

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Chemists: AI Is Here; Unite To Get the Benefits.

Journal of medicinal chemistry
The latest developments in artificial intelligence (AI) have arrived into an existing state of creative tension between computational and medicinal chemists. At their most productive, medicinal and computational chemists have made significant progres...

Practical Applications of Deep Learning To Impute Heterogeneous Drug Discovery Data.

Journal of chemical information and modeling
Contemporary deep learning approaches still struggle to bring a useful improvement in the field of drug discovery because of the challenges of sparse, noisy, and heterogeneous data that are typically encountered in this context. We use a state-of-the...

The Study on the hERG Blocker Prediction Using Chemical Fingerprint Analysis.

Molecules (Basel, Switzerland)
Human ether-a-go-go-related gene (hERG) potassium channel blockage by small molecules may cause severe cardiac side effects. Thus, it is crucial to screen compounds for activity on the hERG channels early in the drug discovery process. In this study,...

Machine learning on drug-specific data to predict small molecule teratogenicity.

Reproductive toxicology (Elmsford, N.Y.)
Pregnant women are an especially vulnerable population, given the sensitivity of a developing fetus to chemical exposures. However, prescribing behavior for the gravid patient is guided on limited human data and conflicting cases of adverse outcomes ...

Learning Molecular Representations for Medicinal Chemistry.

Journal of medicinal chemistry
The accurate modeling and prediction of small molecule properties and bioactivities depend on the critical choice of molecular representation. Decades of informatics-driven research have relied on expert-designed molecular descriptors to establish qu...

Prototype Selection Method Based on the Rivality and Reliability Indexes for the Improvement of the Classification Models and External Predictions.

Journal of chemical information and modeling
Prototype or instance selection techniques is an important field of research in knowledge discovery, data mining, and machine learning. In QSAR, the use of prototype selection techniques in the preprocessing stage of the construction of the QSAR mode...

Using Machine Learning Methods and Structural Alerts for Prediction of Mitochondrial Toxicity.

Molecular informatics
Over the last few years more and more organ and idiosyncratic toxicities were linked to mitochondrial toxicity. Despite well-established assays, such as the seahorse and Glucose/Galactose assay, an in silico approach to mitochondrial toxicity is stil...

Computational Models Using Multiple Machine Learning Algorithms for Predicting Drug Hepatotoxicity with the DILIrank Dataset.

International journal of molecular sciences
Drug-induced liver injury (DILI) remains one of the challenges in the safety profile of both authorized and candidate drugs, and predicting hepatotoxicity from the chemical structure of a substance remains a task worth pursuing. Such an approach is c...

Prediction Model of Aryl Hydrocarbon Receptor Activation by a Novel QSAR Approach, DeepSnap-Deep Learning.

Molecules (Basel, Switzerland)
The aryl hydrocarbon receptor (AhR) is a ligand-dependent transcription factor that senses environmental exogenous and endogenous ligands or xenobiotic chemicals. In particular, exposure of the liver to environmental metabolism-disrupting chemicals c...

A Deep Learning-Based Chemical System for QSAR Prediction.

IEEE journal of biomedical and health informatics
Research on quantitative structure-activity relationships (QSAR) provides an effective approach to determine new hits and promising lead compounds during drug discovery. In the past decades, various works have gained good performance for QSAR with th...