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Quantitative Structure-Activity Relationship

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In Silico Prediction of O⁶-Methylguanine-DNA Methyltransferase Inhibitory Potency of Base Analogs with QSAR and Machine Learning Methods.

Molecules (Basel, Switzerland)
O⁶-methylguanine-DNA methyltransferase (MGMT), a unique DNA repair enzyme, can confer resistance to DNA anticancer alkylating agents that modify the O⁶-position of guanine. Thus, inhibition of MGMT activity in tumors has a great interest for cancer r...

Sparse Generative Topographic Mapping for Both Data Visualization and Clustering.

Journal of chemical information and modeling
To achieve simultaneous data visualization and clustering, the method of sparse generative topographic mapping (SGTM) is developed by modifying the conventional GTM algorithm. While the weight of each grid point is constant in the original GTM, it be...

Boosted feature selectors: a case study on prediction P-gp inhibitors and substrates.

Journal of computer-aided molecular design
Feature selection is commonly used as a preprocessing step to machine learning for improving learning performance, lowering computational complexity and facilitating model interpretation. This paper proposes the application of boosting feature select...

The hepatotoxic potential of protein kinase inhibitors predicted with Random Forest and Artificial Neural Networks.

Toxicology letters
Protein kinases (PKs) play a role in many pivotal aspects of cellular function. Dysregulation and mutations of protein kinases are involved in the development of different diseases, which might be treated by inhibition of the corresponding kinase. Pr...

Artificial Intelligence in Drug Design.

Molecules (Basel, Switzerland)
Artificial Intelligence (AI) plays a pivotal role in drug discovery. In particular artificial neural networks such as deep neural networks or recurrent networks drive this area. Numerous applications in property or activity predictions like physicoch...

Predictive classifier models built from natural products with antimalarial bioactivity using machine learning approach.

PloS one
In view of the vast number of natural products with potential antiplasmodial bioactivity and cost of conducting antiplasmodial bioactivity assays, it may be judicious to learn from previous antiplasmodial bioassays and predict bioactivity of these na...

Modeling adsorption of organic pollutants onto single-walled carbon nanotubes with theoretical molecular descriptors using MLR and SVM algorithms.

Chemosphere
Prediction of adsorption equilibrium coefficients (K) of organic compounds onto single walled carbon nanotubes (SWNTs) from in silico molecular descriptors is of importance for probing potential applications of SWNTs as well as for evaluating environ...

Discovering Highly Potent Molecules from an Initial Set of Inactives Using Iterative Screening.

Journal of chemical information and modeling
The versatility of similarity searching and quantitative structure-activity relationships to model the activity of compound sets within given bioactivity ranges (i.e., interpolation) is well established. However, their relative performance in the com...

Extraction, identification and structure-activity relationship of antioxidant peptides from sesame (Sesamum indicum L.) protein hydrolysate.

Food research international (Ottawa, Ont.)
To elucidate the sequence, origin and structure-activity relationship of antioxidant peptides from sesame protein, sesame protein was hydrolysed by a dual-enzyme system comprised alcalase and trypsin, then this hydrolysate was fractionated by ultrafi...