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Drug Evaluation, Preclinical

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Prediction of Membrane Permeation of Drug Molecules by Combining an Implicit Membrane Model with Machine Learning.

Journal of chemical information and modeling
Lipid membrane permeation of drug molecules was investigated with Heterogeneous Dielectric Generalized Born (HDGB)-based models using solubility-diffusion theory and machine learning. Free energy profiles were obtained for neutral molecules by the st...

Dry olive leaf extract attenuates DNA damage induced by estradiol and diethylstilbestrol in human peripheral blood cells in vitro.

Mutation research. Genetic toxicology and environmental mutagenesis
Phenolic groups of steroidal or nonsteroidal estrogens can redox cycle, leading to oxidative stress, where creation of reactive oxygen species are recognized as the main mechanism of their DNA damage properties. Dry olive (Olea europaea L.) leaf extr...

Predicting tumor cell line response to drug pairs with deep learning.

BMC bioinformatics
BACKGROUND: The National Cancer Institute drug pair screening effort against 60 well-characterized human tumor cell lines (NCI-60) presents an unprecedented resource for modeling combinational drug activity.

Practical Model Selection for Prospective Virtual Screening.

Journal of chemical information and modeling
Virtual (computational) high-throughput screening provides a strategy for prioritizing compounds for experimental screens, but the choice of virtual screening algorithm depends on the data set and evaluation strategy. We consider a wide range of liga...

Exploring Alternative Strategies for the Identification of Potent Compounds Using Support Vector Machine and Regression Modeling.

Journal of chemical information and modeling
Support vector regression (SVR) is a premier approach for the prediction of compound potency. Given the conceptual link between support vector machine (SVM) and SVR modeling, SVR is capable of accounting for continuous and discontinuous structure-act...

High-throughput brain activity mapping and machine learning as a foundation for systems neuropharmacology.

Nature communications
Technologies for mapping the spatial and temporal patterns of neural activity have advanced our understanding of brain function in both health and disease. An important application of these technologies is the discovery of next-generation neurotherap...

Application of Bioactivity Profile-Based Fingerprints for Building Machine Learning Models.

Journal of chemical information and modeling
The volume of high throughput screening data has considerably increased since the beginning of the automated biochemical and cell-based assays era. This information-rich data source provides tremendous repurposing opportunities for data mining. It wa...

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...

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...

Using predicate and provenance information from a knowledge graph for drug efficacy screening.

Journal of biomedical semantics
BACKGROUND: Biomedical knowledge graphs have become important tools to computationally analyse the comprehensive body of biomedical knowledge. They represent knowledge as subject-predicate-object triples, in which the predicate indicates the relation...