Provides assistance for drawing and modeling chemical structures. OSIRIS Property Explorer computes directly different drug-relevant properties whenever a structure is valid. This software allows users to underline properties with high risks of undesired effects such as mutagenicity, poor intestinal absorption or drug conform behavior via different customization features.
Assists users in quantifying compound quality. QED provides a quantitative metric for assessing drug-likeness. The provided values can range between zero (all properties unfavorable) and one (all properties favorable). The functions are based on the underlying distribution data of drug properties. It allows users to submit chemical compounds and subsequent prediction of drug sensitivity in terms of logIC50 (µM).
Combines kernel-based non-linear dimensionality reduction and binary classification (or regression). kbmtl main feature is the joint Bayesian formulation of projecting data points into a shared subspace and learning predictive models for all drugs in this subspace, which helps users to eliminate off-target effects and drug-specific experimental noise. This method allows users to handle missing phenotype values owing to experimental conditions and quality control reasons. It obtains statistically significantly better predictive performance on most of the drugs compared with baseline single-task algorithms that learn drug-specific models. These results show that predicting drug susceptibility against a panel of drugs simultaneously within a multitask learning framework improves overall predictive performance over single-task learning approaches.
Determines cell line-drug associations by incorporating cell line genomic profile, drug chemical structure, drug-target and protein protein interaction (PPI) information. HNMDRP permits users to search several novel potential sensitive associations according to high-ranking prediction results which are supported by related literatures. This method simplifies the research of potential sensitive associations among cell lines and drugs.
Identifies targeted biological pathways for drugs with unclear mechanism of action. iFad is an R package, based on a Bayesian sparse factor analysis model, that merges information from paired gene expression and drug sensitivity from the same set of samples. The application is designed for a natural incorporation of prior knowledge about the connectivity structure of biological pathways.
Allows prediction of drug response from patient tumor gene expression data. pRRophetic can be used to predict non-clinical phenotypes, user-defined training sets and continuous and categorical phenotypes. It employs the Cancer Genome Project (CGP) cell lines as a training set. This tool’s accuracy prediction will ultimately vary from drug to drug based on the predictive power of expression under that particular set of conditions and the appropriateness of cell lines as a model of in vivo drug response.