Drug side effect detection software tools | Drug discovery data analysis
Discovering the unintended “off-targets” that predict adverse drug reactions (ADRs) is daunting by empirical methods alone. Drugs can act on multiple protein targets, some of which can be unrelated by traditional molecular metrics, and hundreds of proteins have been implicated in side effects.
Allows protein ligand-binding site comparison and database searching. SMAP-WS is a computation environment designed for web accessible 3D ligand-binding site comparison and similarity searching on a structural proteome scale. The software is capable of an all-by-all comparison of binding sites for a complete structural proteome. It can assist users in addressing practical problems in biology and drug discovery.
Provides a multiple response partial least-squares (PLS) algorithm for graph mining. GPL is composed of two principal functions: (i) gSpan, which runs a frequent subgraph mining algorithm from graph data and (ii) gPLS which performs a (multiple) graph PLS regression. Results can be visualized thanks to an additional module, only available with MacOs, which converts subgraphs patterns.
Allows to identify possible side effects of potential drug treatments on the human body. BioSight works with an algorithm which permits to predict all of the potential biological targets that are at risk of interacting with a potential drug treatment. It helps researchers to better develop their molecules or reduce the failure rate during the clinical trial phase.
A web server that integrates chemical and biological information to elucidate the molecular mechanisms underlying drug side effects. IntSide currently catalogs 1175 side effects caused by 996 drugs, associated with drug features divided into eight categories, belonging to either biology or chemistry. On the biological side, IntSide reports drug targets and off-targets, pathways, molecular functions and biological processes. From a chemical viewpoint, it includes molecular fingerprints, scaffolds and chemical entities.
Assists in predicting side effects of drug pairs. Decagon is a general graph convolutional neural network designed to operate on a large multimodal graph where nodes can be connected through a large number of different relation types. It infers a prediction model that can identify side effects of pairs of drugs. This application predicts an association between a side effect and a co-prescribed drug pair to identify side effects that cannot be attributed to either drug alone.
Allows users to detect and annotate temporally anchored mentions of Adverse drug events (ADEs) from a clinical text corpus. ADEPt is a modular pipeline that first perform ADE mentions’ identification, and then, organize it, for finally refining the classification thanks to contextual indicators furnished by the source. The application also includes a way for targeting ADE-specific patterns in psychiatric clinical text and an expandable dictionary depicting over 60 common ADEs.