OMPL is a lightweight, thread-safe, easy to use, and extensible library for sampling-based motion planning. The code is written in C++, includes Python bindings and is released under the BSD license. OMPL is also integrated with ROS. On top of the OMPL library, we have developed OMPL.app: a GUI for rigid body motion planning that allows users to load a variety of mesh formats that define a robot and its environment, define start and goal states, and play around with different planners.
Planner Arena is a site for benchmarking sampling-based planners. The site is set up to show the performance of implementations of various sampling-based planning algorithms in the Open Motion Planning Library (OMPL).
The Task-Motion Kit (TMKit) is a framework for Task and Motion Planning. Everyday activities, e.g., setting a table or making coffee, combine discrete decisions about objects and actions with geometric decisions about collision free motion. TMKit jointly reasons about task-level objectives, i.e., choosing actions and objects, and motion-level objectives, i.e., finding collision free paths.
OOPSMP is a package for motion planning that is easy to extend, robust, and efficient. It can be used for motion planning research or as a teaching tool. This package is still available for download, but is no longer further developed (except for minor bug fixes).
Metabolite Translator predicts human metabolites for small molecules including drugs. It is build upon a Neural Machine Translation algorithm representing molecules as sequences using the SMILES notation. Metabolite Translator converts the SMILES of the initial molecule into the SMILES representations of the metabolites that can be possibly formed in the human body. The method has been trained on data that cover metabolism of xenobiotics as well as endogenous compounds and therefore it can predict metabolites through a wide range of enzymes including the enzymes of phase I and phase II drug metabolism.
APE-Gen is a fast method for generating ensembles of bound pMHC conformations. It generates an ensemble of bound conformations by iterated rounds of (i) anchoring the ends of a given peptide near known pockets in the binding site of the MHC, (ii) sampling peptide backbone conformations with loop modelling, and then (iii) performing energy minimization to fix steric clashes, accumulating conformations at each round.