62,874 research outputs found
A Mapping of Drug Space from the Viewpoint of Small Molecule Metabolism
Small molecule drugs target many core metabolic enzymes in humans and pathogens,
often mimicking endogenous ligands. The effects may be therapeutic or toxic, but
are frequently unexpected. A large-scale mapping of the intersection between
drugs and metabolism is needed to better guide drug discovery. To map the
intersection between drugs and metabolism, we have grouped drugs and metabolites
by their associated targets and enzymes using ligand-based set signatures
created to quantify their degree of similarity in chemical space. The results
reveal the chemical space that has been explored for metabolic targets, where
successful drugs have been found, and what novel territory remains. To aid other
researchers in their drug discovery efforts, we have created an online resource
of interactive maps linking drugs to metabolism. These maps predict the
βeffect spaceβ comprising likely target enzymes for each of
the 246 MDDR drug classes in humans. The online resource also provides
species-specific interactive drug-metabolism maps for each of the 385 model
organisms and pathogens in the BioCyc database collection. Chemical similarity
links between drugs and metabolites predict potential toxicity, suggest routes
of metabolism, and reveal drug polypharmacology. The metabolic maps enable
interactive navigation of the vast biological data on potential metabolic drug
targets and the drug chemistry currently available to prosecute those targets.
Thus, this work provides a large-scale approach to ligand-based prediction of
drug action in small molecule metabolism
Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age
Simultaneous Localization and Mapping (SLAM)consists in the concurrent
construction of a model of the environment (the map), and the estimation of the
state of the robot moving within it. The SLAM community has made astonishing
progress over the last 30 years, enabling large-scale real-world applications,
and witnessing a steady transition of this technology to industry. We survey
the current state of SLAM. We start by presenting what is now the de-facto
standard formulation for SLAM. We then review related work, covering a broad
set of topics including robustness and scalability in long-term mapping, metric
and semantic representations for mapping, theoretical performance guarantees,
active SLAM and exploration, and other new frontiers. This paper simultaneously
serves as a position paper and tutorial to those who are users of SLAM. By
looking at the published research with a critical eye, we delineate open
challenges and new research issues, that still deserve careful scientific
investigation. The paper also contains the authors' take on two questions that
often animate discussions during robotics conferences: Do robots need SLAM? and
Is SLAM solved
Performance Evaluation of Vision-Based Algorithms for MAVs
An important focus of current research in the field of Micro Aerial Vehicles
(MAVs) is to increase the safety of their operation in general unstructured
environments. Especially indoors, where GPS cannot be used for localization,
reliable algorithms for localization and mapping of the environment are
necessary in order to keep an MAV airborne safely. In this paper, we compare
vision-based real-time capable methods for localization and mapping and point
out their strengths and weaknesses. Additionally, we describe algorithms for
state estimation, control and navigation, which use the localization and
mapping results of our vision-based algorithms as input.Comment: Presented at OAGM Workshop, 2015 (arXiv:1505.01065
Learning to Fly by Crashing
How do you learn to navigate an Unmanned Aerial Vehicle (UAV) and avoid
obstacles? One approach is to use a small dataset collected by human experts:
however, high capacity learning algorithms tend to overfit when trained with
little data. An alternative is to use simulation. But the gap between
simulation and real world remains large especially for perception problems. The
reason most research avoids using large-scale real data is the fear of crashes!
In this paper, we propose to bite the bullet and collect a dataset of crashes
itself! We build a drone whose sole purpose is to crash into objects: it
samples naive trajectories and crashes into random objects. We crash our drone
11,500 times to create one of the biggest UAV crash dataset. This dataset
captures the different ways in which a UAV can crash. We use all this negative
flying data in conjunction with positive data sampled from the same
trajectories to learn a simple yet powerful policy for UAV navigation. We show
that this simple self-supervised model is quite effective in navigating the UAV
even in extremely cluttered environments with dynamic obstacles including
humans. For supplementary video see: https://youtu.be/u151hJaGKU
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