Motivation: Fast and accurate prediction of protein-ligand binding structures is indispensable for structure-based drug design and accurate estimation of binding free energy of drug candidate molecules in drug discovery. Recently, accurate pose prediction methods based on short Molecular Dynamics (MD) simulations, such as MM-PBSA and MM-GBSA, among generated docking poses have been used. Since molecular structures obtained from MD simulation depend on the initial condition, taking the average over different initial conditions leads to better accuracy. Prediction accuracy of protein-ligand binding poses can be improved with multiple runs at different initial velocity. Results: This paper shows that a machine learning method, called Best Arm Identification, can optimally control the number of MD runs for each binding pose. It allows us to identify a correct binding pose with a minimum number of total runs. Our experiment using three proteins and eight inhibitors showed that the computational cost can be reduced substantially without sacrificing accuracy. This method can be applied for controlling all kinds of molecular simulations to obtain best results under restricted computational resources. Availability and implementation: Code and data are available on GitHub at https://github.com/tsudalab/bpbi. Contact: [email protected] or [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online.

We have developed the forward obstacle detection system by using stereo vision. This system consists of a combination of a stereo processing system based on a Field-Programmable Gate Array (FPGA) and Central Processing Unit (CPU) on PC. By using the FPGA-based system, the system can process images of high resolution and frame rate in real time. Therefore, large field of view and accuracy of distance go together. This system defines the drivable tunnel by detecting road surface and lane. Then, the forward obstacle in the tunnel can be detected. It was confirmed that this system works by the experiment in the expressways.