Molecular docking is widely used in computed drug discovery and biological target identification, but getting fast results can be tedious and often requires supercomputing solutions. AMIDE stands for AutoMated Inverse Docking Engine. It was initially developed in 2014 to perform inverse docking on High Performance Computing. AMIDE version 2 brings substantial speed-up improvement by using AutoDock-GPU and by pulling a total revision of programming workflow, leading to better performances, easier use, bug corrections, parallelization improvements and PC/HPC compatibility. In addition to inverse docking, AMIDE is now an optimized tool capable of high throughput inverse screening. For instance, AMIDE version 2 allows acceleration of the docking up to 12.4 times for 100 runs of AutoDock compared to version 1, without significant changes in docking poses. The reverse docking of a ligand on 87 proteins takes only 23 min on 1 GPU (Graphics Processing Unit), while version 1 required 300 cores to reach the same execution time. Moreover, we have shown an exponential acceleration of the computation time as a function of the number of GPUs used, allowing a significant reduction of the duration of the inverse docking process on large datasets.

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The wide adoption of Web 2.0 services has resulted in an increase in the amount of information produced. The quantity of errors contained in such information has grown even faster. Indeed, in traditional information production process documents were produced by professionals while in the Web context the content is generated by the users themselves. It is therefore necessary to take into account the errors particularly when such systems need to manage information of variable quality. Our state of the art leads us to identify difficulties in the comparative evaluation of error correction systems. Our proposal consists in an evaluation model for error correction systems and low-level string similarity (and distance) metrics they rely on. This model is implemented in an extensible platform providing a framework to evaluate those systems.Benchmark, error correction, textual documents, distance and similarity measure, metrics, information retrieval