Molecular structures are commonly depicted in 2D printed forms in scientific documents such as journal papers and patents. However, these 2D depictions are not machine readable. Due to a backlog of decades and an increasing amount of printed literatures, there is a high demand for translating printed depictions into machinereadable formats, which is known as Optical Chemical Structure Recognition (OCSR). Most OCSR systems developed over the last three decades use a rule-based approach, which vectorizes the depiction based on the interpretation of vectors and nodes as bonds and atoms. Here, we present a practical software called MolMiner, which is primarily built using deep neural networks originally developed for semantic segmentation and object detection to recognize atom and bond elements from documents. These recognized elements can be easily connected as a molecular graph with a distance-based construction algorithm. MolMiner gave state-of-the-art performance on four benchmark data sets and a self-collected external data set from scientific papers. As MolMiner performed similarly well in real-world OCSR tasks with a user-friendly interface, it is a useful and valuable tool for daily applications. The free download links of Mac and Windows versions are available at https://github.com/iipharma/pharmamind-molminer.
Molecular structures are always depicted as 2D printed form in scientific documents like journal papers and patents. However, these 2D depictions are not machinereadable. Due to a backlog of decades and an increasing amount of these printed literature, there is a high demand for the translation of printed depictions into machinereadable formats, which is known as Optical Chemical Structure Recognition (OCSR).Most OCSR systems developed over the last three decades follow a rule-based approach where the key step of vectorization of the depiction is based on the interpretation of vectors and nodes as bonds and atoms. Here, we present a practical software MolMiner,
We introduce and study time-inhomogeneous quantum Markov chains with parameter ζ ≥ 0 and decoherence parameter 0 ≤ p ≤ 1 on finite spaces and their large scale equilibrium properties. Here ζ resembles the inverse temperature in the annealing random process and p is the decoherence strength of the quantum system. Numerical evaluations show that if ζ is small, then quantum Markov chain is ergodic for all 0 < p ≤ 1 and if ζ is large, then it has multiple limiting distributions for all 0 < p ≤ 1. In this paper, we prove the ergodic property in the high temperature region 0 ≤ ζ ≤ 1. We expect that the phase transition occurs at the critical point ζ c = 1. For coherence case p = 0, a critical behavior of periodicity also appears at critical point ζ o = 2.
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