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Biomimetics is the interdisciplinary co-operation of various scientific disciplines and fields of innovation, and it aims to solve practical problems using biological models. Biomimetic research and its fields of application are manifold, and the community is made up of a wide range of disciplines, from biologists and engineers to designers. Guidelines and standards can build a common ground for understanding of the field, communication across disciplines, present and future projects and implementation of biomimetic knowledge. Since 2015, three international standards have been published and defined terms and definitions, as well as specific applications. The scientific literature and patents in several databases were searched for citations of the published standards. Standards or technical guidelines on biomimetics are represented both in the scientific literature and in patents. However, taking into account the increasing number of publications in biomimetics, the number of publications (52) citing the international standards is low. This shows that the perception of technical rules is still underrepresented in the academic field. Greater awareness and acceptance of the importance of standards for quality assurance even in the academic environment is discussed, and active participation in the corresponding International Organization for Standardization committee on biomimetics is asked for.
The use of molecular string representations for deep learning in chemistry has been steadily increasing in recent years. The complexity of existing string representations, and the difficulty in creating meaningful tokens from them, lead to the development of new string representations for chemical structures. In this study, the translation of chemical structure depictions in the form of bitmap images to corresponding molecular string representations was examined. An analysis of the recently developed DeepSMILES and SELFIES representations in comparison with the most commonly used SMILES representation is presented where the ability to translate image features into string representations with transformer models was specifically tested. The SMILES representation exhibits the best overall performance whereas SELFIES guarantee valid chemical structures. DeepSMILES perform in between SMILES and SELFIES, InChIs are not appropriate for the learning task. All investigations were performed using publicly available datasets and the code used to train and evaluate the models has been made available to the public.
Personalpolitik im Wandel
(2004)