| Autor: |
Henning Otto Brinkhaus, Kohulan Rajan, Achim Zielesny, Christoph Steinbeck |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
Journal of Cheminformatics, Vol 14, Iss 1, Pp 1-7 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
1758-2946 |
| DOI: |
10.1186/s13321-022-00609-4 |
| Popis: |
Abstract The development of deep learning-based optical chemical structure recognition (OCSR) systems has led to a need for datasets of chemical structure depictions. The diversity of the features in the training data is an important factor for the generation of deep learning systems that generalise well and are not overfit to a specific type of input. In the case of chemical structure depictions, these features are defined by the depiction parameters such as bond length, line thickness, label font style and many others. Here we present RanDepict, a toolkit for the creation of diverse sets of chemical structure depictions. The diversity of the image features is generated by making use of all available depiction parameters in the depiction functionalities of the CDK, RDKit, and Indigo. Furthermore, there is the option to enhance and augment the image with features such as curved arrows, chemical labels around the structure, or other kinds of distortions. Using depiction feature fingerprints, RanDepict ensures diversely picked image features. Here, the depiction and augmentation features are summarised in binary vectors and the MaxMin algorithm is used to pick diverse samples out of all valid options. By making all resources described herein publicly available, we hope to contribute to the development of deep learning-based OCSR systems. Graphical Abstract |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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