Syntelly has updated its eponymous platform for searching, processing and analyzing chemical information to version 2.1. The new version includes full-text literature search, Markush structures, improved modules for generating new compounds and automatic extraction of structural formulas from documents. In addition, a number of changes have been made to the product interface to simplify its use.
The Syntelly platform can be used in the pharmaceutical, cosmetic and chemical industries, intellectual property protection, education and regulatory activities.
Version 2.1 contains a number of new features and additions to enhance the ability to search and predict chemical compounds:
1. Literature search: 160 million literature sources and 15 million patents have been added to the Syntelly database. Previously searchable solely by structure, now also related scientific publications and patents can be searched, with combined and full-text search, sorting and filtering capabilities. Text queries can be entered in Russian and English. This greatly simplifies the process of finding the necessary information among many sources.
2. Search by Markush structures: allows you to easily find and analyze complex chemical structures, which saves time and effort of users. This function is especially relevant in patent search, when the user needs to search a group of chemical compounds.
3. Updated SynMap module: added optimization capability and additional parameters for generating new connections, the quality of the generated array became an order of magnitude higher.
4. Updated module for automatic extraction of structural formulas from documents - PDF2SMILES 2.0: the accuracy of the neural network has been improved, now it is possible to recognize Markush structures on any documents, such as patents, scientific articles, test reports and dissertations. This simplifies and speeds up the process of collecting and analyzing data for research, developing new compounds, and writing literature reviews.
5. One of the new integrations was the model applicability indicator. For each prediction, it provides an assessment of its applicability to a specific molecule, which increases the reliability of profiling new compounds and enables informed strategic planning decisions for applications in organic synthesis, medicinal and pharmaceutical chemistry.
6. New thematic datasets: datasets on molecular targets and therapeutic indications have been added to the platform. The datasets compiled by the Syntelly team may be particularly useful for educational applications.
7. Improved usability of the platform: notifications of important events on the platform have been implemented. In addition, it is possible to customize the hint system. The interface is more intuitive and navigation is smoother.
The Syntelly platform can be used in the pharmaceutical, cosmetic and chemical industries, intellectual property protection, education and regulatory activities.
Version 2.1 contains a number of new features and additions to enhance the ability to search and predict chemical compounds:
1. Literature search: 160 million literature sources and 15 million patents have been added to the Syntelly database. Previously searchable solely by structure, now also related scientific publications and patents can be searched, with combined and full-text search, sorting and filtering capabilities. Text queries can be entered in Russian and English. This greatly simplifies the process of finding the necessary information among many sources.
2. Search by Markush structures: allows you to easily find and analyze complex chemical structures, which saves time and effort of users. This function is especially relevant in patent search, when the user needs to search a group of chemical compounds.
3. Updated SynMap module: added optimization capability and additional parameters for generating new connections, the quality of the generated array became an order of magnitude higher.
4. Updated module for automatic extraction of structural formulas from documents - PDF2SMILES 2.0: the accuracy of the neural network has been improved, now it is possible to recognize Markush structures on any documents, such as patents, scientific articles, test reports and dissertations. This simplifies and speeds up the process of collecting and analyzing data for research, developing new compounds, and writing literature reviews.
5. One of the new integrations was the model applicability indicator. For each prediction, it provides an assessment of its applicability to a specific molecule, which increases the reliability of profiling new compounds and enables informed strategic planning decisions for applications in organic synthesis, medicinal and pharmaceutical chemistry.
6. New thematic datasets: datasets on molecular targets and therapeutic indications have been added to the platform. The datasets compiled by the Syntelly team may be particularly useful for educational applications.
7. Improved usability of the platform: notifications of important events on the platform have been implemented. In addition, it is possible to customize the hint system. The interface is more intuitive and navigation is smoother.