Test access to the modular platform Sintelly

From April 1 to May 31, 2025, RUDN University has opened test access to the modular platform Syntelli  The platform allows searching by structures, similarity and reactions, and also supports links to external data sources.

The platform functionality provides access to the following modules:

• The "Molecular Editor" module is designed to predict the properties of compounds that are not in the Syntelli database.
• The "Datasets" module allows you to create, load, edit and analyze personal, corporate (company datasets) and thematic datasets.
• The "SynMap" module provides the ability to visualize molecules on a 2D plane, group molecules with similar properties into clusters and generate compounds with specified properties.
• The "Reaction Prediction" module is designed to plan the synthesis of compounds and predict the paths of chemical transformations.
• The Spectra module contains three modules for predicting various types of spectral data, each of which provides information on the structure and properties of the analyzed molecules: Nuclear Magnetic Resonance (NMR), Mass Spectrometry (QToF MS/MS), and Infrared Spectroscopy.
• The Synthesis Cost module allows you to predict the cost of synthetic compounds, taking into account many factors, including the scale of production and the complexity of synthesis.
• The PDF2SMILES module is a tool for optical recognition of molecular structures and Markush structures from PDF documents and export of structures from documents to a separate dataset for further analysis.
• The SMILES2IUPAC module is an automated tool for converting chemical structures from the SMILES format to a systematic name according to the IUPAC nomenclature.
• The Statistics module – this section provides information on the accuracy and reliability of the models used in the system for two types of metrics: RMSE (Root Mean Square Error) for measuring the root mean square error of predictions and ROC AUC (Receiver Operating Characteristic Area Under for measuring the quality of binary classification.

The resource will be useful for students and postgraduates of chemical faculties of universities and can be used to solve various problems in the field of organic and medicinal chemistry