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8.2B
Data on properties
160M
Molecules
150M
Scientific publications
26M
Patents
7M
Reactions
Database
The Syntelly database is built from a wide range of sources and enhanced with a proprietary pipeline of neural-network modules that extract information from scientific documents.
Syntelly enables professionals across various industries to effectively manage and work with chemical data:
Syntelly – AI Platform for Chemistry
Cosmetics companies
quickly choose and evaluate cosmetic ingredients.
Regulatory affairs
working with existing substance registries, monitoring and verifying chemical safety (toxicity)
Chemical industry
identifying promising alternatives and predicting molecular cost and safety profiles before conducting laboratory testing
Patent analysts
verifying whether chemical compounds are patentable and identifying potential issues that could impact patent protection
Education & Science
instant access to critical scientific information with powerful search capabilities
Pharmaceutical industry
reducing the risk of failure in early-stage drug development
Modules
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Syntelly Benefits
3,500+ Users
Many of the best companies, research organizations, and regulators use Syntelly. More than 3,500 users already depend on the platform for working with chemical data.
User-Driven Development
We carefully analyze user feedback and continuously improve the platform based on the real needs of the scientific community.
Transparency and Reliability of Data
Model statistics and applicability indicators are available directly on the platform for every user. This allows for making informed decisions using predicted data.
Multifunctional Ecosystem
You don't have to switch between different services — all the essential tools for working with chemical data are integrated into one platform.
Advanced Machine Learning Technologies
More than 80 validated AI models for collecting, analyzing, and processing chemical information. The models ensure high accuracy of results.
What Customers Say About Syntelly
All work tasks required during the testing period were successfully completed. I want to highlight the variety of functionality, the integration of different features within a single platform, and the intuitive interface.
Yulian Farkhodov
Dokuchaev Soil Science Institute, Russian Academy of Sciences
The key value of Syntelly for us is the significant acceleration of the development process. Its capabilities allow us to efficiently filter out unsuitable molecules, substantially reducing research costs.
Konstantin S. Nazarov
Deputy Director of Production, INFAMED K LLC
It is possible to quickly search both for known compounds with experimental properties and for new ones, where machine learning models predict various physicochemical parameters. I was very impressed with the PDF2SMILES module. The reaction prediction and retrosynthetic analysis functions are also impressive—they allow me to plan syntheses or anticipate reaction outcomes.
E. V. Skorb
Doctor of Chemistry, Director of the Scientific and Educational Center for Infochemistry, Professor, ITMO University
All physicochemical properties are available; everything is collected in one place and sourced from multiple databases. Another plus is the inclusion of biological activity.
Evgeniya Doronina
PhD in Chemistry, Senior Researcher, Favorsky Institute of Chemistry, Irkutsk
Clear advantages I see as a user: the drawing tool is intuitive, similar to other familiar editors, and you can search by CAS number. Hyperlinks to open databases are excellent.
N. V. Sidorenko
Associate Professor, Department of Chemistry and Elastomer Processing Technology, VSTU
Latest News
22.06.2020
Scientists evaluated the ability of artificial intelligence that suggest products to buy and recommend new antiviral com ...
24.08.2021
Scientists developed the first open-source tool to translate chemical structures to their IUPAC names using machine lear ...
02.02.2022
Researchers have proposed a new method for visualizing chemical reactions to help scientists understand the global chemi ...
22.02.2022
Researchers from a startup train neural network to recognize chemical formulas from research papers
01.08.2024
The new version includes full-text literature search, Markush structures, improved modules for generating new compounds ...
23.11.2024
The new version includes improvements to the Datasets module, SynMap and a number of changes in the product interface
Frequently Asked Questions
Useful Resources
Software Features
This document explains what the Syntelly software system can do. Syntelly is a chemoinformatics platform that uses artificial intelligence to solve problems in organic chemistry.
User Guide
A simple step-by-step guide to working with Syntelly It includes instructions for using the key, setting it up, and using the platform efficiently.
Syntelly’s modules employ a variety of machine learning methods, including classical approaches such as gradient boosting models, as well as different types of neural network, including fully connected, graph-based and transformer models, along with specialised computer vision and natural language processing (NLP) models. Key technologies are described and published in reputable scientific journals. More detailed information can be found on each module’s page.
Free trial access is provided for 15 days once a year.
When citing information obtained using the Syntelly platform, the following format is recommended:
Results were obtained using the Syntelly platform [1].
[1] Syntelly Platform (http://syntelly.com). Syntelly LLC, Year. Accessed on 00/00/20__.
Results were obtained using the Syntelly platform [1].
[1] Syntelly Platform (http://syntelly.com). Syntelly LLC, Year. Accessed on 00/00/20__.
Pricing is determined based on the client’s requirements and depends on the features and number of users within the organisation. To request a licence quote, please submit a request or email sales@syntelly.com.
Syntelly’s database is populated from multiple sources:
- Structures: PubChem and a proprietary neural-network pipeline for extracting data from documents
- Experimental properties: dozens of sources, including major data aggregators such as PubChem and ChEMBL
- Chemical reactions: a proprietary neural-network pipeline for extracting data from documents and external sources and datasets (e.g. the Open Reactions Database)
- Scientific publications: Crossref, PubMed and PubChem
- Patents: covers all major global patent offices Patent data is collected from Google Patents and PubChem, with monthly updates from Rospatent (FIPS).
- Structures: PubChem and a proprietary neural-network pipeline for extracting data from documents
- Experimental properties: dozens of sources, including major data aggregators such as PubChem and ChEMBL
- Chemical reactions: a proprietary neural-network pipeline for extracting data from documents and external sources and datasets (e.g. the Open Reactions Database)
- Scientific publications: Crossref, PubMed and PubChem
- Patents: covers all major global patent offices Patent data is collected from Google Patents and PubChem, with monthly updates from Rospatent (FIPS).
The database is updated monthly.
The platform is most effective in the following sectors:
- Education and research
- The chemical industry
- Pharmaceutical companies
- Patent organisations
- The cosmetics industry
- Education and research
- The chemical industry
- Pharmaceutical companies
- Patent organisations
- The cosmetics industry
Yes. The system supports the upload of user molecules in CSV, SMILES and SDF formats. You can also add custom property data and notes for your compounds.
Yes, new sources, such as scientific articles and patents, are added regularly. User requests may be prioritised.
No, the platform currently does not support polymers as there is no database for them and most of our models are not applicable. However, you can still find scientific publications and patents related to polymer chemistry on the platform.
Syntelly works with all major chemical data formats, including linear representations such as SMILES, InChI, IUPAC names, trivial names, CAS numbers and other database identifiers. Molecules can be entered via the built-in drawing tool. Data can also be uploaded from SDF, CSV or XLSX files, and data from multiple sources can be combined within a single project.
The platform adheres to strict security and privacy standards. All user data is isolated. Analytics are anonymised and we do not track your activity on the platform. [Privacy Policy link]
Clients and Partners
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