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__.

Pricing depends on your requirements, the features you need, and the number of users in your organization. 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).

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

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 prioritized.

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 anonymized and we do not track your activity on the platform. [Privacy Policy link]