Small molecules are one of the most important classes of therapeutics and also serve as invaluable tools to study biology, often with the goal to validate novel targets for the development of future therapeutic drugs. Reproducibility of experimental results and the interoperability and reusability of resulting datasets depend on accurate descriptions of associated research objects, and most critically on correct representations of small molecules that are tested in biological assays.  For example, it is not possible to develop predictive models of protein target - small molecule interactions if their chemical structure representations are not correct.  Many factors contribute to errors in reported chemical structures in small molecule screening and omics reference databases, scientific publications, and many other web-based resources and documents.  Because of the complexity of representing small molecule chemical structure graphs and the lack of thorough curation, errors are frequently introduced by non-experts and error propagation across different digital research assets is a pervasive problem.  Instead of complex chemical structure graphs, many researchers prefer to identify small molecule research objects by human recognizable identifiers or names.  By integrating several large-scale public datasets, we have systematically analyzed the extent of chemical structure ambiguity that arises when a single, non-systematic chemical identifier is linked to multiple systematic identifiers.  We have developed a framework to identify chemical structure representation errors and classified common mistakes.<br/>To address this challenging problem via a scalable approach, we are developing the Automated Molecular Identity Disambiguator (AutoMID).  AutoMID will be usable in batch mode at scale via an API, for example to assist chemical structure standardization and registration by maintainers of digital research assets, and also via interactive (UI) mode for everyday researchers to quickly and easily validate or correct their small molecule representations. AutoMID will leverage extensive highly standardized linked databases of chemical structures and associated information including names, synonyms and their sources / provenance and leverage expert rules and AI to enable reliable disambiguation of chemical structure identities at scale.

Common errors and ignorance in small molecule chemical structure representation: Towards developing an Automated Molecular Identity Disambiguator (AutoMID)

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ACS Fall 2022

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.