Several methods were introduced by the different toolkits, such as adding, modifying or removing header elements one element at a time or using a list of actions, defined by the tools or manually, to be conducted on several elements simultaneously.
While challenges remain – such as the development of a standard aromatic model for SMILES – the ability to create the same SMILES using different toolkits will mean that for the first time it will be possible to easily compare the chemical models used by different toolkits.
ChemDes makes it easy to compare the results obtained by different toolkits for the same descriptors.
Cinfony makes it easy to compare the results obtained by different toolkits for the same operations.
Fig. 2 The relationship between the descriptors calculated by different toolkits.
This common API unites different molecular toolkits, such as RDKit and OpenBabel, and makes interacting with them more Python-like.
Each sample is quantitatively characterized by experimental toolkits such as cantilever bioassay, PeakForce QNM, as well as lateral force microscopy (LFM).
Fig. 2 Boxplot graphic for the external predictive accuracy achieved by the QSAR models reported in this manuscript (see Table 6) and fitted using structures generated by CORINA software, over the corresponding test sets optimized by five different toolkits.
However, there are at least three barriers to overcome: the need for a programming language that can access all the toolkits simultaneously, the difficulty of exchanging chemical models between different toolkits, and differences in the API for core cheminformatics tasks shared by the toolkits.
Open Source cheminformatics toolkits such as OpenBabel, the CDK and the RDKit share the same core functionality but support different sets of file formats and forcefields, and calculate different fingerprints and descriptors.
