The Chemistry Development Kit (CDK) v2.0: atom typing, depiction, molecular formulas, and substructure searching

Abstract

Background The Chemistry Development Kit (CDK) is a widely used open source cheminformatics toolkit, providing data structures to represent chemical concepts along with methods to manipulate such structures and perform computations on them. The library implements a wide variety of cheminformatics algorithms ranging from chemical structure canonicalization to molecular descriptor calculations and pharmacophore perception. It is used in drug discovery, metabolomics, and toxicology. Over the last 10 years, the code base has grown significantly, however, resulting in many complex interdependencies among components and poor performance of many algorithms.Results We report improvements to the CDK v2.0 since the v1.2 release series, specifically addressing the increased functional complexity and poor performance. We first summarize the addition of new functionality, such atom typing and molecular formula handling, and improvement to existing functionality that has led to significantly better performance for substructure searching, molecular fingerprints, and rendering of molecules. Second, we outline how the CDK has evolved with respect to quality control and the approaches we have adopted to ensure stability, including a code review mechanism.ConclusionsThis paper highlights our continued efforts to provide a community driven, open source cheminformatics library, and shows that such collaborative projects can thrive over extended periods of time, resulting in a high-quality and performant library. By taking advantage of community support and contributions, we show that an open source cheminformatics project can act as a peer reviewed publishing platform for scientific computing software.Graphical abstractCDK 2.0 provides new features and improved performance Electronic supplementary materialThe online version of this article (doi:10.1186/s13321-017-0220-4) contains supplementary material, which is available to authorized users.

Predicting Molecular Fingerprint from Electron−Ionization Mass Spectrum with Deep Neural Networks

“…We trained full connected neural network model for each bit of fingerprint, which represented the substructure information of the unknown compound. In this work, six kinds of substructure-related molecular fingerprints were used and listed in Table 1 [15,[19][20][21][22][23]. There are 8034 fingerprint bits when combining all the six kinds of molecular fingerprints.…”

“…CDK standard fingerprint 1024 [19] PubChem fingerprint 881 [20] Klekota-Roth fingerprint 4860 [21] MACCS keys 166 [15] Estate fingerprint 79 [22] Circular fingerprint 1024 [23]…”

NMReDATA, a standard to report the NMR assignment and parameters of organic compounds

“…In such a way, a program could also take into account the nature of the solvent to consider whether the protons from OH or NH are expected to exchange with the ones of the solvent. We encourage that exchanging protons be named “Ex.” Note that in the example of menthol, the hydroxyl proton is not exchanging (as demonstrates the coupling with H10ax) otherwise it would have been called “Ex.” This name will indicate to an automatic verification program that a group of protons assigned to a single signal is not due to symmetry, a property which can be assessed using cheminformatic tools such as CDK …”

“…This name will indicate to an automatic verification program that a group of protons assigned to a single signal is not due to symmetry, a property which can be assessed using cheminformatic tools such as CDK. [32] A verification of the reported multiplicity of signals can be performed regarding the spectrum and the chemical structure. Simply considering that multiplicity should include one "d" for each spin 1/2 located 1-3 bonds from the reference nucleus and keeping in mind that "t" counts for two couplings, "q" counts for three, etc.…”

Analysis of the effects of related fingerprints on molecular similarity using an eigenvalue entropy approach

“…We used the implementation of CDK (version 2.3) [16] to compute 166-bit MACCS and 881-bit Pubchem fingerprint vectors. To measure the similarity of a pair of compounds, a and b, we computed Tanimoto coefficient of their l-bit fingerprint vectors, v a and v b as follows:…”