jCompoundMapper: An open source Java library and command-line tool for chemical fingerprints

Hinselmann, Georg; Rosenbaum, Lars; Jahn, Andreas; Fechner, Nikolas; Zell, Andreas

doi:10.1186/1758-2946-3-3

Cited by 83 publications

(61 citation statements)

References 32 publications

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“…The other eight sets were considered for a deeper modeling. These eight sets have been widely used in the literature, including the study where the superior predictive ability of the QuBiLS‐MIDAS 3D‐MDs was evidenced . These datasets are comprised of angiotensin‐converting enzymes (ACE) inhibitors, acetylcholinesterase inhibitors (ACHE), ligands for the benzodiazepine receptor (BZR), cyclooxygenase‐2 inhibitors (COX2), dihydrofolate reductase inhibitors (DHFR), inhibitors of glycogen phosphorylase b (GPB), thermolysin inhibitors (THER), and thrombin inhibitors (THR).…”

Section: Validation Of the Smoothed Spherical Truncation Methods Basedmentioning

confidence: 99%

Smoothed Spherical Truncation based on Fuzzy Membership Functions: Application to the Molecular Encoding

et al. 2019

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A novel spherical truncation method, based on fuzzy membership functions, is introduced to truncate interatomic (or interaminoacid) relations according to smoothing values computed from fuzzy membership degrees. In this method, the molecules are circumscribed into a sphere, so that the geometric centers of the molecules are the centers of the spheres. The fuzzy membership degree of each atom (or aminoacid) is computed from its distance with respect to the geometric center of the molecule, by using a fuzzy membership function. So, the smoothing value to be applied in the truncation of a relation (or interaction) is computed by averaging the fuzzy membership degrees of the atoms (or aminoacids) involved in the relation. This truncation method is rather different from the existing ones, at considering the geometric center for the whole molecule and not only for atom‐groups, as well as for using fuzzy membership functions to compute the smoothing values. A variability study on a set comprised of 20,469 compounds (15,050 drug‐like compounds, 2994 drugs approved, 880 natural products from African sources, and 1545 plant‐derived natural compounds exhibiting anti‐cancerous activity) demonstrated that the truncation method proposed allows to determine molecular encodings with better ability for discriminating among structurally different molecules than the encodings obtained without applying truncation or applying non‐fuzzy truncation functions. Moreover, a principal component analysis revealed that orthogonal chemical information of the molecules is encoded by using the method proposed. Lastly, a modeling study proved that the truncation method improves the modeling ability of existing geometric molecular descriptors, at allowing to develop more robust models than the ones built only using non‐truncated descriptors. In this sense, a comparison and statistical assessment were performed on eight chemical datasets. As a result, the models based on the truncated molecular encodings yielded statistically better results than 12 procedures considered from the literature. It can thus be stated that the proposed truncation method is a relevant strategy for obtaining better molecular encodings, which will be ultimately useful in enhancing the modeling ability of existing encodings both on small‐to‐medium size molecules and biomacromolecules. © 2019 Wiley Periodicals, Inc.

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Section: Validation Of the Smoothed Spherical Truncation Methods Basedmentioning

confidence: 99%

Smoothed Spherical Truncation based on Fuzzy Membership Functions: Application to the Molecular Encoding

et al. 2019

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“…Compound‐specific properties, such as physicochemical, molecular modeling, and structural properties in both the non‐curated and the curated datasets were obtained. To add substructure information, extended connectivity fingerprints (ECFP) were calculated using jCompoundMapper . In this study, ECFP_4, containing information about all atoms within a diameter of four chemical bonds, was obtained as 4,096 binary descriptors using the following parameters: Fingerprint algorithm=ECFPVariant, and Distance cut‐off=6.…”

Section: Methodsmentioning

confidence: 99%

Data Curation can Improve the Prediction Accuracy of Metabolic Intrinsic Clearance

Esaki

Watanabe

Kawashima

et al. 2018

Molecular Informatics

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A key consideration at the screening stages of drug discovery is in vitro metabolic stability, often measured in human liver microsomes. Computational prediction models can be built using a large quantity of experimental data available from public databases, but these databases typically contain data measured using various protocols in different laboratories, raising the issue of data quality. In this study, we retrieved the intrinsic clearance ( CL int ) measurements from an open database and performed extensive manual curation. Then, chemical descriptors were calculated using freely available software, and prediction models were built using machine learning algorithms. The models trained on the curated data showed better performance than those trained on the non‐curated data and achieved performance comparable to previously published models, showing the importance of manual curation in data preparation. The curated data were made available, to make our models fully reproducible.

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“…SMILES string patterns of ECFP_4 features were generated using jCompoundMapper [50]. An active set and an inactive set of compounds (same compounds at different assay concentrations were considered unique) was derived for every kinase with compounds inhibiting kinase activity by 50% or more being considered as active, whilst compounds showing an inhibition of less than 50% being considered as inactive.…”

Section: Methodsmentioning

confidence: 99%

Are phylogenetic trees suitable for chemogenomics analyses of bioactivity data sets: the importance of shared active compounds and choosing a suitable data embedding method, as exemplified on Kinases

et al. 2013

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Background‘Phylogenetic trees’ are commonly used for the analysis of chemogenomics datasets and to relate protein targets to each other, based on the (shared) bioactivities of their ligands. However, no real assessment as to the suitability of this representation has been performed yet in this area. We aimed to address this shortcoming in the current work, as exemplified by a kinase data set, given the importance of kinases in many diseases as well as the availability of large-scale datasets for analysis. In this work, we analyzed a dataset comprising 157 compounds, which have been tested at concentrations of 1 μM and 10 μM against a panel of 225 human protein kinases in full-matrix experiments, aiming to explain kinase promiscuity and selectivity against inhibitors. Compounds were described by chemical features, which were used to represent kinases (i.e. each kinase had an active set of features and an inactive set).ResultsUsing this representation, a bioactivity-based classification was made of the kinome, which partially resembles previous sequence-based classifications, where particularly kinases from the TK, CDK, CLK and AGC branches cluster together. However, we were also able to show that in approximately 57% of cases, on average 6 kinase inhibitors exhibit activity against kinases which are located at a large distance in the sequence-based classification (at a relative distance of 0.6 – 0.8 on a scale from 0 to 1), but are correctly located closer to each other in our bioactivity-based tree (distance 0 – 0.4). Despite this improvement on sequence-based classification, also the bioactivity-based classification needed further attention: for approximately 80% of all analyzed kinases, kinases classified as neighbors according to the bioactivity-based classification also show high SAR similarity (i.e. a high fraction of shared active compounds and therefore, interaction with similar inhibitors). However, in the remaining ~20% of cases a clear relationship between kinase bioactivity profile similarity and shared active compounds could not be established, which is in agreement with previously published atypical SAR (such as for LCK, FGFR1, AKT2, DAPK1, TGFR1, MK12 and AKT1).ConclusionsIn this work we were hence able to show that (1) targets (here kinases) with few shared activities are difficult to establish neighborhood relationships for, and (2) phylogenetic tree representations make implicit assumptions (i.e. that neighboring kinases exhibit similar interaction profiles with inhibitors) that are not always suitable for analyses of bioactivity space. While both points have been implicitly alluded to before, this is to the information of the authors the first study that explores both points on a comprehensive basis. Excluding kinases with few shared activities improved the situation greatly (the percentage of kinases for which no neighborhood relationship could be established dropped from 20% to only 4%). We can conclude that all of the above findings need to be taken into account when performing chemogenomics ...

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jCompoundMapper: An open source Java library and command-line tool for chemical fingerprints

Cited by 83 publications

References 32 publications

Smoothed Spherical Truncation based on Fuzzy Membership Functions: Application to the Molecular Encoding

Smoothed Spherical Truncation based on Fuzzy Membership Functions: Application to the Molecular Encoding

Data Curation can Improve the Prediction Accuracy of Metabolic Intrinsic Clearance

Are phylogenetic trees suitable for chemogenomics analyses of bioactivity data sets: the importance of shared active compounds and choosing a suitable data embedding method, as exemplified on Kinases

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