DRAGON SOFTWARE: AN EASY APPROACH TO MOLECULAR DESCRIPTOR CALCULATIONS

By A. Mauri, Viviana Consonni, M. Pavan, Roberto Todeschini, Milano Chemometrics. 2006

532 citations recorded by the source corpus.

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Citations in the corpus

Mordred: a molecular descriptor calculator (2018, methodology): “Although both open-source and proprietary software have been developed for calculating molecular descrip-tors—such as PaDEL-Descriptor [6], BlueDesc [7], Che-moPy [8], PyDPI [9], Rcpi [10], cinfony [11], and Dragon [12]—they each have advantages and disadvantages (Table 1).”
Message passing neural networks for molecular property prediction (2019, background): “Many current approaches to molecular property prediction rely on standard machine learning models such as random forests [6] and support vector machines [10] which are applied to hand-crafted molecular descriptors, such as the Dragon descriptors [31], or to molecular fingerprints, like Morgan (ECFP)”
Meta-QSAR: a large-scale application of meta-learning to drug design and discovery (2017, methodology): “For this we used Dragon version 6 (Mauri et al., 2006), which is a commercially available software library that can potentially calculate up to 4,885 molecular descriptors, depending on the availability of 3D structural information of the molecules.”
A deep learning approach to antibiotic discovery (2020, background): “important development relates to how molecules are represented; traditionally, molecules were represented 79 by their fingerprint vectors, which reflected the presence or absence of functional groups in the molecule, or 80 by descriptors that include computable molecular properties and require exper”
A Deep Learning Approach to Antibiotic Discovery. (2020, unknown): “An important development relates to how molecules are represented; traditionally, molecules were represented by their fingerprint vectors, which reflected the presence or absence of functional groups in the molecule, or by descriptors that include computable molecular properties and require expert k”
Software Tools and Approaches for Compound Identification of LC-MS/MS Data in Metabolomics (2018, methodology): “Retention times can be predicted by using chemical descriptors as input parameters which can be computed directly from structures by tools such as Dragon [115], MOLD2 [116] or PaDel [117].”
Calculation of molecular lipophilicity: state of the art and comparison of methods on more than 96000 compounds (2009, methodology): “In our study we used MLOGP implemented in ADMET Predictor and in Dragon by Todeschini and colleagues.(74)”