Fragment Descriptors in SAR/QSAR/QSPR Studies, Molecular Similarity Analysis and in Virtual Screening

Baskin, Igor I.; Varnek, Alexandre

doi:10.1039/9781847558879-00001

Cited by 38 publications

(22 citation statements)

References 256 publications

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“…Amongst numerous families of descriptors, the fragment descriptors that count occurrences of a subgraph in a molecule graph, hold a distinctive status due to simplicity in the calculation. Moreover, there is a theoretical proof that one can successfully build any QSAR model with them [5]. Even a small database of compounds contains thousands of fragmental descriptors and some feature selection algorithm has traditionally been used to find a proper subset of descriptors for better quality, and to speed up the whole modeling process.…”

Section: Introductionmentioning

confidence: 99%

Transformer-CNN: Swiss knife for QSAR modeling and interpretation

2020

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We present SMILES-embeddings derived from the internal encoder state of a Transformer [1] model trained to canonize SMILES as a Seq2Seq problem. Using a CharNN [2] architecture upon the embeddings results in higher quality interpretable QSAR/QSPR models on diverse benchmark datasets including regression and classification tasks. The proposed Transformer-CNN method uses SMILES augmentation for training and inference, and thus the prognosis is based on an internal consensus. That both the augmentation and transfer learning are based on embeddings allows the method to provide good results for small datasets. We discuss the reasons for such effectiveness and draft future directions for the development of the method. The source code and the embeddings needed to train a QSAR model are available on https ://githu b.com/bigch em/trans forme r-cnn. The repository also has a standalone program for QSAR prognosis which calculates individual atoms contributions, thus interpreting the model's result. OCHEM [3] environment (https ://ochem .eu) hosts the on-line implementation of the method proposed.

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Section: Introductionmentioning

confidence: 99%

Transformer-CNN: Swiss knife for QSAR modeling and interpretation

2020

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“…In this section, we demonstrate that CIFs can be considered as 3D analogues of 2D topological fragment descriptors [1][2][3][4][25][26][27][28]. Let's consider the fragment descriptor based on the simplest substructure-an atom.…”

Section: Cifs and Fragment Descriptorsmentioning

confidence: 99%

“…Fragment (substructure) descriptors [1][2][3][4] and molecular fields [5] are two common ways of representing molecules in numerous applications in chemoinformatics and drug discovery [6,7]. Although both of them play an important role in building structure-property/activity models, visualizing chemical space, performing similarity search and virtual screening, they represent molecules in a totally different manner, and therefore they are used differently.…”

Section: Introductionmentioning

confidence: 99%

Continuous indicator fields: a novel universal type of molecular fields

Sitnikov

Zhokhova

Ustynyuk

et al. 2014

J Comput Aided Mol Des

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A novel type of molecular fields, Continuous Indicator Fields (CIFs), is suggested to provide 3D structural description of molecules. The values of CIFs are calculated as the degree to which a point with given 3D coordinates belongs to an atom of a certain type. They can be used similarly to standard physicochemical fields for building 3D structure-activity models. One can build CIF-based 3D structure-activity models in the framework of the continuous molecular fields approach described earlier (J Comput-Aided Mol Des 27 (5):427-442, 2013) for the case of physicochemical molecular fields. CIFs are thought to complement and further extend traditional physicochemical fields. The models built with CIFs can be interpreted in terms of preferable and undesirable positions of certain types of atoms in space. This helps to understand which changes in chemical structure should be made in order to design a compound possessing desirable properties. We have demonstrated that CIFs can be considered as 3D analogues of 2D topological molecular fragments. The performance of this approach is demonstrated in structure-activity studies of thrombin inhibitors, multidentate N-heterocyclic ligands for Am(3+)/Eu(3+) separation, and coloring dyes.

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“…In this sense, all the mtk-QSBER models are focused on using topological indices, which can be expressed as linear combinations of the occurrence of fragments in the molecules [58,96,97]. Accordingly, the mtk-QSBER models can be employed to generate large libraries of fragments, with the posterior calculation of the contributions of these fragments to diverse biological effects.…”

Section: Expert Opinionmentioning

confidence: 99%