SMARTS search, Crippen logP and PAINS alerts

SmartsMatch: SMARTS substructure search on a SMILES molecule read with morie_avalon_parse (implicit hydrogens to the lowest allowed valence, ring bonds as non-bridges, smallest set of smallest rings, Hueckel 4n + 2 perception of rings written in Kekule form). ClogpEstimate: Wildman-Crippen logP and molar refractivity by first-match atom typing with explicit hydrogens (the RDKit Crippen table). PainsFilter: the 480 PAINS alerts (Baell and Holloway 2010) in the RDKit/WEHI SMARTS form with query hydrogens merged into at-least-k H counts. Identical to the Python arm morie.fn.chemsmarts (atom indices 1-based here, 0-based there).

Usage

SmartsMatch(smiles, smarts, merge_hs = FALSE)

ClogpEstimate(smiles)

PainsFilter(smiles)

Arguments

smiles: SMILES string.
smarts: SMARTS pattern.
merge_hs: Fold query hydrogens into H-count queries (RDKit mergeHs).

Value

List. SmartsMatch: matched, anchors, n_atoms. ClogpEstimate: logp, mr, types, contributions. PainsFilter: flagged, names, n_alerts.

References

Wildman, S. A. and Crippen, G. M. (1999). Prediction of physicochemical parameters by atomic contributions. Journal of Chemical Information and Computer Sciences 39, 868-873.

Baell, J. B. and Holloway, G. A. (2010). New substructure filters for removal of pan assay interference compounds (PAINS) from screening libraries and for their exclusion in bioassays. Journal of Medicinal Chemistry 53, 2719-2740.

Daylight Chemical Information Systems. SMARTS - A Language for Describing Molecular Patterns. Daylight Theory Manual.

Examples

SmartsMatch("CC(=O)Oc1ccccc1C(=O)O", "[CX3](=O)[OX2H1]")$anchors
#> [1] 11
ClogpEstimate("c1ccccc1O")$logp
#> [1] 1.3922
PainsFilter("Oc1ccc(CC)cc1O")$names
#> [1] "catechol_A(92)"