New substructure filters for removal of pan assay interference compounds (PAINS) from screening libraries and for their exclusion in bioassays.
By J. Baell, Georgina A. Holloway. 2010
3,594 citations recorded by the source corpus.
Subfields: Computational Theory and Mathematics, Molecular Biology, Spectroscopy
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computational Theory and Mathematics
- Antoine Daina
- Vincent Zoete
- Olivier Michielin
- Antonio Lavecchia
- C. D. Giovanni
- Sun Choi
- Vijayakumar Gosu
- Sunhye Hong
In Molecular Biology
- Antoine Daina
- Vincent Zoete
- Olivier Michielin
- Antonio Lavecchia
- C. D. Giovanni
- Sun Choi
- Vijayakumar Gosu
- Sunhye Hong
In Spectroscopy
- Olivier Michielin
- Vincent Zoete
- Antoine Daina
- Alán Aspuru-Guzik
- Izhar Wallach
- Laurianne David
- Richard S. Judson
- Michael Dzamba
Citations in the corpus
- AtomNet: A Deep Convolutional Neural Network for Bioactivity Prediction in Structure-based Drug Discovery (2015, positive): “5 ˚A. • Ligands passed PAINS filers [24] and promiscuity rules [25].”
- ADMETlab 2.0: an integrated online platform for accurate and comprehensive predictions of ADMET properties (2021, methodology): “⚫ Empirical decision: 0 violations: excellent (green); otherwise: poor (red) PAINS [10] ⚫ Pan Assay Interference Compounds (PAINS) is one of the most famous frequent hitters filters, which comprises 480 substructures derived from the analysis of FHs determined by six target-based HTS assay.”
- Discovery of a structural class of antibiotics with explainable deep learning (2023, background): “3e and Supplementary Table 2).”
- Equivariant 3D-conditional diffusion model for molecular linker design (2022, neutral): “1, and the pseudocode of DiffLinker’s training and sampling procedures is provided in Supplementary Information. https://doi.org/10.1038/s42256-024-00815-9”
- Molecular representations in AI-driven drug discovery: a review and practical guide (2020, methodology): “SMARTS patterns have been used to define substructures with the aim of selecting or eliminating associated compounds [135–137].”
- Role of computer-aided drug design in modern drug discovery (2015, background): “Additionally, pan-assay interference compounds (PAINS) filter (Baell and Holloway 2010; Erlanson 2015) can take out compounds that are frequent hitters in various HTS experiments, and thus cannot be used as specific inhibitors.”
- Synthon-based ligand discovery in virtual libraries of over 11 billion compounds (2021, positive): “The remaining compounds were filtered with PAINS score, drug-likeness properties, chemical diversity as well as ligand interaction diversity to sample different binding modes in the pocket.”