ArticlePacific Symposium on Biocomputing. Pacific Symposium on Biocomputing2014
Prediction of off-target drug effects through data fusion.
Article in Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed.
- Structure-based pose prediction: Non-cognate docking extended to macrocyclic ligands.Journal of computer-aided molecular design · 2024Article
- Rapid Identification of Potential Drug Candidates from Multi-Million Compounds' Repositories. Combination of 2D Similarity Search with 3D Ligand/Structure Based Methods and In Vitro Screening.Molecules (Basel, Switzerland) · 2021Review
- ForceGen 3D structure and conformer generation: from small lead-like molecules to macrocyclic drugs.Journal of computer-aided molecular design · 2017Article
- Extrapolative prediction using physically-based QSAR.Journal of computer-aided molecular design · 2016Article
- Knowledge-guided docking: accurate prospective prediction of bound configurations of novel ligands using Surflex-Dock.Journal of computer-aided molecular design · 2015Article
- Chemical and protein structural basis for biological crosstalk between PPARα and COX enzymes.Journal of computer-aided molecular design · 2015Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
We present a probabilistic data fusion framework that combines multiple computational approaches for drawing relationships between drugs and targets. The approach has special relevance to identifying surprising unintended biological targets of drugs. Comparisons between molecules are made based on 2D topological structural considerations, based on 3D surface characteristics, and based on English descriptions of clinical effects. Similarity computations within each modality were transformed into probability scores. Given a new molecule along with a set of molecules sharing some biological effect, a single score based on comparison to the known set is produced, reflecting either 2D similarity, 3D similarity, clinical effects similarity or their combination. The methods were validated within acurated structural pharmacology database (SPDB) and further tested by blind application to data derived from the ChEMBL database. For prediction of off-target effects, 3D-similarity performed best as a single modality, but combining all methods produced performance gains. Striking examples of structurally surprising off-target predictions are presented.
Indexed as
Identifiers
24297543PMC3897331What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.