ArticleJournal of medicinal chemistry2009
Physical binding pocket induction for affinity prediction.
Article in Journal of medicinal chemistry, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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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
9 citing papers in PubMed, 18 citations in OpenAlex.
- Predicting drug-target interactions using matrix factorization with self-paced learning and dual similarity information.Technology and health care : official journal of the European Society for Engineering and Medicine · 2024Article
- Quantitative surface field analysis: learning causal models to predict ligand binding affinity and pose.Journal of computer-aided molecular design · 2018Article
- Predicting drug-target interactions by dual-network integrated logistic matrix factorization.Scientific reports · 2017Article
- Extrapolative prediction using physically-based QSAR.Journal of computer-aided molecular design · 2016Article
- A structure-guided approach for protein pocket modeling and affinity prediction.Journal of computer-aided molecular design · 2013Article
- Iterative refinement of a binding pocket model: active computational steering of lead optimization.Journal of medicinal chemistry · 2012Article
- Does your model weigh the same as a duck?Journal of computer-aided molecular design · 2012Article
- Surface-based protein binding pocket similarity.Proteins · 2011Article
- QMOD: physically meaningful QSAR.Journal of computer-aided molecular design · 2010Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
Computational methods for predicting ligand affinity where no protein structure is known generally take the form of regression analysis based on molecular features that have only a tangential relationship to a protein/ligand binding event. Such methods have limited utility when structural variation moves beyond congeneric series. We present a novel approach based on the multiple-instance learning method of Compass, where a physical model of a binding site is induced from ligands and their corresponding activity data. The model consists of molecular fragments that can account for multiple positions of literal protein residues. We demonstrate the method on 5HT1a ligands by training on a series with limited scaffold variation and testing on numerous ligands with variant scaffolds. Predictive error was between 0.5 and 1.0 log units (0.7-1.4 kcal/mol), with statistically significant rank correlations. Accurate activity predictions of novel ligands were demonstrated using a validation approach where a small number of ligands of limited structural variation known at a fixed time point were used to make predictions on a blind test set of widely varying molecules, some discovered at a much later time point.
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What 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.