ArticleJournal of medicinal chemistry2006
Development of quantitative structure-binding affinity relationship models based on novel geometrical chemical descriptors of the protein-ligand interfaces.
Article in Journal of medicinal chemistry, 2006. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 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
29 citing papers in PubMed.
- Predicting Inhibition of CDK2 with SAnDReS: The Application of Machine Learning to Navigate the Scoring Function Space.Current medicinal chemistry · 2025Review
- Development and evaluation of a java-based deep neural network method for drug response predictions.Frontiers in artificial intelligence · 2023Article
- Pyridine-N-Oxide Alkaloids fromBioMed research international · 2022Article
- Machine learning on ligand-residue interaction profiles to significantly improve binding affinity prediction.Briefings in bioinformatics · 2021Article
- SAG-DTA: Prediction of Drug-Target Affinity Using Self-Attention Graph Network.International journal of molecular sciences · 2021Article
- Article
- Understanding Nanoparticle Toxicity to Direct a Safe-by-Design Approach in Cancer Nanomedicine.Nanomaterials (Basel, Switzerland) · 2020Review
- Prediction of Antimalarial Drug-Decorated Nanoparticle Delivery Systems with Random Forest Models.Biology · 2020Article
- Review
- The discovery of novel HDAC3 inhibitors via virtual screening and in vitro bioassay.Journal of enzyme inhibition and medicinal chemistry · 2018Article
- The Development of Target-Specific Pose Filter Ensembles To Boost Ligand Enrichment for Structure-Based Virtual Screening.Journal of chemical information and modeling · 2017Article
- A comparative study of family-specific protein-ligand complex affinity prediction based on random forest approach.Journal of computer-aided molecular design · 2015Article
- Estimation of acute oral toxicity in rat using local lazy learning.Journal of cheminformatics · 2014Article
- Do crystal structures obviate the need for theoretical models of GPCRs for structure-based virtual screening?Proteins · 2012Article
- Cheminformatics meets molecular mechanics: a combined application of knowledge-based pose scoring and physical force field-based hit scoring functions improves the accuracy of structure-based virtual screening.Journal of chemical information and modeling · 2012Article
- From laptop to benchtop to bedside: structure-based drug design on protein targets.Current pharmaceutical design · 2012Review
- Combined application of cheminformatics- and physical force field-based scoring functions improves binding affinity prediction for CSAR data sets.Journal of chemical information and modeling · 2011Article
- Binding affinity prediction with property-encoded shape distribution signatures.Journal of chemical information and modeling · 2010Article
- Molecular networks in drug discovery.Critical reviews in biomedical engineering · 2010Review
- Scoring function for DNA-drug docking of anticancer and antiparasitic compounds based on spectral moments of 2D lattice graphs for molecular dynamics trajectories.European journal of medicinal chemistry · 2009Article
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
Novel geometrical chemical descriptors have been derived on the basis of the computational geometry of protein-ligand interfaces and Pauling atomic electronegativities (EN). Delaunay tessellation has been applied to a diverse set of 517 X-ray characterized protein-ligand complexes yielding a unique collection of interfacial nearest neighbor atomic quadruplets for each complex. Each quadruplet composition was characterized by a single descriptor calculated as the sum of the EN values for the four participating atom types. We termed these simple descriptors generated from atomic EN values and derived with the Delaunay Tessellation the ENTess descriptors and used them in the variable selection k-nearest neighbor quantitative structure-binding affinity relationship (QSBR) studies of 264 diverse protein-ligand complexes with known binding constants. Twenty-four complexes with chemically dissimilar ligands were set aside as an independent validation set, and the remaining dataset of 240 complexes was divided into multiple training and test sets. The best models were characterized by the leave-one-out cross-validated correlation coefficient q(2) as high as 0.66 for the training set and the correlation coefficient R(2) as high as 0.83 for the test set. The high predictive power of these models was confirmed independently by applying them to the validation set of 24 complexes yielding R(2) as high as 0.85. We conclude that QSBR models built with the ENTess descriptors can be instrumental for predicting the binding affinity of receptor-ligand complexes.
Indexed as
Identifiers
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.