ArticleJournal of chemical information and modeling2015
Neural-Network Scoring Functions Identify Structurally Novel Estrogen-Receptor Ligands.
Article in Journal of chemical information and modeling, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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
11 citing papers in PubMed.
- Inactive-enriched machine-learning models exploiting patent data improve structure-based virtual screening for PDL1 dimerizers.Journal of advanced research · 2025Article
- Comprehensive machine learning boosts structure-based virtual screening for PARP1 inhibitors.Journal of cheminformatics · 2024Article
- A practical guide to machine-learning scoring for structure-based virtual screening.Nature protocols · 2023Review
- Beware of Simple Methods for Structure-Based Virtual Screening: The Critical Importance of Broader Comparisons.Journal of chemical information and modeling · 2023Article
- Structure-based virtual screening for PDL1 dimerizers: Evaluating generic scoring functions.Current research in structural biology · 2022Review
- Application of Various Molecular Modelling Methods in the Study of Estrogens and Xenoestrogens.International journal of molecular sciences · 2020Review
- Machine learning classification can reduce false positives in structure-based virtual screening.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
- Computer-Aided Ligand Discovery for Estrogen Receptor Alpha.International journal of molecular sciences · 2020Review
- Key Topics in Molecular Docking for Drug Design.International journal of molecular sciences · 2019Review
- Improving small molecule virtual screening strategies for the next generation of therapeutics.Current opinion in chemical biology · 2018Review
- Protein-Ligand Scoring with Convolutional Neural Networks.Journal of chemical information and modeling · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
The magnitude of the investment required to bring a drug to the market hinders medical progress, requiring hundreds of millions of dollars and years of research and development. Any innovation that improves the efficiency of the drug-discovery process has the potential to accelerate the delivery of new treatments to countless patients in need. "Virtual screening," wherein molecules are first tested in silico in order to prioritize compounds for subsequent experimental testing, is one such innovation. Although the traditional scoring functions used in virtual screens have proven useful, improved accuracy requires novel approaches. In the current work, we use the estrogen receptor to demonstrate that neural networks are adept at identifying structurally novel small molecules that bind to a selected drug target, ultimately allowing experimentalists to test fewer compounds in the earliest stages of lead identification while obtaining higher hit rates. We describe 39 novel estrogen-receptor ligands identified in silico with experimentally determined Ki values ranging from 460 nM to 20 μM, presented here for the first time.
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.