ArticleJournal of medicinal chemistry2012
Iterative refinement of a binding pocket model: active computational steering of lead optimization.
Article in Journal of medicinal chemistry, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 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
13 citing papers in PubMed.
- Advancing genetic engineering with active learning: theory, implementations and potential opportunities.Briefings in bioinformatics · 2025Review
- From UK-2A to florylpicoxamid: Active learning to identify a mimic of a macrocyclic natural product.Journal of computer-aided molecular design · 2024Article
- Benchmarking Active Learning Protocols for Ligand-Binding Affinity Prediction.Journal of chemical information and modeling · 2024Article
- Novel Efficient Multistage Lead Optimization Pipeline Experimentally Validated for DYRK1B Selective Inhibitors.Journal of medicinal chemistry · 2022Article
- Data-driven discovery of cardiolipin-selective small molecules by computational active learning.Chemical science · 2022Article
- Comprehensive strategies of machine-learning-based quantitative structure-activity relationship models.iScience · 2021Review
- Quantitative surface field analysis: learning causal models to predict ligand binding affinity and pose.Journal of computer-aided molecular design · 2018Article
- Multi-objective active machine learning rapidly improves structure-activity models and reveals new protein-protein interaction inhibitors.Chemical science · 2016Article
- Extrapolative prediction using physically-based QSAR.Journal of computer-aided molecular design · 2016Article
- Drug Design for CNS Diseases: Polypharmacological Profiling of Compounds Using Cheminformatic, 3D-QSAR and Virtual Screening Methodologies.Frontiers in neuroscience · 2016Review
- Is ring breaking feasible in relative binding free energy calculations?Journal of chemical information and modeling · 2015Article
- QSAR modeling: where have you been? Where are you going to?Journal of medicinal chemistry · 2014Article
- A structure-guided approach for protein pocket modeling and affinity prediction.Journal of computer-aided molecular design · 2013Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Computational approaches for binding affinity prediction are most frequently demonstrated through cross-validation within a series of molecules or through performance shown on a blinded test set. Here, we show how such a system performs in an iterative, temporal lead optimization exercise. A series of gyrase inhibitors with known synthetic order formed the set of molecules that could be selected for "synthesis." Beginning with a small number of molecules, based only on structures and activities, a model was constructed. Compound selection was done computationally, each time making five selections based on confident predictions of high activity and five selections based on a quantitative measure of three-dimensional structural novelty. Compound selection was followed by model refinement using the new data. Iterative computational candidate selection produced rapid improvements in selected compound activity, and incorporation of explicitly novel compounds uncovered much more diverse active inhibitors than strategies lacking active novelty selection.
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.