ArticleJournal of chemical theory and computation2013
One Size Does Not Fit All: The Limits of Structure-Based Models in Drug Discovery.
Article in Journal of chemical theory and computation, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers.
What it found
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Who cites it
49 citing papers in PubMed.
- Bridging between Structure-Based and Data-Driven Affinity Prediction.Journal of chemical information and modeling · 2026Article
- Comparative Assessment of Free Energy Computational Methods for Revealing the Interactions Driving PARP1 Selective Inhibition.Journal of chemical information and modeling · 2026Article
- Cooperative molecular interaction networks govern PARP1 inhibitor selectivity and binding affinity.PLoS computational biology · 2026Article
- Integrated Computer-Aided Drug Design: Advances in GPCR Natural Ligand Discovery.Cell biochemistry and biophysics · 2026Review
- Tree-Based Methods to Predict Enzyme Inhibition.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Molegro Data Modeller to Estimate CDK6 Inhibition.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Differential Evolution for Docking Simulations.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Targeting CDK9 with Molegro Virtual Docker.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Neural Networks with Molegro Data Modeller.Methods in molecular biology (Clifton, N.J.) · 2026Article
- AlphaFold for Docking Screens.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Decision Tree for Prediction of Binding Affinity.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Extremely Randomized Trees to Determine Binding Affinity.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Molegro Data Modeller for Machine Learning.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Combining MVD and Ridge Method to Predict CDK2 Inhibition.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Calculating Enzyme Inhibition with Random Forests.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Exploring the Scoring Function Space with Lasso Regression.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Hands-On Docking with Molegro Virtual Docker.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Elastic Net Regression to Predict CDK2 Inhibition.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Gradient Descent to Predict Enzyme Inhibition.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Molegro Virtual Docker for Docking Screens.Methods in molecular biology (Clifton, N.J.) · 2026Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A major goal in computational chemistry has been to discover the set of rules that can accurately predict the binding affinity of any protein-drug complex, using only a single snapshot of its three-dimensional structure. Despite the continual development of structure-based models, predictive accuracy remains low, and the fundamental factors that inhibit the inference of all-encompassing rules have yet to be fully explored. Using statistical learning theory and information theory, here we prove that even the very best generalized structure-based model is inherently limited in its accuracy, and protein-specific models are always likely to be better. Our results refute the prevailing assumption that large data sets and advanced machine learning techniques will yield accurate, universally applicable models. We anticipate that the results will aid the development of more robust virtual screening strategies and scoring function error estimations.
Identifiers
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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.