ArticleJournal of chemical information and modeling2022
Exposing the Limitations of Molecular Machine Learning with Activity Cliffs.
Article in Journal of chemical information and modeling, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 119 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
119 citing papers in PubMed.
- Global descriptors informed learning for organic melting-point prediction.RSC advances · 2026Article
- A critical appraisal of structure-based virtual screening, binding free energy methods, and AI-driven molecular design across the evolving computational landscape of drug discovery.Journal of computer-aided molecular design · 2026Review
- Chemical language models for early-stage drug discovery: applications, pitfalls, and future directions.Journal of computer-aided molecular design · 2026Review
- PAD4-DB: A Curated Structure-Activity Resource Reveals Hub-Organized Activity Cliffs and Scaffold-Dependent SAR Ruggedness in PAD4 Inhibitors.International journal of molecular sciences · 2026Article
- µORScreen: a lightweight consensus modeling framework for µ-opioid receptor ligand prediction and virtual screening.RSC advances · 2026Article
- Data-driven interfacial regulations through molecular additive screening for batteries and electrocatalysis.Chemical science · 2026Review
- How effective are contrastive learning-based approaches for activity-cliff prediction?bioRxiv : the preprint server for biology · 2026Article
- Robots and Minimal, Physics-Informed Features: A Hybrid Framework for Enzyme Catalysis.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Efficient visualization of chemical space.Chemical science · 2026Article
- Task-adaptive multimodal molecular representations for structure-sensitive property prediction.Chemical science · 2026Article
- Reliability of AI Methods in Drug Discovery: Evaluation of Boltz-2 for Structure and Binding Affinity Prediction.Journal of chemical theory and computation · 2026Article
- Artificial intelligence in drug discovery - what it is, where we stand and the path forward.Nature reviews. Drug discovery · 2026Review
- Reliable Apparent Permeability Prediction Through Benchmarking, Stability, Uncertainty Quantification, and Explainable Machine Learning.CPT: pharmacometrics & systems pharmacology · 2026Article
- Activity-cliff awareness enables robust graph learning for molecular property prediction.Nature communications · 2026Article
- NMR-AI: An Open Platform for NMR-Enhanced Molecular Representations and Physicochemical Property Prediction.Journal of chemical information and modeling · 2026Article
- Organic Chemistry as a Catalyst for AI Innovation: Challenges, Methods, and Emerging Paradigms.Chemical reviews · 2026Review
- Improving hit discovery by integrating activity cliff sensitivity into active learning.Bioinformatics (Oxford, England) · 2026Article
- Undersampling Techniques for Nonlinear Chemical Space Visualization.Molecular informatics · 2026Article
- Toward decision-grade interpretability in melting-transition modeling: integrating uncertainty, applicability domains, activity cliffs, and reproducibility in molecular machine learning.Journal of molecular modeling · 2026Article
- Meta-Learning as a Promising Strategy for Lipid Nanoparticle Optimization and Ionizable Lipid Discovery.Nano letters · 2026Article
59 more citing papers are in PubMed but not listed here.
Corrections and comments
- Erratum issued
- Erratum issued
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine learning has become a crucial tool in drug discovery and chemistry at large,
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.