ArticleNature communications2023
A systematic study of key elements underlying molecular property prediction.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 74 papers, 1 of them a synthesis that pooled it.
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Who cites it
74 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic review of molecular representation learning foundation models.Briefings in bioinformatics · 2026Pooled it
- Times are changing but order matters: transferable prediction of small-molecule liquid chromatography retention times.Nature methods · 2026Article
- Bridging BET bromodomain and immune checkpoint inhibitors through generative bioorganic frameworks for next-generation cancer immunotherapy.RSC medicinal chemistry · 2026Review
- Data-driven interfacial regulations through molecular additive screening for batteries and electrocatalysis.Chemical science · 2026Review
- Task-adaptive multimodal molecular representations for structure-sensitive property prediction.Chemical science · 2026Article
- MSF-HierGNN: a multi-source substructure-fusion hierarchical GNN method and web server to predict molecular property for drug design.Briefings in bioinformatics · 2026Article
- A survey of transformer and LLM-based architectures, workflows, and systems in drug discovery.Journal of computer-aided molecular design · 2026Review
- Trust Beyond Accuracy: Conformal Uncertainty Quantification Reveals the Generalization Gap in Polymer Glass Transition Temperature Prediction.ACS omega · 2026Article
- A unified framework for molecular property prediction based on hierarchical multi-granularity molecular representation learning.Bioinformatics (Oxford, England) · 2026Article
- Activity-cliff awareness enables robust graph learning for molecular property prediction.Nature communications · 2026Article
- Transformers for molecular property prediction: domain adaptation efficiently improves performance.Journal of cheminformatics · 2026Article
- A Decision-Oriented Calibrated Screening Workflow for Tylosin Derivatives: Closed-Loop MIC Validation AgainstAntibiotics (Basel, Switzerland) · 2026Article
- Improving Generalizability in Whole-Cell Antibiotic Discovery Through Active Learning.bioRxiv : the preprint server for biology · 2026Article
- Multimodal feature fusion for molecular property classification.Journal of cheminformatics · 2026Article
- Improving hit discovery by integrating activity cliff sensitivity into active learning.Bioinformatics (Oxford, England) · 2026Article
- Dynamics-enhanced molecular property prediction guided by deep learning.PLoS computational biology · 2026Article
- MolProphecy: Bridging medicinal chemists' knowledge and molecular pre-trained models via a multi-modal framework.Journal of advanced research · 2026Article
- Enhancing molecular property prediction of transformer models with dual graph representation.Nature communications · 2026Article
- Prediction of Ligand Binding to Transthyretin Using Machine Learning Algorithms and Low-Dimensional Molecular Descriptors: A Tox24 Challenge Study.Chemical research in toxicology · 2026Article
- The Tsetlin Machine: A "Third Way" in QSAR Modeling.Journal of chemical information and modeling · 2026Article
14 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) has been widely applied in drug discovery with a major task as molecular property prediction. Despite booming techniques in molecular representation learning, key elements underlying molecular property prediction remain largely unexplored, which impedes further advancements in this field. Herein, we conduct an extensive evaluation of representative models using various representations on the MoleculeNet datasets, a suite of opioids-related datasets and two additional activity datasets from the literature. To investigate the predictive power in low-data and high-data space, a series of descriptors datasets of varying sizes are also assembled to evaluate the models. In total, we have trained 62,820 models, including 50,220 models on fixed representations, 4200 models on SMILES sequences and 8400 models on molecular graphs. Based on extensive experimentation and rigorous comparison, we show that representation learning models exhibit limited performance in molecular property prediction in most datasets. Besides, multiple key elements underlying molecular property prediction can affect the evaluation results. Furthermore, we show that activity cliffs can significantly impact model prediction. Finally, we explore into potential causes why representation learning models can fail and show that dataset size is essential for representation learning models to excel.
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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.