ArticleBriefings in bioinformatics2021
Comparative analysis of molecular fingerprints in prediction of drug combination effects.
Article in Briefings in bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
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Who cites it
38 citing papers in PubMed.
- MacroTox: A Macroscopic Graph Topology-Based Multimodal Learning Framework for Robust Molecular Toxicity Prediction.JACS Au · 2026Article
- Ensemble Machine Learning- and Deep Learning-Driven Identification and Validation of Sennidin B as a Novel Dipeptidyl Peptidase-4 Inhibitor.International journal of molecular sciences · 2026Article
- ChronicDPipredictor: an interpretable deep learning framework for chemical chronic and subchronic toxicity assessment.Molecular diversity · 2026Article
- TensoGraph: a tensor-transformer framework for global-local drug synergy prediction on heterogeneous graphs.Briefings in bioinformatics · 2026Article
- A comprehensive review of cluster methods for drug-drug interaction network.Quantitative biology (Beijing, China) · 2026Review
- A survey of contrastive learning methods in molecular representation.Briefings in bioinformatics · 2026Review
- Machine learning for drug-target interaction prediction: A comprehensive review of models, challenges, and computational strategies.Computational and structural biotechnology journal · 2026Review
- Enhancing cancer drug discovery: QSAR modeling with machine learning and chemical representations.PloS one · 2026Article
- MetAP DB and Metal-FP: a database and fingerprint framework for advancing metal-based drug discovery.Journal of computer-aided molecular design · 2025Article
- MoleculeFormer is a GCN-transformer architecture for molecular property prediction.Communications biology · 2025Article
- Exploring Artificial Intelligence's Potential to Enhance Conventional Anticancer Drug Development.Drug development research · 2025Review
- KGG: Knowledge-Guided Graph Self-Supervised Learning to Enhance Molecular Property Predictions.Journal of chemical information and modeling · 2025Article
- The topology of molecular representations and its influence on machine learning performance.Journal of cheminformatics · 2025Article
- A Multi-Task Self-Supervised Strategy for Predicting Molecular Properties and FGFR1 Inhibitors.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Identifying traditional Chinese medicine combinations for breast cancer treatment based on transcriptional regulation and chemical structure.Chinese medicine · 2025Article
- Evaluating data partitioning strategies for accurate prediction of protein-ligand binding free energy changes in mutated proteins.Computational and structural biotechnology journal · 2025Article
- Self-Driving Laboratories for Chemistry and Materials Science.Chemical reviews · 2024Review
- Deep learning algorithms applied to computational chemistry.Molecular diversity · 2024Review
- Predicting small molecules solubility on endpoint devices using deep ensemble neural networks.Digital discovery · 2024Article
- Directional ΔJournal of chemical information and modeling · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Application of machine and deep learning methods in drug discovery and cancer research has gained a considerable amount of attention in the past years. As the field grows, it becomes crucial to systematically evaluate the performance of novel computational solutions in relation to established techniques. To this end, we compare rule-based and data-driven molecular representations in prediction of drug combination sensitivity and drug synergy scores using standardized results of 14 high-throughput screening studies, comprising 64 200 unique combinations of 4153 molecules tested in 112 cancer cell lines. We evaluate the clustering performance of molecular representations and quantify their similarity by adapting the Centered Kernel Alignment metric. Our work demonstrates that to identify an optimal molecular representation type, it is necessary to supplement quantitative benchmark results with qualitative considerations, such as model interpretability and robustness, which may vary between and throughout preclinical drug development projects.
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