ArticleBiomolecules2020
Analysis of Biological Screening Compounds with Single- or Multi-Target Activity via Diagnostic Machine Learning.
Article in Biomolecules, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- AI-Assisted Detection of Biomarkers by Sensors and Biosensors for Early Diagnosis and Monitoring.Biosensors · 2024Review
- Explaining Multiclass Compound Activity Predictions Using Counterfactuals and Shapley Values.Molecules (Basel, Switzerland) · 2023Article
- Explaining Accurate Predictions of Multitarget Compounds with Machine Learning Models Derived for Individual Targets.Molecules (Basel, Switzerland) · 2023Article
- Fine-tuning of a generative neural network for designing multi-target compounds.Journal of computer-aided molecular design · 2022Article
- Differentiating Inhibitors of Closely Related Protein Kinases with Single- or Multi-Target Activity via Explainable Machine Learning and Feature Analysis.Biomolecules · 2022Article
- A Discovery Strategy for Active Compounds of Chinese Medicine Based on the Prediction Model of Compound-Disease Relationship.Journal of oncology · 2022Article
- Explainable machine learning predictions of dual-target compounds reveal characteristic structural features.Scientific reports · 2021Article
- Machine learning reveals that structural features distinguishing promiscuous and non-promiscuous compounds depend on target combinations.Scientific reports · 2021Article
- A Novel Graph Neural Network Methodology to Investigate Dihydroorotate Dehydrogenase Inhibitors in Small Cell Lung Cancer.Biomolecules · 2021Article
- Structured data sets of compounds with multi-target and corresponding single-target activity from biological assays.Future science OA · 2021Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Predicting compounds with single- and multi-target activity and exploring origins of compound specificity and promiscuity is of high interest for chemical biology and drug discovery. We present a large-scale analysis of compound promiscuity including two major components. First, high-confidence datasets of compounds with multi- and corresponding single-target activity were extracted from biological screening data. Positive and negative assay results were taken into account and data completeness was ensured. Second, these datasets were investigated using diagnostic machine learning to systematically distinguish between compounds with multi- and single-target activity. Models built on the basis of chemical structure consistently produced meaningful predictions. These findings provided evidence for the presence of structural features differentiating promiscuous and non-promiscuous compounds. Machine learning under varying conditions using modified datasets revealed a strong influence of nearest neighbor relationship on the predictions. Many multi-target compounds were found to be more similar to other multi-target compounds than single-target compounds and vice versa, which resulted in consistently accurate predictions. The results of our study confirm the presence of structural relationships that differentiate promiscuous and non-promiscuous compounds.
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