ArticleJournal of natural products2024
PECAN Predicts Patterns of Cancer Cell Cytostatic Activity of Natural Products Using Deep Learning.
Article in Journal of natural products, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Combating Antibacterial Resistance: The Integrative Role of Artificial Intelligence in Bio-Based Product Development.Antibiotics (Basel, Switzerland) · 2026Review
- Green genes from blue greens: challenges and solutions to unlocking the potential of cyanobacteria in drug discovery.Natural product reports · 2026Review
- Predictive modeling of flavonoid efficacy against esophageal carcinoma: a comprehensive approach.Scientific reports · 2025Article
- Advances, opportunities, and challenges in methods for interrogating the structure activity relationships of natural products.Natural product reports · 2024Review
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Authors and funding
5 authors.
Funding
Abstract
Many machine learning techniques are used as drug discovery tools with the intent to speed characterization by determining relationships between compound structure and biological function. However, particularly in anticancer drug discovery, these models often make only binary decisions about the biological activity for a narrow scope of drug targets. We present a feed-forward neural network, PECAN (Prediction Engine for the Cytostatic Activity of Natural product-like compounds), that simultaneously classifies the potential antiproliferative activity of compounds against 59 cancer cell lines. It predicts the activity to be one of six categories, indicating not only if activity is present but the degree of activity. Using an independent subset of NCI data as a test set, we show that PECAN can reach 60.1% accuracy in a six-way classification and present further evidence that it classifies based on useful structural features of compounds using a "within-one" measure that reaches 93.0% accuracy.
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Registered trials
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