ArticleJournal of computer-aided molecular design2022
Simplified, interpretable graph convolutional neural networks for small molecule activity prediction.
Article in Journal of computer-aided molecular design, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- AI and network biology for rational polypharmacology in signaling drug design: a review.NPJ precision oncology · 2026Review
- A Comprehensive Investigation: Developing the Pharmaceutical Industry through Artificial Intelligence.Current drug discovery technologies · 2025Review
- Topological regression as an interpretable and efficient tool for quantitative structure-activity relationship modeling.Nature communications · 2024Article
- Enhancing property and activity prediction and interpretation using multiple molecular graph representations with MMGX.Communications chemistry · 2024Article
- Advancing material property prediction: using physics-informed machine learning models for viscosity.Journal of cheminformatics · 2024Article
- Extended study on atomic featurization in graph neural networks for molecular property prediction.Journal of cheminformatics · 2023Article
- A Perspective on Explanations of Molecular Prediction Models.Journal of chemical theory and computation · 2023Review
- Molecular Property Prediction by Combining LSTM and GAT.Biomolecules · 2023Article
- Cheminformatics and artificial intelligence for accelerating agrochemical discovery.Frontiers in chemistry · 2023Review
- Deep generative molecular design reshapes drug discovery.Cell reports. Medicine · 2022Review
- AI-Based Computational Methods in Early Drug Discovery and Post Market Drug Assessment: A Survey.IEEE transactions on computational biology and bioinformaticsReview
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We here present a streamlined, explainable graph convolutional neural network (gCNN) architecture for small molecule activity prediction. We first conduct a hyperparameter optimization across nearly 800 protein targets that produces a simplified gCNN QSAR architecture, and we observe that such a model can yield performance improvements over both standard gCNN and RF methods on difficult-to-classify test sets. Additionally, we discuss how reductions in convolutional layer dimensions potentially speak to the "anatomical" needs of gCNNs with respect to radial coarse graining of molecular substructure. We augment this simplified architecture with saliency map technology that highlights molecular substructures relevant to activity, and we perform saliency analysis on nearly 100 data-rich protein targets. We show that resultant substructural clusters are useful visualization tools for understanding substructure-activity relationships. We go on to highlight connections between our models' saliency predictions and observations made in the medicinal chemistry literature, focusing on four case studies of past lead finding and lead optimization campaigns.
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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.