ArticleScientific reports2021
Prediction of pharmacological activities from chemical structures with graph convolutional neural networks.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Graph Neural Networks Model Based on Atomic Hybridization for Predicting Drug Targets.Journal of chemical information and modeling · 2026Article
- Artificial intelligence drives the identification and screening of novel antibiotics and antimicrobial peptides.Briefings in bioinformatics · 2026Review
- Graph Neural Networks Model Based on Atomic Hybridization for Predicting Drug Targets.bioRxiv : the preprint server for biology · 2025Article
- MaskMol: knowledge-guided molecular image pre-training framework for activity cliffs with pixel masking.BMC biology · 2025Article
- GraphDeep-hERG: Graph Neural Network PharmacoAnalytics for Assessing hERG-Related Cardiotoxicity.Pharmaceutical research · 2025Article
- AiGPro: a multi-tasks model for profiling of GPCRs for agonist and antagonist.Journal of cheminformatics · 2025Article
- Classification of substances by health hazard using deep neural networks and molecular electron densities.Journal of cheminformatics · 2024Article
- Functional microRNA-targeting drug discovery by graph-based deep learning.Patterns (New York, N.Y.) · 2024Article
- Article
- Prediction of pathogenic single amino acid substitutions using molecular fragment descriptors.Bioinformatics (Oxford, England) · 2023Article
- AiKPro: deep learning model for kinome-wide bioactivity profiling using structure-based sequence alignments and molecular 3D conformer ensemble descriptors.Scientific reports · 2023Article
- Bio-activity prediction of drug candidate compounds targeting SARS-Cov-2 using machine learning approaches.PloS one · 2023Article
- Studying and mitigating the effects of data drifts on ML model performance at the example of chemical toxicity data.Scientific reports · 2022Article
- Ligand-based approaches to activity prediction for the early stage of structure-activity-relationship progression.Journal of computer-aided molecular design · 2022Article
- Assisting Multitargeted Ligand Affinity Prediction of Receptor Tyrosine Kinases Associated Nonsmall Cell Lung Cancer Treatment with Multitasking Principal Neighborhood Aggregation.Molecules (Basel, Switzerland) · 2022Article
- PlayMolecule Glimpse: Understanding Protein-Ligand Property Predictions with Interpretable Neural Networks.Journal of chemical information and modeling · 2022Article
- MoCL: Data-driven Molecular Fingerprint via Knowledge-aware Contrastive Learning from Molecular Graph.KDD : proceedings. International Conference on Knowledge Discovery & Data Mining · 2021Article
- Graph Neural Networks as a Potential Tool in Improving Virtual Screening Programs.Frontiers in chemistry · 2021Review
- pdCSM-GPCR: predicting potent GPCR ligands with graph-based signatures.Bioinformatics advances · 2021Article
- De novo Prediction of Cell-Drug Sensitivities Using Deep Learning-based Graph Regularized Matrix Factorization.Pacific Symposium on Biocomputing. Pacific Symposium on BiocomputingArticle
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Many therapeutic drugs are compounds that can be represented by simple chemical structures, which contain important determinants of affinity at the site of action. Recently, graph convolutional neural network (GCN) models have exhibited excellent results in classifying the activity of such compounds. For models that make quantitative predictions of activity, more complex information has been utilized, such as the three-dimensional structures of compounds and the amino acid sequences of their respective target proteins. As another approach, we hypothesized that if sufficient experimental data were available and there were enough nodes in hidden layers, a simple compound representation would quantitatively predict activity with satisfactory accuracy. In this study, we report that GCN models constructed solely from the two-dimensional structural information of compounds demonstrated a high degree of activity predictability against 127 diverse targets from the ChEMBL database. Using the information entropy as a metric, we also show that the structural diversity had less effect on the prediction performance. Finally, we report that virtual screening using the constructed model identified a new serotonin transporter inhibitor with activity comparable to that of a marketed drug in vitro and exhibited antidepressant effects in behavioural studies.
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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.