ArticleMolecules (Basel, Switzerland)2019
Modeling Physico-Chemical ADMET Endpoints with Multitask Graph Convolutional Networks.
Article in Molecules (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed.
- Artificial intelligence empowers targeted protein degradation: Core technological innovations, multi-scenario applications, and translational prospects.Smart molecules : open access · 2026Review
- Graph-based transformer to predict the octanol-water partition coefficient.Journal of cheminformatics · 2026Article
- Tiliacorinine as a Promising Candidate for Cholangiocarcinoma Therapy via Oxidative Stress Molecule Modulation: A Study Integrating Network Pharmacology, Molecular Docking and Molecular Dynamics Simulation.Antioxidants (Basel, Switzerland) · 2025Article
- Hybrid fragment-SMILES tokenization for ADMET prediction in drug discovery.BMC bioinformatics · 2024Article
- A data science roadmap for open science organizations engaged in early-stage drug discovery.Nature communications · 2024Review
- Deep-PK: deep learning for small molecule pharmacokinetic and toxicity prediction.Nucleic acids research · 2024Article
- Absorption Distribution Metabolism Excretion and Toxicity Property Prediction Utilizing a Pre-Trained Natural Language Processing Model and Its Applications in Early-Stage Drug Development.Pharmaceuticals (Basel, Switzerland) · 2024Article
- Formulation Graphs for Mapping Structure-Composition of Battery Electrolytes to Device Performance.Journal of chemical information and modeling · 2023Article
- ADMET property prediction via multi-task graph learning under adaptive auxiliary task selection.iScience · 2023Article
- Equivariant Graph Neural Networks for Toxicity Prediction.Chemical research in toxicology · 2023Article
- Explaining compound activity predictions with a substructure-aware loss for graph neural networks.Journal of cheminformatics · 2023Article
- Prediction of ADMET Properties of Anti-Breast Cancer Compounds Using Three Machine Learning Algorithms.Molecules (Basel, Switzerland) · 2023Article
- Article
- Cheminformatics and artificial intelligence for accelerating agrochemical discovery.Frontiers in chemistry · 2023Review
- Topology-enhanced molecular graph representation for anti-breast cancer drug selection.BMC bioinformatics · 2022Article
- Block-wise Exploration of Molecular Descriptors with Multi-block Orthogonal Component Analysis (MOCA).Molecular informatics · 2022Article
- PlayMolecule Glimpse: Understanding Protein-Ligand Property Predictions with Interpretable Neural Networks.Journal of chemical information and modeling · 2022Article
- Drug-likeness scoring based on unsupervised learning.Chemical science · 2022Article
- Graph neural networks for materials science and chemistry.Communications materials · 2022Review
- Don't Overweight Weights: Evaluation of Weighting Strategies for Multi-Task Bioactivity Classification Models.Molecules (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Simple physico-chemical properties, like logD, solubility, or melting point, can reveal a great deal about how a compound under development might later behave. These data are typically measured for most compounds in drug discovery projects in a medium throughput fashion. Collecting and assembling all the Bayer in-house data related to these properties allowed us to apply powerful machine learning techniques to predict the outcome of those assays for new compounds. In this paper, we report our finding that, especially for predicting physicochemical ADMET endpoints, a multitask graph convolutional approach appears a highly competitive choice. For seven endpoints of interest, we compared the performance of that approach to fully connected neural networks and different single task models. The new model shows increased predictive performance compared to previous modeling methods and will allow early prioritization of compounds even before they are synthesized. In addition, our model follows the generalized solubility equation without being explicitly trained under this constraint.
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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.