ArticleMolecules (Basel, Switzerland)2019
Toxicity Prediction Method Based on Multi-Channel Convolutional Neural Network.
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 8 papers.
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Who cites it
8 citing papers in PubMed.
- Projection-based molecular feature maps for CNN-driven nephrotoxicity prediction.Archives of toxicology · 2026Article
- Quantum-to-Classical Neural Network Transfer Learning Applied to Drug Toxicity Prediction.Journal of chemical theory and computation · 2024Article
- Gap-Δenergy, a New Metric of the Bond Energy State, Assisting to Predict Molecular Toxicity.ACS omega · 2024Article
- Deep learning radiomics based on contrast enhanced MRI for preoperatively predicting early recurrence in hepatocellular carcinoma after curative resection.Frontiers in oncology · 2024Article
- From intuition to AI: evolution of small molecule representations in drug discovery.Briefings in bioinformatics · 2023Article
- Leveraging high-throughput screening data, deep neural networks, and conditional generative adversarial networks to advance predictive toxicology.PLoS computational biology · 2021Article
- Comparing predictive ability of QSAR/QSPR models using 2D and 3D molecular representations.Journal of computer-aided molecular design · 2021Article
- DeepSnap-Deep Learning Approach Predicts Progesterone Receptor Antagonist Activity With High Performance.Frontiers in bioengineering and biotechnology · 2019Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Molecular toxicity prediction is one of the key studies in drug design. In this paper, a deep learning network based on a two-dimension grid of molecules is proposed to predict toxicity. At first, the van der Waals force and hydrogen bond were calculated according to different descriptors of molecules, and multi-channel grids were generated, which could discover more detail and helpful molecular information for toxicity prediction. The generated grids were fed into a convolutional neural network to obtain the result. A Tox21 dataset was used for the evaluation. This dataset contains more than 12,000 molecules. It can be seen from the experiment that the proposed method performs better compared to other traditional deep learning and machine learning methods.
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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.