ArticleMolecules (Basel, Switzerland)2021
Prediction of Molecular Properties Using Molecular Topographic Map.
Article in Molecules (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Drug discovery and mechanism prediction with explainable graph neural networks.Scientific reports · 2025Article
- Matrix-based vector representations in neural networks for classifying molecular biology data.Bioinformatics advances · 2025Article
- Molecular Modeling of Vasodilatory Activity: Unveiling Novel Candidates Through Density Functional Theory, QSAR, and Molecular Dynamics.International journal of molecular sciences · 2024Article
- Gap-Δenergy, a New Metric of the Bond Energy State, Assisting to Predict Molecular Toxicity.ACS omega · 2024Article
- A knowledge-guided pre-training framework for improving molecular representation learning.Nature communications · 2023Article
- QSAR Studies, Molecular Docking, Molecular Dynamics, Synthesis, and Biological Evaluation of Novel Quinolinone-Based Thiosemicarbazones againstAntibiotics (Basel, Switzerland) · 2022Article
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1 author.
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Abstract
Prediction of molecular properties plays a critical role towards rational drug design. In this study, the Molecular Topographic Map (MTM) is proposed, which is a two-dimensional (2D) map that can be used to represent a molecule. An MTM is generated from the atomic features set of a molecule using generative topographic mapping and is then used as input data for analyzing structure-property/activity relationships. In the visualization and classification of 20 amino acids, differences of the amino acids can be visually confirmed from and revealed by hierarchical clustering with a similarity matrix of their MTMs. The prediction of molecular properties was performed on the basis of convolutional neural networks using MTMs as input data. The performance of the predictive models using MTM was found to be equal to or better than that using Morgan fingerprint or MACCS keys. Furthermore, data augmentation of MTMs using mixup has improved the prediction performance. Since molecules converted to MTMs can be treated like 2D images, they can be easily used with existing neural networks for image recognition and related technologies. MTM can be effectively utilized to predict molecular properties of small molecules to aid drug discovery research.
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