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Prediction of Molecular Properties Using Molecular Topographic Map [PDF]
Molecules, 2021 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
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A systematic study of key elements underlying molecular property prediction
Nature Communications, 2023 Artificial intelligence (AI) has been widely applied in drug discovery with a major task as molecular property prediction. Despite booming techniques in molecular representation learning, key elements underlying molecular property prediction remain ...
Jianyuan Deng +5 more
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MetaGIN: a lightweight framework for molecular property prediction
Frontiers of Computer Science, 2023 Abstract Recent advancements in AI-based synthesis of small molecules have led to the creation of extensive databases, housing billions of small molecules. Given this vast scale, traditional quantum chemistry (QC) methods become inefficient for determining the chemical and physical properties of such an extensive array of molecules.
Xuan Zhang +5 more
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Artificial Intelligence-Based Molecular Property Prediction of Photosensitizing Effects of Drugs [PDF]
, 2023 Introduction: Drug-induced photosensitivity is an adverse event of various agents that are used in all major specialties of clinical medicine. Apart from the acute condition, an association of photosensitive events and an increased risk of skin cancer ...
Amun Georg, Hofmann, Asan, Agibetov
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iupacGPT: IUPAC-based large-scale molecular pre-trained model for property prediction and molecule generation [PDF]
, 2023 The IUPAC (International Union of Pure and Applied Chemistry) nomenclature is a globally recognized unique naming system which assigns names to chemical compounds.
Kyoung Tai, No +3 more
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Molecular geometric deep learning
Cell Reports: Methods, 2023 Summary: Molecular representation learning plays an important role in molecular property prediction. Existing molecular property prediction models rely on the de facto standard of covalent-bond-based molecular graphs for representing molecular topology ...
Cong Shen, Jiawei Luo, Kelin Xia
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Explainable Uncertainty Quantifications for Deep Learning-Based Molecular Property Prediction [PDF]
, 2022 Quantifying uncertainty in machine learning is important in new research areas with scarce high-quality data. In this work, we develop an explainable uncertainty quantification method for deep learning-based molecular property prediction. This method can
Chu-I, Yang, Yi-Pei, Li
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Double-head transformer neural network for molecular property prediction
Journal of Cheminformatics, 2023 Existing molecular property prediction methods based on deep learning ignore the generalization ability of the nonlinear representation of molecular features and the reasonable assignment of weights of molecular features, making it difficult to further ...
Yuanbing Song +4 more
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Pre-training Transformers for Molecular Property Prediction Using Reaction Prediction
, 2022 Molecular property prediction is essential in chemistry, especially for drug discovery applications. However, available molecular property data is often limited, encouraging the transfer of information from related data.
Ylipää, Erik +2 more
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Joint Graph-Sequence Learning for Molecular Property Prediction
, 2022 Molecular property prediction has achieved promising improvement for accelerating drug development with machine learning models. The emergence of graph neural networks especially benefits the discriminative representation learning of molecular graph data,
Uddamvathanak, R, Zheng, X, Pan, S
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