The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Des études récentes sur la prédiction de la structure des protéines, telles qu'AlphaFold, ont permis à l'apprentissage profond d'attirer une grande attention sur la tâche Drug-Target Affinity (DTA). La plupart des travaux se consacrent à intégrer une propriété moléculaire unique et des informations homogènes, ignorant les divers gains d'informations hétérogènes contenus dans les molécules et les interactions. Motivés par cela, nous proposons un cadre d'apprentissage profond de bout en bout pour effectuer la fusion de fonctionnalités moléculaires hétérogènes (MolHF) pour la prédiction DTA sur l'hétérogénéité. Pour relever les défis que les attributs biochimiques localisent dans différents espaces hétérogènes, nous concevons un module d'apprentissage de l'information moléculaire hétérogène avec un apprentissage multi-stratégies. En particulier, le module Molecular Heterogeneous Attention Fusion est présent pour obtenir les gains des caractéristiques moléculaires hétérogènes. Grâce à ceux-ci, la diversité des informations sur la structure moléculaire des médicaments peut être extraite. Des expériences approfondies sur deux ensembles de données de référence montrent que notre méthode surpasse les lignes de base dans les quatre mesures. Les études d'ablation valident l'effet d'une fusion attentive et de caractéristiques hétérogènes de plusieurs groupes de médicaments. Les présentations visuelles démontrent l'impact du niveau d'incorporation des protéines et la capacité du modèle à ajuster les données. En résumé, les divers gains apportés par des informations hétérogènes contribuent à la prédiction de l’affinité médicament-cible.
Runze WANG
Taiyuan University of Technology
Zehua ZHANG
Taiyuan University of Technology
Yueqin ZHANG
Taiyuan University of Technology
Zhongyuan JIANG
Xidian University
Shilin SUN
Taiyuan University of Technology
Guixiang MA
University of Illinois at Chicago
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Runze WANG, Zehua ZHANG, Yueqin ZHANG, Zhongyuan JIANG, Shilin SUN, Guixiang MA, "MolHF: Molecular Heterogeneous Attributes Fusion for Drug-Target Affinity Prediction on Heterogeneity" in IEICE TRANSACTIONS on Information,
vol. E106-D, no. 5, pp. 697-706, May 2023, doi: 10.1587/transinf.2022DLP0023.
Abstract: Recent studies in protein structure prediction such as AlphaFold have enabled deep learning to achieve great attention on the Drug-Target Affinity (DTA) task. Most works are dedicated to embed single molecular property and homogeneous information, ignoring the diverse heterogeneous information gains that are contained in the molecules and interactions. Motivated by this, we propose an end-to-end deep learning framework to perform Molecular Heterogeneous features Fusion (MolHF) for DTA prediction on heterogeneity. To address the challenges that biochemical attributes locates in different heterogeneous spaces, we design a Molecular Heterogeneous Information Learning module with multi-strategy learning. Especially, Molecular Heterogeneous Attention Fusion module is present to obtain the gains of molecular heterogeneous features. With these, the diversity of molecular structure information for drugs can be extracted. Extensive experiments on two benchmark datasets show that our method outperforms the baselines in all four metrics. Ablation studies validate the effect of attentive fusion and multi-group of drug heterogeneous features. Visual presentations demonstrate the impact of protein embedding level and the model ability of fitting data. In summary, the diverse gains brought by heterogeneous information contribute to drug-target affinity prediction.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2022DLP0023/_p
Copier
@ARTICLE{e106-d_5_697,
author={Runze WANG, Zehua ZHANG, Yueqin ZHANG, Zhongyuan JIANG, Shilin SUN, Guixiang MA, },
journal={IEICE TRANSACTIONS on Information},
title={MolHF: Molecular Heterogeneous Attributes Fusion for Drug-Target Affinity Prediction on Heterogeneity},
year={2023},
volume={E106-D},
number={5},
pages={697-706},
abstract={Recent studies in protein structure prediction such as AlphaFold have enabled deep learning to achieve great attention on the Drug-Target Affinity (DTA) task. Most works are dedicated to embed single molecular property and homogeneous information, ignoring the diverse heterogeneous information gains that are contained in the molecules and interactions. Motivated by this, we propose an end-to-end deep learning framework to perform Molecular Heterogeneous features Fusion (MolHF) for DTA prediction on heterogeneity. To address the challenges that biochemical attributes locates in different heterogeneous spaces, we design a Molecular Heterogeneous Information Learning module with multi-strategy learning. Especially, Molecular Heterogeneous Attention Fusion module is present to obtain the gains of molecular heterogeneous features. With these, the diversity of molecular structure information for drugs can be extracted. Extensive experiments on two benchmark datasets show that our method outperforms the baselines in all four metrics. Ablation studies validate the effect of attentive fusion and multi-group of drug heterogeneous features. Visual presentations demonstrate the impact of protein embedding level and the model ability of fitting data. In summary, the diverse gains brought by heterogeneous information contribute to drug-target affinity prediction.},
keywords={},
doi={10.1587/transinf.2022DLP0023},
ISSN={1745-1361},
month={May},}
Copier
TY - JOUR
TI - MolHF: Molecular Heterogeneous Attributes Fusion for Drug-Target Affinity Prediction on Heterogeneity
T2 - IEICE TRANSACTIONS on Information
SP - 697
EP - 706
AU - Runze WANG
AU - Zehua ZHANG
AU - Yueqin ZHANG
AU - Zhongyuan JIANG
AU - Shilin SUN
AU - Guixiang MA
PY - 2023
DO - 10.1587/transinf.2022DLP0023
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E106-D
IS - 5
JA - IEICE TRANSACTIONS on Information
Y1 - May 2023
AB - Recent studies in protein structure prediction such as AlphaFold have enabled deep learning to achieve great attention on the Drug-Target Affinity (DTA) task. Most works are dedicated to embed single molecular property and homogeneous information, ignoring the diverse heterogeneous information gains that are contained in the molecules and interactions. Motivated by this, we propose an end-to-end deep learning framework to perform Molecular Heterogeneous features Fusion (MolHF) for DTA prediction on heterogeneity. To address the challenges that biochemical attributes locates in different heterogeneous spaces, we design a Molecular Heterogeneous Information Learning module with multi-strategy learning. Especially, Molecular Heterogeneous Attention Fusion module is present to obtain the gains of molecular heterogeneous features. With these, the diversity of molecular structure information for drugs can be extracted. Extensive experiments on two benchmark datasets show that our method outperforms the baselines in all four metrics. Ablation studies validate the effect of attentive fusion and multi-group of drug heterogeneous features. Visual presentations demonstrate the impact of protein embedding level and the model ability of fitting data. In summary, the diverse gains brought by heterogeneous information contribute to drug-target affinity prediction.
ER -