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".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
AlphaFold와 같은 단백질 구조 예측에 대한 최근 연구를 통해 DTA(Drug-Target Affinity) 작업에 대한 깊은 관심을 얻을 수 있는 딥 러닝이 가능해졌습니다. 대부분의 작업은 분자 및 상호 작용에 포함된 다양한 이종 정보 이득을 무시하고 단일 분자 속성 및 동종 정보를 포함하는 데 전념합니다. 이에 동기를 부여하여 이질성에 대한 DTA 예측을 위한 Molecular Heterogeneous features Fusion(MolHF)을 수행하기 위한 end-to-end 딥러닝 프레임워크를 제안합니다. 생화학적 속성이 서로 다른 이질적인 공간에 있는 문제를 해결하기 위해 다중 전략 학습을 통해 분자 이질적 정보 학습 모듈을 설계합니다. 특히, Molecular Heterogeneous Attention Fusion 모듈은 분자 이질적 특징의 이득을 얻기 위해 존재합니다. 이를 통해 약물에 대한 다양한 분자 구조 정보를 추출할 수 있습니다. 두 개의 벤치마크 데이터 세트에 대한 광범위한 실험은 우리의 방법이 네 가지 메트릭 모두에서 기준선을 능가한다는 것을 보여줍니다. 절제 연구는 세심한 융합 및 다중 그룹의 약물 이질적 특징의 효과를 검증합니다. 시각적 프레젠테이션은 단백질 임베딩 수준의 영향과 피팅 데이터의 모델 능력을 보여줍니다. 요약하면, 이질적인 정보가 가져오는 다양한 이득은 약물-표적 친화성 예측에 기여합니다.
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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부
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
부
@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},}
부
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 -