MFAE: Multilevel Feature Aggregation Enhanced Drug‐Target Affinity Prediction for Drug Repurposing Against Colorectal Cancer

to related objects

Colorectal cancer (CRC), a leading cause of cancer‐related deaths globally, demands innovative therapeutic strategies to improve patient outcomes. Drug repurposing, identifying new uses for existing drugs, provides a cost‐effective solution. To this end, this study constructs the first drug‐target affinity dataset specifically for two novel therapeutic targets for CRC, P2X4 and mTOR, and designs a new deep learning‐based multilevel feature aggregation enhanced (MFAE) model. The model implements hierarchical feature extraction and multilevel feature aggregation and enhancement to simulate complex drug‐target interactions. Evaluations using fivefold cross‐validation on the collected CRC dataset showcase MFAE's superior predictive accuracy. Fine‐tuning of the model on external experimental data further enhances its performance, with a concordance index of 0.930, a determination coefficient of 0.782, and a mean squared error of 0.191. Ablation studies further highlight the key role of the group‐wise feature enhancement mechanism and ensemble learning strategy in enhancing the model's performance. Virtual screening of the Food and Drug Administration‐approved drugs identifies Ponatinib and Talazoparib as potential repurposing candidates. Despite limitations in experimental validation, this study establishes an innovative computational framework designed for CRC drug discovery. Overall, this research offers a valuable perspective on leveraging computational approaches for precision oncology.

Location: Deutsche Nationalbibliothek Frankfurt am Main

Extent: Online-Ressource

Language: Englisch

Bibliographic citation: MFAE: Multilevel Feature Aggregation Enhanced Drug‐Target Affinity Prediction for Drug Repurposing Against Colorectal Cancer ; day:01 ; month:12 ; year:2023 ; extent:14
Advanced intelligent systems ; (01.12.2023) (gesamt 14)

Creator: Chen, Guanxing
He, Haohuai
Chen, Calvin Yu-Chian

DOI: 10.1002/aisy.202300546

URN: urn:nbn:de:101:1-2023120214060299431710

Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.

Last update: 15.08.2025, 7:22 AM CEST

Data provider

This object is provided by:
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.

Show original at data provider

Associated

Chen, Guanxing
He, Haohuai
Chen, Calvin Yu-Chian

Other Objects (12)

Explainable deep drug–target representations for binding affinity prediction

Drug–target affinity prediction with extended graph learning-convolutional networks

Aufsatzsammlung

Drug repurposing

DGDTA: dynamic graph attention network for predicting drug–target binding affinity

Sequence-based drug-target affinity prediction using weighted graph neural networks

A comparison of embedding aggregation strategies in drug–target interaction prediction

MTAF–DTA: multi-type attention fusion network for drug–target affinity prediction

Exploring drug-target interaction networks of illicit drugs

Drug-Online: an online platform for drug-target interaction, affinity, and binding sites identification using deep learning

DCGAN-DTA: Predicting drug-target binding affinity with deep convolutional generative adversarial networks

Drug repurposing in cardiology

Drug Repurposing in Oncology

Explainable deep drug–target representations for binding affinity prediction

Drug–target affinity prediction with extended graph learning-convolutional networks

Aufsatzsammlung

Drug repurposing

DGDTA: dynamic graph attention network for predicting drug–target binding affinity

Sequence-based drug-target affinity prediction using weighted graph neural networks

A comparison of embedding aggregation strategies in drug–target interaction prediction

MTAF–DTA: multi-type attention fusion network for drug–target affinity prediction

Exploring drug-target interaction networks of illicit drugs

Drug-Online: an online platform for drug-target interaction, affinity, and binding sites identification using deep learning

DCGAN-DTA: Predicting drug-target binding affinity with deep convolutional generative adversarial networks

Drug repurposing in cardiology

Drug Repurposing in Oncology

Explainable deep drug–target representations for binding affinity prediction

Drug–target affinity prediction with extended graph learning-convolutional networks

Aufsatzsammlung

Drug repurposing

DGDTA: dynamic graph attention network for predicting drug–target binding affinity

Sequence-based drug-target affinity prediction using weighted graph neural networks

A comparison of embedding aggregation strategies in drug–target interaction prediction

MTAF–DTA: multi-type attention fusion network for drug–target affinity prediction

Exploring drug-target interaction networks of illicit drugs

Drug-Online: an online platform for drug-target interaction, affinity, and binding sites identification using deep learning

DCGAN-DTA: Predicting drug-target binding affinity with deep convolutional generative adversarial networks

Drug repurposing in cardiology

Drug Repurposing in Oncology

Cultural heritage institutions wishing to register will find more information here.

Fields marked * need to be filled in.

Username*

Please enter your username

Email*

Please enter your email address

Please do not fill this field

First name

Last name

Password*

Please enter your password

Confirm password*

Please enter the same password

I have read the terms of use and the privacy policy for the collection of personal data and accept them. *

This field is required.

I would like to subscribe to the newsletter of the Deutsche Digitale Bibliothek. See newsletter subscription info.

Account created

Your "My DDB" account has been successfully created. Before you can log in to your account, you must click the confirmation link in the message we just sent to the email address you provided.

MFAE: Multilevel Feature Aggregation Enhanced Drug‐Target Affinity Prediction for Drug Repurposing Against Colorectal Cancer

Object Details

References and Relationships

Contributors, Places and Time

Further information

Data provider

Associated

Other Objects (12)

Explainable deep drug–target representations for binding affinity prediction

Drug–target affinity prediction with extended graph learning-convolutional networks

Drug repurposing

DGDTA: dynamic graph attention network for predicting drug–target binding affinity

Sequence-based drug-target affinity prediction using weighted graph neural networks

A comparison of embedding aggregation strategies in drug–target interaction prediction

MTAF–DTA: multi-type attention fusion network for drug–target affinity prediction

Exploring drug-target interaction networks of illicit drugs

Drug-Online: an online platform for drug-target interaction, affinity, and binding sites identification using deep learning

DCGAN-DTA: Predicting drug-target binding affinity with deep convolutional generative adversarial networks

Drug repurposing in cardiology

Drug Repurposing in Oncology

Explainable deep drug–target representations for binding affinity prediction

Drug–target affinity prediction with extended graph learning-convolutional networks

Drug repurposing

DGDTA: dynamic graph attention network for predicting drug–target binding affinity

Sequence-based drug-target affinity prediction using weighted graph neural networks

A comparison of embedding aggregation strategies in drug–target interaction prediction

MTAF–DTA: multi-type attention fusion network for drug–target affinity prediction

Exploring drug-target interaction networks of illicit drugs

Drug-Online: an online platform for drug-target interaction, affinity, and binding sites identification using deep learning

DCGAN-DTA: Predicting drug-target binding affinity with deep convolutional generative adversarial networks

Drug repurposing in cardiology

Drug Repurposing in Oncology

Explainable deep drug–target representations for binding affinity prediction

Drug–target affinity prediction with extended graph learning-convolutional networks

Drug repurposing

DGDTA: dynamic graph attention network for predicting drug–target binding affinity

Sequence-based drug-target affinity prediction using weighted graph neural networks

A comparison of embedding aggregation strategies in drug–target interaction prediction

MTAF–DTA: multi-type attention fusion network for drug–target affinity prediction

Exploring drug-target interaction networks of illicit drugs

Drug-Online: an online platform for drug-target interaction, affinity, and binding sites identification using deep learning

DCGAN-DTA: Predicting drug-target binding affinity with deep convolutional generative adversarial networks

Drug repurposing in cardiology

Drug Repurposing in Oncology

Related objects

Reset password