ELM-APDPs: An Explainable Ensemble Learning Method for Accurate Prediction of Druggable Proteins

Identifying druggable proteins, which are capable of binding therapeutic compounds, remains a critical and resource-intensive challenge in drug discovery. To address this, we propose CEL-IDP (Comparison of Ensemble Learning M... | Find, read and cite all the research you need on Tech Science Press

Authors

Publication Details

Journal
Computer Modeling in Engineering & Sciences
Published
2025-01-01
DOI
https://doi.org/10.32604/cmes.2025.067412
Primary Topic
Machine Learning in Bioinformatics
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

ELM-APDPs: An Explainable Ensemble Learning Method for Accurate Prediction of Druggable Proteins

Dildar Hussain, Yeong Hyeon Gu, Mahboob-ur Rehman, Tariq Ahmad et al.
Computer Modeling in Engineering & Sciences
Machine Learning in Bioinformatics
article

ELM-APDPs: An Explainable Ensemble Learning Method for Accurate Prediction of Druggable Proteins

Dildar Hussain, Yeong Hyeon Gu, Mahboob-ur Rehman, Tariq Ahmad, Ali Ghulam, Qinghua Liu, Jawad Khan
article en

Abstract

Identifying druggable proteins, which are capable of binding therapeutic compounds, remains a critical and resource-intensive challenge in drug discovery. To address this, we propose CEL-IDP (Comparison of Ensemble Learning M... | Find, read and cite all the research you need on Tech Science Press

Computer Modeling in Engineering & SciencesVol. 145(1)
Openalex Percentile: Top 72%
Machine Learning in Bioinformatics
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.