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A Bilingual Computational Workflow for Identifying Potential PLK1 Inhibitors in American Sign Language and English

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Integrated machine learning and molecular dynamics framework for predicting and elucidating ABCB1 allocrite

Jianjia Su^1,2, Yiyang Wu², Wei Xiong¹

¹School of Pharmacy, Shenzhen University Medical School, Shenzhen University, No. 1066 Xueyuan Avenue, Nanshan District, Shenzhen, Guangdong, 518055, China.

Briefings in Bioinformatics

|April 8, 2026

Summary

This summary is machine-generated.

This study reveals how ABCB1 (ATP-binding cassette sub-family B member 1) recognizes substrates and inhibitors, crucial for overcoming cancer drug resistance. Machine learning and biophysics identified key molecular features and binding mechanisms.

Keywords:

ABCB1 allocrite interactions machine learning molecular dynamics multidrug resistance

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Area of Science:

Biophysics
Computational Biology
Molecular Biology

Background:

ABCB1 (ATP-binding cassette sub-family B member 1) is a key transporter protein.
It mediates multidrug resistance in cancer by effluxing diverse substrates.
The precise recognition mechanisms of ABCB1 substrates and inhibitors remain poorly understood.

Purpose of the Study:

To develop an integrated framework combining biophysics and computational biology.
To predict ABCB1 substrate and inhibitor interactions and elucidate their mechanisms.
To facilitate the rational design of novel inhibitors to combat multidrug resistance.

Main Methods:

Curated hierarchical-confidence bioactivity datasets from multi-source assays.
Developed MolMM, a convolutional neural network using meta-learning and multi-task learning.
Employed SHapley Additive exPlanations (SHAP) for feature importance analysis.
Utilized coarse-grained umbrella sampling simulations for free energy landscape mapping.

Main Results:

MolMM achieved high predictive performance (AUC-ROC 83.33% for inhibitors, 81.26% for substrates).
SHAP analysis identified key polar and hydrophobic motifs distinguishing substrates from inhibitors.
Simulations proposed an amphiphilic substrate binding model and an inhibitory mechanism stabilizing transitional conformations.

Conclusions:

The synergy of machine learning and molecular dynamics provides mechanistic insights into ABCB1 polyspecificity.
Understanding these interactions is vital for developing strategies against multidrug resistance.
This framework aids in the rational design of ABCB1 inhibitors for cancer therapy.