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Protein Target Prediction and Validation of Small Molecule Compound
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Topological deep learning for drug-target interaction, virtual screening, and docking scoring: a practical,

Beatriz Suay-García1, Antonio Falcó1

  • 1Departamento de Matemáticas, Física y Ciencias Tecnológicas, Universidad Cardenal Herrera-CEU, CEU Universities, C/ Luis Vives, nº 2 (46115) en Alfara del Patriarca, Valencia, Spain.

Briefings in Bioinformatics
|July 12, 2026
PubMed
Summary

Topological deep learning (TDL) enhances computational drug discovery by capturing molecular structure. This review synthesizes TDL methods for drug-target interaction prediction, virtual screening, and docking scoring, offering practical guidance.

Keywords:
benchmarksdocking scoringdrug–target interactionpersistent homologytopological deep learningvirtual screening

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Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis
08:49

Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis

Published on: June 20, 2025

Area of Science:

  • Computational chemistry
  • Machine learning
  • Structural biology

Background:

  • Artificial intelligence (AI) is crucial for computational drug discovery, but faces limitations in predicting drug-target interactions (DTI), virtual screening (VS), and docking scoring due to molecular complexity and evaluation issues.
  • Existing methods struggle with the multiscale geometric nature of molecular recognition and are susceptible to dataset biases and leakage.

Purpose of the Study:

  • To provide a practical, task-driven synthesis of Topological Deep Learning (TDL) methods for DTI prediction, VS, and docking scoring.
  • To offer a decision-oriented taxonomy and evaluation playbook for TDL in drug discovery, emphasizing real-world utility and reproducibility.
  • To address the limitations of current AI approaches by leveraging TDL's ability to encode global and multiscale molecular structure.

Main Methods:

  • Review and synthesis of TDL methods, including persistent homology, for analyzing molecular data (ligands, pockets, complexes).
  • Exploration of design choices: data modality, topological objects/filtrations, and vectorization/integration patterns.
  • Development of a benchmark-driven evaluation playbook with standards for data splits, metrics, baselines, and ablations.

Main Results:

  • A decision-oriented taxonomy categorizing TDL approaches for drug discovery tasks.
  • An evaluation playbook specifying minimum standards for benchmarking TDL methods, including reproducibility measures.
  • Curated summary tables mapping tasks to recommended protocols and common failure modes.

Conclusions:

  • TDL offers a powerful complementary approach to geometric deep learning for computational drug discovery, improving the encoding of molecular structure.
  • Standardized evaluation protocols and reporting are essential for assessing and advancing TDL methods in drug discovery.
  • This review provides a practical framework for researchers to effectively apply and evaluate TDL in DTI prediction, VS, and docking scoring.