ArticleBMC bioinformatics2021
Quantitative prediction model for affinity of drug-target interactions based on molecular vibrations and overall system of ligand-receptor.
Article in BMC bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- GSF-DTA: An Innovative Graph-Sequence Fusion Framework for Drug-Target Affinity Prediction.Interdisciplinary sciences, computational life sciences · 2026Article
- MFDR-DTI: Multisource Feature Decoupling and Dual-Level Reorganization Fusion for Accurate Drug-Target Interaction Prediction.ACS omega · 2026Article
- An interpretable geometric graph neural network for enhancing the generalizability of drug-target interaction prediction.BMC biology · 2025Article
- Inductive-Associative Meta-learning Pipeline with Human Cognitive Patterns for Unseen Drug-Target Interaction Prediction.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Prediction of drug-target interactions based on substructure subsequences and cross-public attention mechanism.PloS one · 2025Article
- CAT-DTI: cross-attention and Transformer network with domain adaptation for drug-target interaction prediction.BMC bioinformatics · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
backgroundThe study of drug-target interactions (DTIs) affinity plays an important role in safety assessment and pharmacology. Currently, quantitative structure-activity relationship (QSAR) and molecular docking (MD) are most common methods in research of DTIs affinity. However, they often built for a specific target or several targets, and most QSAR and MD methods were based either on structure of drug molecules or on structure of receptors with low accuracy and small scope of application. How to construct quantitative prediction models with high accuracy and wide applicability remains a challenge. To this end, this paper screened molecular descriptors based on molecular vibrations and took molecule-target as a whole system to construct prediction models with high accuracy-wide applicability based on dissociation constant (Kd) and concentration for 50% of maximal effect (EC50), and to provide reference for quantifying affinity of DTIs.
resultsAfter comprehensive comparison, the results showed that RF models are optimal models to analyze and predict DTIs affinity with coefficients of determination (R
conclusionThrough screening molecular descriptors based on molecular vibrations and taking molecule-target as whole system, we obtained optimal models based on RF with more accurate-widely applicable, which indicated that selection of molecular descriptors associated with molecular vibrations and the use of molecular-target as whole system are reliable methods for improving performance of models. It can provide reference for quantifying affinity of DTIs.
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