ReviewComputational and structural biotechnology journal2021
Structure-based protein-ligand interaction fingerprints for binding affinity prediction.
Review in Computational and structural biotechnology journal, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- EasyDock 1.3: An Automated Pipeline for Molecular Docking.Journal of chemical information and modeling · 2026Article
- Machine Learning Approaches for Compound-Target Interaction Prediction: A Review.Foods (Basel, Switzerland) · 2026Review
- Computer-aided discovery of CDK16 inhibitors: a docking-augmented machine learning regression modelling approach.Journal of computer-aided molecular design · 2026Article
- Molegro Data Modeller for Machine Learning.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Computational approaches to druggable site identification: Current status and future perspective.Acta pharmaceutica Sinica. B · 2026Review
- Inhibition of human N myristoyltransferase 1 as a strategy to suppress cancer progression driven by myristoylation.Scientific reports · 2025Article
- Predicting Protein-Ligand Binding Affinity Using Fusion Model of Spatial-Temporal Graph Neural Network and 3D Structure-Based Complex Graph.Interdisciplinary sciences, computational life sciences · 2025Article
- Scoring protein-ligand binding structures through learning atomic graphs with inter-molecular adjacency.PLoS computational biology · 2025Article
- GRADE and X-GRADE: Unveiling Novel Protein-Ligand Interaction Fingerprints Based on GRAIL Scores.Journal of chemical information and modeling · 2025Article
- Bridging Structure- and Ligand-Based Virtual Screening through Fragmented Interaction Fingerprint.ACS omega · 2024Article
- Binding affinity predictions with hybrid quantum-classical convolutional neural networks.Scientific reports · 2023Article
- Molecular Motifs in Vascular Morphogenesis: Vascular Endothelial Growth Factor A (VEGFA) as the Leading Promoter of Angiogenesis.International journal of molecular sciences · 2023Article
- Exploration of SARS-CoV-2 Mpro Noncovalent Natural Inhibitors Using Structure-Based Approaches.ACS omega · 2023Article
- Neural networks prediction of the protein-ligand binding affinity with circular fingerprints.Technology and health care : official journal of the European Society for Engineering and Medicine · 2023Article
- Chemoinformatics and artificial intelligence colloquium: progress and challenges in developing bioactive compounds.Journal of cheminformatics · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Binding affinity prediction (BAP) using protein-ligand complex structures is crucial to computer-aided drug design, but remains a challenging problem. To achieve efficient and accurate BAP, machine-learning scoring functions (SFs) based on a wide range of descriptors have been developed. Among those descriptors, protein-ligand interaction fingerprints (IFPs) are competitive due to their simple representations, elaborate profiles of key interactions and easy collaborations with machine-learning algorithms. In this paper, we have adopted a building-block-based taxonomy to review a broad range of IFP models, and compared representative IFP-based SFs in target-specific and generic scoring tasks. Atom-pair-counts-based and substructure-based IFPs show great potential in these tasks.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.