ReviewBriefings in bioinformatics2022
Artificial intelligence in the prediction of protein-ligand interactions: recent advances and future directions.
Review in Briefings in bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 109 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
109 citing papers in PubMed.
- Insights into the Complexities of Pharmacotherapy Parameters in Artificial Intelligence Models for Drug Selection, Precision Personalised Medicine and Optimal Therapeutic Outcomes.Pharmaceutics · 2026Review
- DVNDTA: a dual virtual node based heterogeneous interaction model for accurate prediction of drug-target affinity.BMC bioinformatics · 2026Article
- Artificial intelligence driven protein design and sustainable nanomedicine for advanced theranostics.Bioactive materials · 2026Review
- Sequence-based drug-target binding site pre-training enables cryptic pocket detection and improves binding affinity and kinetics prediction.Journal of cheminformatics · 2026Article
- Sequence-based Drug-Target Binding Site Pretraining Enables Cryptic Pocket Detection and Improves Binding Affinity and Kinetics Prediction.bioRxiv : the preprint server for biology · 2026Article
- [A protein-ligand binding affinity prediction model integrating multi-scale interaction features].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2026Article
- LiBRe: A Ligand-Aware Sequence-Based Binding Residue Prediction Model for Virtual Screening.Journal of chemical information and modeling · 2026Article
- DeepMice: a novel protein-ligand molecular docking model based on multilevel mapping modules.Molecular diversity · 2026Article
- Could statistical potential models achieve comparable or better performance than deep learning models?Briefings in bioinformatics · 2026Article
- Review
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- Contrastive learning in both structure and function spaces improve drug-target interaction prediction.BMC bioinformatics · 2026Article
- Bioinformatics-Driven, Plant-Based Antibiotic Research Against Quorum Sensing and Biofilm Formation inBiomolecules · 2026Review
- ActivityFinder: Toward the Fully Automatic Integration of Structural and Binding Affinity Data.Journal of chemical information and modeling · 2026Article
- Structure-informed machine learning for drug discovery: a task-centric perspective.Briefings in bioinformatics · 2026Review
- Computational Innovations in Cancer Research and How Computing is Transforming Drug Discovery and Development: A Review.Mini reviews in medicinal chemistry · 2026Review
- Machine Learning to Predict CDK4 Inhibition.Methods in molecular biology (Clifton, N.J.) · 2026Article
- AI-driven pilot platforms and computational pharmaceutics: accelerating innovation in small molecule drug development under industry 4.0 and 5.0 paradigms.Frontiers in pharmacology · 2026Review
- Assessing the potential of deep learning for protein-ligand docking.Nature machine intelligence · 2026Article
49 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
New drug production, from target identification to marketing approval, takes over 12 years and can cost around $2.6 billion. Furthermore, the COVID-19 pandemic has unveiled the urgent need for more powerful computational methods for drug discovery. Here, we review the computational approaches to predicting protein-ligand interactions in the context of drug discovery, focusing on methods using artificial intelligence (AI). We begin with a brief introduction to proteins (targets), ligands (e.g. drugs) and their interactions for nonexperts. Next, we review databases that are commonly used in the domain of protein-ligand interactions. Finally, we survey and analyze the machine learning (ML) approaches implemented to predict protein-ligand binding sites, ligand-binding affinity and binding pose (conformation) including both classical ML algorithms and recent deep learning methods. After exploring the correlation between these three aspects of protein-ligand interaction, it has been proposed that they should be studied in unison. We anticipate that our review will aid exploration and development of more accurate ML-based prediction strategies for studying protein-ligand interactions.
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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.