{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T12:48:50Z","timestamp":1792759730574,"version":"4.5.0"},"reference-count":58,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2026,7,12]],"date-time":"2026-07-12T00:00:00Z","timestamp":1783814400000},"content-version":"vor","delay-in-days":11,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100031289","name":"Universidad CEU Cardenal Herrera","doi-asserted-by":"publisher","award":["INDI25\/17"],"award-info":[{"award-number":["INDI25\/17"]}],"id":[{"id":"10.13039\/100031289","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100031289","name":"Universidad CEU Cardenal Herrera","doi-asserted-by":"publisher","award":["GIR25\/14"],"award-info":[{"award-number":["GIR25\/14"]}],"id":[{"id":"10.13039\/100031289","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,7,3]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Artificial intelligence is now central to computational drug discovery, yet performance in core tasks\u2014drug\u2013target interaction (DTI) prediction, virtual screening (VS), and docking scoring\u2014is still limited by the multiscale geometric nature of molecular recognition and by evaluation pitfalls such as dataset bias and leakage. Topological deep learning (TDL) offers a complementary route to encode global and multiscale structure from ligands, binding pockets, surfaces, and protein\u2013ligand complexes via persistent homology and related constructions. This review provides a practical, task-driven synthesis of TDL methods for DTI\/VS\/docking scoring, with an emphasis on design choices that determine real-world utility: (i) data modality (ligand, pocket, or complex\/pose) under controllable uncertainty, (ii) topological objects and filtration families (distance\/alpha versus physicochemical or interaction-field filtrations), and (iii) vectorizations and integration patterns (persistent homology-as-features, hybrid geometric deep learning, and emerging end-to-end approaches). Distinct from prior surveys, we present a decision-oriented taxonomy and a benchmark-driven evaluation playbook that specifies minimum standards for splits (scaffold, temporal, and target-wise\/cluster), metrics (including early-recognition metrics for VS), baselines, and ablations to isolate the topological contribution. To support reproducibility, we provide a reporting checklist and curated summary tables (methods matrix and benchmark recommendations) that map tasks to recommended protocols and common failure modes.<\/jats:p>","DOI":"10.1093\/bib\/bbag370","type":"journal-article","created":{"date-parts":[[2026,7,12]],"date-time":"2026-07-12T22:44:04Z","timestamp":1783896244000},"source":"Crossref","is-referenced-by-count":1,"title":["Topological deep learning for drug\u2013target interaction, virtual screening, and docking scoring: a practical, benchmark-driven review"],"prefix":"10.1093","volume":"27","author":[{"given":"Beatriz","family":"Suay-Garc\u00eda","sequence":"first","affiliation":[{"name":"Departamento de Matem\u00e1ticas, F\u00edsica y Ciencias Tecnol\u00f3gicas, Universidad Cardenal Herrera-CEU, CEU Universities , C\/ Luis Vives, n\u00ba 2 (46115) en Alfara del Patriarca, Valencia ,","place":["Spain"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Antonio","family":"Falc\u00f3","sequence":"additional","affiliation":[{"name":"Departamento de Matem\u00e1ticas, F\u00edsica y Ciencias Tecnol\u00f3gicas, Universidad Cardenal Herrera-CEU, CEU Universities , C\/ Luis Vives, n\u00ba 2 (46115) en Alfara del Patriarca, Valencia ,","place":["Spain"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2026,7,12]]},"reference":[{"key":"2026071210233777800_ref1","doi-asserted-by":"publisher","first-page":"e1005690","DOI":"10.1371\/journal.pcbi.1005690","article-title":"TopologyNet: topology based deep convolutional and multi-task neural networks for biomolecular property predictions","volume":"13","author":"Cang","year":"2017","journal-title":"PLOS Comput Biol"},{"key":"2026071210233777800_ref2","doi-asserted-by":"publisher","first-page":"681108","DOI":"10.3389\/frai.2021.681108","article-title":"A survey of topological machine learning methods","volume":"4","author":"Hensel","year":"2021","journal-title":"Front Artif Intell"},{"key":"2026071210233777800_ref3","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1007\/s10462-023-10593-3","article-title":"Topological deep learning: a review on new emerging paradigm","volume":"57","author":"Zia","year":"2024","journal-title":"Artif Intell Rev"},{"key":"2026071210233777800_ref4","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1090\/S0273-0979-09-01249-X","article-title":"Topology and data","volume":"46","author":"Carlsson","year":"2009","journal-title":"Bull Am Math Soc"},{"key":"2026071210233777800_ref5","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1007\/s00454-004-1146-y","article-title":"Computing persistent homology","volume":"33","author":"Zomorodian","year":"2005","journal-title":"Discrete Comput Geom"},{"key":"2026071210233777800_ref6","volume-title":"Elements of Algebraic Topology","author":"Munkres","year":"1984"},{"key":"2026071210233777800_ref7","doi-asserted-by":"publisher","first-page":"3230","DOI":"10.1038\/s41467-020-17035-5","article-title":"Representation of molecular structures with persistent homology for machine learning applications in chemistry","volume":"11","author":"Townsend","year":"2020","journal-title":"Nat Commun"},{"key":"2026071210233777800_ref8","doi-asserted-by":"publisher","first-page":"e2914","DOI":"10.1002\/cnm.2914","article-title":"Integration of element specific persistent homology and machine learning for protein\u2013ligand binding affinity prediction","volume":"34","author":"Cang","year":"2018","journal-title":"Int J Numer Method Biomed Eng"},{"key":"2026071210233777800_ref9","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1007\/s10822-019-00237-5","article-title":"MathDL: mathematical deep learning for D3R grand challenge 4","volume":"34","author":"Nguyen","year":"2020","journal-title":"J Comput Aided Mol Des"},{"key":"2026071210233777800_ref10","doi-asserted-by":"publisher","first-page":"i133","DOI":"10.1093\/bioinformatics\/btaf183","article-title":"Top-DTI: integrating topological deep learning and large language models for drug\u2013target interaction prediction","volume":"41","author":"Talo","year":"2025","journal-title":"Bioinformatics"},{"key":"2026071210233777800_ref11","doi-asserted-by":"publisher","first-page":"106946","DOI":"10.1016\/j.compbiomed.2023.106946","article-title":"DrugormerDTI: drug Graphormer for drug\u2013target interaction prediction","volume":"161","author":"Hu","year":"2023","journal-title":"Comput Biol Med"},{"key":"2026071210233777800_ref12","doi-asserted-by":"publisher","first-page":"799","DOI":"10.1038\/s42256-024-00855-1","article-title":"Multiscale topology-enabled structure-to-sequence transformer for protein\u2013ligand interaction predictions","volume":"6","author":"Chen","year":"2024","journal-title":"Nat Mach Intell"},{"key":"2026071210233777800_ref13","doi-asserted-by":"publisher","first-page":"6732","DOI":"10.1021\/acs.jcim.5c00940","article-title":"CAML: commutative algebra machine learning\u2014a case study on protein\u2013ligand binding affinity prediction","volume":"65","author":"Feng","year":"2025","journal-title":"J Chem Inf Model"},{"key":"2026071210233777800_ref14","doi-asserted-by":"publisher","first-page":"e2408431121","DOI":"10.1073\/pnas.2408431121","article-title":"Knot data analysis using multiscale Gauss link integral","volume":"121","author":"Shen","year":"2024","journal-title":"Proc Natl Acad Sci"},{"key":"2026071210233777800_ref15","doi-asserted-by":"publisher","first-page":"14069","DOI":"10.1109\/TPAMI.2023.3308391","article-title":"A survey of vectorization methods in topological data analysis","volume":"45","author":"Ali","year":"2023","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2026071210233777800_ref16","doi-asserted-by":"publisher","first-page":"465","DOI":"10.1080\/17460441.2025.2477625","article-title":"Topological approaches to AI-driven drug discovery","volume":"20","author":"Suay-Garc\u00eda","year":"2025","journal-title":"Expert Opin Drug Discov"},{"key":"2026071210233777800_ref17","doi-asserted-by":"crossref","volume-title":"Persistent Homology for Virtual Screening","author":"Keller","DOI":"10.26434\/chemrxiv.6969260.v3"},{"key":"2026071210233777800_ref18","article-title":"ToDD: topological representation learning for molecular property prediction","volume-title":"Advances in Neural Information Processing Systems","author":"Demir","year":"2022"},{"key":"2026071210233777800_ref19","doi-asserted-by":"publisher","first-page":"e1005929","DOI":"10.1371\/journal.pcbi.1005929","article-title":"Representability of algebraic topology for biomolecules in machine learning based scoring and virtual screening","volume":"14","author":"Cang","year":"2018","journal-title":"PLoS Comput Biol"},{"key":"2026071210233777800_ref20","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1007\/S10822-018-0146-6","article-title":"Mathematical deep learning for pose and binding affinity prediction and ranking in D3R Grand challenges","volume":"33","author":"Nguyen","year":"2019","journal-title":"J Comput Aided Mol Des"},{"key":"2026071210233777800_ref21","doi-asserted-by":"publisher","first-page":"e1013216","DOI":"10.1371\/journal.pcbi.1013216","article-title":"Predicting affinity through homology (PATH): interpretable binding affinity prediction with persistent homology","volume":"21","author":"Long","year":"2025","journal-title":"PLoS Comput Biol"},{"key":"2026071210233777800_ref22","doi-asserted-by":"publisher","first-page":"bbaa411","DOI":"10.1093\/bib\/bbaa411","article-title":"Hypergraph-based persistent cohomology (HPC) for molecular representations in drug design","volume":"22","author":"Liu","year":"2021","journal-title":"Brief Bioinform"},{"key":"2026071210233777800_ref23","doi-asserted-by":"publisher","first-page":"bbab127","DOI":"10.1093\/bib\/bbab127","article-title":"Persistent spectral hypergraph based machine learning (PSH-ML) for protein-ligand binding affinity prediction","volume":"22","author":"Liu","year":"2021","journal-title":"Brief Bioinform"},{"key":"2026071210233777800_ref24","doi-asserted-by":"publisher","first-page":"eabc5329","DOI":"10.1126\/sciadv.abc5329","article-title":"Persistent spectral\u2013based machine learning (PerSpect ML) for protein-ligand binding affinity prediction","volume":"7","author":"Meng","year":"2021","journal-title":"Sci Adv"},{"key":"2026071210233777800_ref25","doi-asserted-by":"publisher","first-page":"e1009943","DOI":"10.1371\/journal.pcbi.1009943","article-title":"Dowker complex based machine learning (DCML) models for protein-ligand binding affinity prediction","volume":"18","author":"Liu","year":"2022","journal-title":"PLoS Comput Biol"},{"key":"2026071210233777800_ref26","doi-asserted-by":"publisher","first-page":"253","DOI":"10.1142\/s2737416524500613","article-title":"Mayer-homology learning prediction of protein-ligand binding affinities","volume":"24","author":"Feng","year":"2025","journal-title":"J Comput Biophys Chem"},{"key":"2026071210233777800_ref27","article-title":"Persistent Directed Flag Laplacian (PDFL)-based machine learning for protein-ligand binding affinity prediction","author":"Zia","year":"2024"},{"key":"2026071210233777800_ref28","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1021\/ci00057a005","article-title":"SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules","volume":"28","author":"Weininger","year":"1988","journal-title":"J Chem Inf Comput Sci"},{"key":"2026071210233777800_ref29","doi-asserted-by":"publisher","first-page":"742","DOI":"10.1021\/ci100050t","article-title":"Extended-connectivity fingerprints","volume":"50","author":"Rogers","year":"2010","journal-title":"J Chem Inf Model"},{"key":"2026071210233777800_ref30","article-title":"Neural message passing for quantum chemistry","author":"Gilmer","year":"2017"},{"key":"2026071210233777800_ref31","volume-title":"Computational Topology: An Introduction","author":"Edelsbrunner","year":"2010"},{"key":"2026071210233777800_ref32","first-page":"1","article-title":"Persistence images: a stable vector representation of persistent homology","volume":"18","author":"Adams","year":"2017","journal-title":"J Mach Learn Res"},{"key":"2026071210233777800_ref33","first-page":"77","article-title":"Statistical topological data analysis using persistence landscapes","volume":"16","author":"Bubenik","year":"2015","journal-title":"J Mach Learn Res"},{"key":"2026071210233777800_ref34","doi-asserted-by":"publisher","first-page":"4741","DOI":"10.1109\/CVPR.2015.7299106","article-title":"A stable multi-scale kernel for topological machine learning","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","author":"Reininghaus","year":"2015"},{"key":"2026071210233777800_ref35","first-page":"1634","article-title":"Deep learning with topological signatures","volume-title":"Advances in Neural Information Processing Systems","author":"Hofer","year":"2017"},{"key":"2026071210233777800_ref36","first-page":"7045","article-title":"Topological autoencoders","volume-title":"Proceedings of the 37th International Conference on Machine Learning","author":"Moor","year":"2020"},{"key":"2026071210233777800_ref37","doi-asserted-by":"publisher","first-page":"241722","DOI":"10.1063\/1.5019779","article-title":"SchNet\u2014a deep learning architecture for molecules and materials","volume":"148","author":"Sch\u00fctt","year":"2018","journal-title":"J Chem Phys"},{"key":"2026071210233777800_ref38","first-page":"9377","article-title":"Equivariant message passing for the prediction of tensorial properties and molecular spectra","volume-title":"Proceedings of the 38th International Conference on Machine Learning (ICML)","author":"Sch\u00fctt","year":"2021"},{"key":"2026071210233777800_ref39","article-title":"Directional message passing for molecular graphs","volume-title":"International Conference on Learning Representations (ICLR)","author":"Gasteiger","year":"2020"},{"key":"2026071210233777800_ref40","doi-asserted-by":"publisher","first-page":"2453","DOI":"10.1038\/s41467-022-29939-5","article-title":"E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials","volume":"13","author":"Batzner","year":"2022","journal-title":"Nat Commun"},{"key":"2026071210233777800_ref41","doi-asserted-by":"crossref","DOI":"10.52202\/068431-0830","article-title":"MACE: higher order equivariant message passing neural networks for fast and accurate force fields","volume-title":"Advances in Neural Information Processing Systems (NeurIPS)","author":"Batatia","year":"2022"},{"key":"2026071210233777800_ref42","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1007\/s00454-006-1276-5","article-title":"Stability of persistence diagrams","volume":"37","author":"Cohen-Steiner","year":"2007","journal-title":"Discret Comput Geom"},{"key":"2026071210233777800_ref43","doi-asserted-by":"publisher","first-page":"D1633","DOI":"10.1093\/nar\/gkae1075","article-title":"BindingDB in 2024: a FAIR knowledgebase of protein\u2013small molecule binding data","volume":"53","author":"Liu","year":"2025","journal-title":"Nucleic Acids Res"},{"key":"2026071210233777800_ref44","doi-asserted-by":"publisher","first-page":"D1180","DOI":"10.1093\/nar\/gkad1004","article-title":"The ChEMBL database in 2023: a drug discovery platform spanning multiple bioactivity data types and time periods","volume":"52","author":"Zdrazil","year":"2024","journal-title":"Nucleic Acids Res"},{"key":"2026071210233777800_ref45","doi-asserted-by":"publisher","first-page":"D1265","DOI":"10.1093\/nar\/gkad976","article-title":"DrugBank 6.0: the DrugBank knowledgebase for 2024","volume":"52","author":"Knox","year":"2024","journal-title":"Nucleic Acids Res"},{"key":"2026071210233777800_ref46","doi-asserted-by":"publisher","first-page":"1046","DOI":"10.1038\/nbt.1990","article-title":"Comprehensive analysis of kinase inhibitor selectivity","volume":"29","author":"Davis","year":"2011","journal-title":"Nat Biotechnol"},{"key":"2026071210233777800_ref47","doi-asserted-by":"publisher","first-page":"735","DOI":"10.1021\/ci400709d","article-title":"Making sense of large-scale kinase inhibitor bioactivity data sets: a comparative and integrative analysis","volume":"54","author":"Tang","year":"2014","journal-title":"J Chem Inf Model"},{"key":"2026071210233777800_ref48","doi-asserted-by":"publisher","first-page":"i821","DOI":"10.1093\/bioinformatics\/bty593","article-title":"DeepDTA: deep drug\u2013target binding affinity prediction","volume":"34","author":"\u00d6zt\u00fcrk","year":"2018","journal-title":"Bioinformatics"},{"key":"2026071210233777800_ref49","article-title":"Therapeutics data commons: machine learning datasets and tasks for drug discovery and development","author":"Huang","year":"2021"},{"key":"2026071210233777800_ref50","doi-asserted-by":"publisher","first-page":"6582","DOI":"10.1021\/jm300687e","article-title":"Directory of useful decoys, enhanced (DUD-E): better ligands and decoys for better benchmarking","volume":"55","author":"Mysinger","year":"2012","journal-title":"J Med Chem"},{"key":"2026071210233777800_ref51","doi-asserted-by":"publisher","first-page":"1447","DOI":"10.1021\/ci400115b","article-title":"Evaluation and optimization of virtual screening workflows with DEKOIS 2.0\u2014a public library of challenging docking benchmark sets","volume":"53","author":"Bauer","year":"2013","journal-title":"J Chem Inf Model"},{"key":"2026071210233777800_ref52","doi-asserted-by":"publisher","first-page":"4263","DOI":"10.1021\/acs.jcim.0c00155","article-title":"LIT-PCBA: an unbiased dataset for machine learning and virtual screening","volume":"60","author":"Tran-Nguyen","year":"2020","journal-title":"J Chem Inf Model"},{"key":"2026071210233777800_ref53","article-title":"Data leakage and redundancy in LIT-PCBA: implications for virtual screening benchmarks","author":"Huang","year":"2025"},{"key":"2026071210233777800_ref54","doi-asserted-by":"publisher","first-page":"488","DOI":"10.1021\/ci600426e","article-title":"Evaluating virtual screening methods: good and bad metrics for the early recognition problem","volume":"47","author":"Truchon","year":"2007","journal-title":"J Chem Inf Model"},{"key":"2026071210233777800_ref55","doi-asserted-by":"publisher","first-page":"4111","DOI":"10.1021\/jm030580l","article-title":"The PDBbind database: collection of binding affinities for protein\u2013ligand complexes with known three-dimensional structures","volume":"48","author":"Wang","year":"2005","journal-title":"J Med Chem"},{"key":"2026071210233777800_ref56","doi-asserted-by":"publisher","first-page":"895","DOI":"10.1021\/acs.jcim.8b00545","article-title":"Comparative assessment of scoring functions: CASF-2016 update and evaluation based on PDBbind","volume":"59","author":"Su","year":"2019","journal-title":"J Chem Inf Model"},{"key":"2026071210233777800_ref57","doi-asserted-by":"publisher","first-page":"2887","DOI":"10.1021\/jm9602928","article-title":"The properties of known drugs. 1. Molecular frameworks","volume":"39","author":"Bemis","year":"1996","journal-title":"J Med Chem"},{"key":"2026071210233777800_ref58","doi-asserted-by":"publisher","first-page":"837","DOI":"10.2307\/2531595","article-title":"Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach","volume":"44","author":"DeLong","year":"1988","journal-title":"Biometrics"}],"container-title":["Briefings in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/27\/4\/bbag370\/68761211\/bbag370.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/27\/4\/bbag370\/68761211\/bbag370.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,12]],"date-time":"2026-07-12T22:44:08Z","timestamp":1783896248000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bib\/article\/doi\/10.1093\/bib\/bbag370\/8732820"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7]]},"references-count":58,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2026,7,3]]}},"URL":"https:\/\/doi.org\/10.1093\/bib\/bbag370","relation":{},"ISSN":["1467-5463","1477-4054"],"issn-type":[{"value":"1467-5463","type":"print"},{"value":"1477-4054","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2026,7]]},"published":{"date-parts":[[2026,7]]},"article-number":"bbag370"}}