{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T21:32:24Z","timestamp":1757626344710,"version":"3.44.0"},"reference-count":64,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,8,29]],"date-time":"2025-08-29T00:00:00Z","timestamp":1756425600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,8,29]],"date-time":"2025-08-29T00:00:00Z","timestamp":1756425600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["24CX04029A"],"award-info":[{"award-number":["24CX04029A"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62272479","62372469","62202498"],"award-info":[{"award-number":["62272479","62372469","62202498"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2021YFA1000103","2021YFA1000102"],"award-info":[{"award-number":["2021YFA1000103","2021YFA1000102"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"publisher","award":["ZR2021QF023"],"award-info":[{"award-number":["ZR2021QF023"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Cheminform"],"DOI":"10.1186\/s13321-025-01077-2","type":"journal-article","created":{"date-parts":[[2025,8,29]],"date-time":"2025-08-29T09:38:25Z","timestamp":1756460305000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Alphappimi: a comprehensive deep learning framework for predicting PPI-modulator interactions"],"prefix":"10.1186","volume":"17","author":[{"given":"Dayan","family":"Liu","sequence":"first","affiliation":[]},{"given":"Tao","family":"Song","sequence":"additional","affiliation":[]},{"given":"Shuang","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Xue","family":"Li","sequence":"additional","affiliation":[]},{"given":"Peifu","family":"Han","sequence":"additional","affiliation":[]},{"given":"Jianmin","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Shudong","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,8,29]]},"reference":[{"issue":"7172","key":"1077_CR1","doi-asserted-by":"publisher","first-page":"1001","DOI":"10.1038\/nature06526","volume":"450","author":"JA Wells","year":"2007","unstructured":"Wells JA, McClendon CL (2007) Reaching for high-hanging fruit in drug discovery at protein-protein interfaces. Nature 450(7172):1001\u20131009","journal-title":"Nature"},{"issue":"6","key":"1077_CR2","doi-asserted-by":"publisher","first-page":"957","DOI":"10.1016\/j.cell.2005.08.029","volume":"122","author":"U Stelzl","year":"2005","unstructured":"Stelzl U, Worm U, Lalowski M, Haenig C, Brembeck FH, Goehler H, Stroedicke M, Zenkner M, Schoenherr A, Koeppen S et al (2005) A human protein-protein interaction network: a resource for annotating the proteome. Cell 122(6):957\u2013968","journal-title":"Cell"},{"issue":"7062","key":"1077_CR3","doi-asserted-by":"publisher","first-page":"1173","DOI":"10.1038\/nature04209","volume":"437","author":"J-F Rual","year":"2005","unstructured":"Rual J-F, Venkatesan K, Hao T, Hirozane-Kishikawa T, Dricot A, Li N, Berriz GF, Gibbons FD, Dreze M, Ayivi-Guedehoussou N et al (2005) Towards a proteome-scale map of the human protein-protein interaction network. Nature 437(7062):1173\u20131178","journal-title":"Nature"},{"issue":"1","key":"1077_CR4","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1002\/mas.21574","volume":"38","author":"K Titeca","year":"2019","unstructured":"Titeca K, Lemmens I, Tavernier J, Eyckerman S (2019) Discovering cellular protein-protein interactions: technological strategies and opportunities. Mass Spectrom Rev 38(1):79\u2013111","journal-title":"Mass Spectrom Rev"},{"issue":"5659","key":"1077_CR5","doi-asserted-by":"publisher","first-page":"844","DOI":"10.1126\/science.1092472","volume":"303","author":"LT Vassilev","year":"2004","unstructured":"Vassilev LT, Vu BT, Graves B, Carvajal D, Podlaski F, Filipovic Z, Kong N, Kammlott U, Lukacs C, Klein C et al (2004) In vivo activation of the p53 pathway by small-molecule antagonists of mdm2. Sci 303(5659):844\u2013848","journal-title":"Sci"},{"issue":"7","key":"1077_CR6","doi-asserted-by":"publisher","first-page":"393","DOI":"10.1016\/j.tips.2013.04.007","volume":"34","author":"AA Ivanov","year":"2013","unstructured":"Ivanov AA, Khuri FR, Fu H (2013) Targeting protein-protein interactions as an anticancer strategy. Trends Pharmacol Sci 34(7):393\u2013400","journal-title":"Trends Pharmacol Sci"},{"issue":"4","key":"1077_CR7","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1038\/nrd.2016.253","volume":"16","author":"A Ashkenazi","year":"2017","unstructured":"Ashkenazi A, Fairbrother WJ, Leverson JD, Souers AJ (2017) From basic apoptosis discoveries to advanced selective bcl-2 family inhibitors. Nat Rev Drug Discov 16(4):273\u2013284","journal-title":"Nat Rev Drug Discov"},{"key":"1077_CR8","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1016\/j.ymeth.2017.08.006","volume":"131","author":"W-H Shin","year":"2017","unstructured":"Shin W-H, Christoffer CW, Kihara D (2017) In silico structure-based approaches to discover protein-protein interaction-targeting drugs. Methods 131:22\u201332","journal-title":"Methods"},{"issue":"4","key":"1077_CR9","doi-asserted-by":"publisher","first-page":"248","DOI":"10.1038\/nrc3690","volume":"14","author":"TL Nero","year":"2014","unstructured":"Nero TL, Morton CJ, Holien JK, Wielens J, Parker MW (2014) Oncogenic protein interfaces: small molecules, big challenges. Nat Rev Cancer 14(4):248\u2013262","journal-title":"Nat Rev Cancer"},{"issue":"8","key":"1077_CR10","doi-asserted-by":"publisher","first-page":"533","DOI":"10.1038\/nrd.2016.29","volume":"15","author":"DE Scott","year":"2016","unstructured":"Scott DE, Bayly AR, Abell C, Skidmore J (2016) Small molecules, big targets: drug discovery faces the protein-protein interaction challenge. Nat Rev Drug Discov 15(8):533\u2013550","journal-title":"Nat Rev Drug Discov"},{"issue":"3","key":"1077_CR11","doi-asserted-by":"publisher","first-page":"605","DOI":"10.1016\/j.drudis.2018.01.010","volume":"23","author":"S Mignani","year":"2018","unstructured":"Mignani S, Rodrigues J, Tomas H, Jalal R, Singh PP, Majoral J-P, Vishwakarma RA (2018) Present drug-likeness filters in medicinal chemistry during the hit and lead optimization process: how far can they be simplified? Drug Discov Today 23(3):605\u2013615","journal-title":"Drug Discov Today"},{"issue":"1","key":"1077_CR12","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/S0169-409X(96)00423-1","volume":"23","author":"C Lipinski","year":"1997","unstructured":"Lipinski C, Lombardo F, Dominy B, Feeney P (1997) In vitro models for selection of development candidatesexperimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev 23(1):3\u201325","journal-title":"Adv Drug Deliv Rev"},{"issue":"4","key":"1077_CR13","doi-asserted-by":"publisher","first-page":"337","DOI":"10.1016\/j.ddtec.2004.11.007","volume":"1","author":"CA Lipinski","year":"2004","unstructured":"Lipinski CA (2004) Lead-and drug-like compounds: the rule-of-five revolution. Drug Discov Today Technol 1(4):337\u2013341","journal-title":"Drug Discov Today Technol"},{"issue":"4","key":"1077_CR14","doi-asserted-by":"publisher","first-page":"475","DOI":"10.1016\/j.cbpa.2011.05.024","volume":"15","author":"X Morelli","year":"2011","unstructured":"Morelli X, Bourgeas R, Roche P (2011) Chemical and structural lessons from recent successes in protein-protein interaction inhibition (2p2i). Curr Opin Chem Biol 15(4):475\u2013481","journal-title":"Curr Opin Chem Biol"},{"issue":"2","key":"1077_CR15","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1038\/nchem.1243","volume":"4","author":"GR Bickerton","year":"2012","unstructured":"Bickerton GR, Paolini GV, Besnard J, Muresan S, Hopkins AL (2012) Quantifying the chemical beauty of drugs. Nat Chem 4(2):90\u201398","journal-title":"Nat Chem"},{"key":"1077_CR16","doi-asserted-by":"crossref","unstructured":"Kosugi T, Ohue M (2021) Quantitative estimate of protein-protein interaction targeting drug-likeness. In: 2021 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), pp. 1\u20138 . IEEE","DOI":"10.1109\/CIBCB49929.2021.9562931"},{"key":"1077_CR17","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1016\/j.ymeth.2023.01.004","volume":"210","author":"J Wang","year":"2023","unstructured":"Wang J, Mao J, Wang M, Le X, Wang Y (2023) Explore drug-like space with deep generative models. Methods 210:52\u201359","journal-title":"Methods"},{"issue":"9","key":"1077_CR18","doi-asserted-by":"publisher","first-page":"925","DOI":"10.1080\/17460441.2017.1346608","volume":"12","author":"SA Andrei","year":"2017","unstructured":"Andrei SA, Sijbesma E, Hann M, Davis J, O\u2019Mahony G, Perry MW, Karawajczyk A, Eickhoff J, Brunsveld L, Doveston RG et al (2017) Stabilization of protein-protein interactions in drug discovery. Expert Opin Drug Discov 12(9):925\u2013940","journal-title":"Expert Opin Drug Discov"},{"issue":"2","key":"1077_CR19","doi-asserted-by":"publisher","first-page":"184","DOI":"10.1038\/s41592-019-0666-6","volume":"17","author":"P Gainza","year":"2020","unstructured":"Gainza P, Sverrisson F, Monti F, Rodol\u00e0 E, Boscaini D, Bronstein MM, Correia BE (2020) Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learning. Nat Methods 17(2):184\u2013192","journal-title":"Nat Methods"},{"issue":"7743","key":"1077_CR20","doi-asserted-by":"publisher","first-page":"224","DOI":"10.1038\/s41586-019-0917-9","volume":"566","author":"J Lyu","year":"2019","unstructured":"Lyu J, Wang S, Balius TE, Singh I, Levit A, Moroz YS, O\u2019Meara MJ, Che T, Algaa E, Tolmachova K et al (2019) Ultra-large library docking for discovering new chemotypes. Nature 566(7743):224\u2013229","journal-title":"Nature"},{"issue":"23","key":"1077_CR21","doi-asserted-by":"publisher","first-page":"9063","DOI":"10.1021\/acs.jmedchem.5b00586","volume":"58","author":"D Kozakov","year":"2015","unstructured":"Kozakov D, Hall DR, Napoleon RL, Yueh C, Whitty A, Vajda S (2015) New frontiers in druggability. J Med Chem 58(23):9063\u20139088","journal-title":"J Med Chem"},{"issue":"4","key":"1077_CR22","doi-asserted-by":"publisher","first-page":"1225","DOI":"10.1021\/cr040409x","volume":"108","author":"O Keskin","year":"2008","unstructured":"Keskin O, Gursoy A, Ma B, Nussinov R (2008) Principles of protein-protein interactions: what are the preferred ways for proteins to interact? Chem Rev 108(4):1225\u20131244","journal-title":"Chem Rev"},{"issue":"2\u20133","key":"1077_CR23","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1016\/j.pbiomolbio.2014.06.003","volume":"116","author":"E Cukuroglu","year":"2014","unstructured":"Cukuroglu E, Engin HB, Gursoy A, Keskin O (2014) Hot spots in protein-protein interfaces: towards drug discovery. Prog Biophys Mol Biol 116(2\u20133):165\u2013173","journal-title":"Prog Biophys Mol Biol"},{"issue":"4","key":"1077_CR24","doi-asserted-by":"publisher","first-page":"383","DOI":"10.1017\/S0033583512000108","volume":"45","author":"A Winter","year":"2012","unstructured":"Winter A, Higueruelo AP, Marsh M, Sigurdardottir A, Pitt WR, Blundell TL (2012) Biophysical and computational fragment-based approaches to targeting protein-protein interactions: applications in structure-guided drug discovery. Q Rev Biophys 45(4):383\u2013426","journal-title":"Q Rev Biophys"},{"issue":"6","key":"1077_CR25","doi-asserted-by":"publisher","first-page":"344","DOI":"10.1093\/bib\/bbab344","volume":"22","author":"Y Cheng","year":"2021","unstructured":"Cheng Y, Gong Y, Liu Y, Song B, Zou Q (2021) Molecular design in drug discovery: a comprehensive review of deep generative models. Brief Bioinform 22(6):344","journal-title":"Brief Bioinform"},{"issue":"19","key":"1077_CR26","doi-asserted-by":"publisher","first-page":"14011","DOI":"10.1021\/acs.jmedchem.1c00927","volume":"64","author":"X Tong","year":"2021","unstructured":"Tong X, Liu X, Tan X, Li X, Jiang J, Xiong Z, Xu T, Jiang H, Qiao N, Zheng M (2021) Generative models for de novo drug design. J Med Chem 64(19):14011\u201314027","journal-title":"J Med Chem"},{"key":"1077_CR27","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1016\/j.sbi.2021.10.001","volume":"72","author":"M Wang","year":"2022","unstructured":"Wang M, Wang Z, Sun H, Wang J, Shen C, Weng G, Chai X, Li H, Cao D, Hou T (2022) Deep learning approaches for de novo drug design: an overview. Curr Opin Struct Biol 72:135\u2013144","journal-title":"Curr Opin Struct Biol"},{"issue":"13","key":"1077_CR28","doi-asserted-by":"publisher","first-page":"232","DOI":"10.1093\/bioinformatics\/btn162","volume":"24","author":"Y Yamanishi","year":"2008","unstructured":"Yamanishi Y, Araki M, Gutteridge A, Honda W, Kanehisa M (2008) Prediction of drug-target interaction networks from the integration of chemical and genomic spaces. Bioinformatics 24(13):232\u2013240","journal-title":"Bioinformatics"},{"issue":"22\u201323","key":"1077_CR29","first-page":"5545","volume":"36","author":"K Huang","year":"2020","unstructured":"Huang K, Fu T, Glass LM, Zitnik M, Xiao C, Sun J (2020) Deeppurpose: a deep learning library for drug-target interaction prediction. Bioinformatics 36(22\u201323):5545\u20135547","journal-title":"Bioinformatics"},{"issue":"9","key":"1077_CR30","doi-asserted-by":"publisher","first-page":"4695","DOI":"10.1021\/cr400698c","volume":"114","author":"L-G Milroy","year":"2014","unstructured":"Milroy L-G, Grossmann TN, Hennig S, Brunsveld L, Ottmann C (2014) Modulators of protein-protein interactions. Chem Rev 114(9):4695\u20134748","journal-title":"Chem Rev"},{"key":"1077_CR31","doi-asserted-by":"publisher","first-page":"5316","DOI":"10.1016\/j.csbj.2022.08.070","volume":"20","author":"F Soleymani","year":"2022","unstructured":"Soleymani F, Paquet E, Viktor H, Michalowski W, Spinello D (2022) Protein-protein interaction prediction with deep learning: a comprehensive review. Comput Struct Biotechnol J 20:5316\u20135341","journal-title":"Comput Struct Biotechnol J"},{"key":"1077_CR32","doi-asserted-by":"publisher","first-page":"3223","DOI":"10.1016\/j.csbj.2022.06.025","volume":"20","author":"X Hu","year":"2022","unstructured":"Hu X, Feng C, Ling T, Chen M (2022) Deep learning frameworks for protein-protein interaction prediction. Comput Struct Biotechnol J 20:3223\u20133233","journal-title":"Comput Struct Biotechnol J"},{"issue":"90","key":"1077_CR33","doi-asserted-by":"publisher","first-page":"20130860","DOI":"10.1098\/rsif.2013.0860","volume":"11","author":"V Hamon","year":"2014","unstructured":"Hamon V, Bourgeas R, Ducrot P, Theret I, Xuereb L, Basse MJ, Brunel JM, Combes S, Morelli X, Roche P (2014) 2p2ihunter: a tool for filtering orthosteric protein-protein interaction modulators via a dedicated support vector machine. J R Soc Interface 11(90):20130860","journal-title":"J R Soc Interface"},{"issue":"4","key":"1077_CR34","doi-asserted-by":"publisher","first-page":"160501","DOI":"10.1098\/rsos.160501","volume":"4","author":"T Jana","year":"2017","unstructured":"Jana T, Ghosh A, Das Mandal S, Banerjee R, Saha S (2017) Ppimpred: a web server for high-throughput screening of small molecules targeting protein-protein interaction. Royal Society Open Sci 4(4):160501","journal-title":"Royal Society Open Sci"},{"issue":"11","key":"1077_CR35","doi-asserted-by":"publisher","first-page":"5438","DOI":"10.1021\/acs.jcim.1c01135","volume":"61","author":"CH Rodrigues","year":"2021","unstructured":"Rodrigues CH, Pires DE, Ascher DB (2021) Pdcsm-ppi: using graph-based signatures to identify protein-protein interaction inhibitors. J Chem Inf Model 61(11):5438\u20135445","journal-title":"J Chem Inf Model"},{"issue":"5","key":"1077_CR36","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1093\/bib\/bbab111","volume":"22","author":"P Gupta","year":"2021","unstructured":"Gupta P, Mohanty D (2021) Smmppi: a machine learning-based approach for prediction of modulators of protein-protein interactions and its application for identification of novel inhibitors for rbd: hace2 interactions in sars-cov-2. Brief Bioinform 22(5):111","journal-title":"Brief Bioinform"},{"issue":"1","key":"1077_CR37","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13321-024-00930-0","volume":"16","author":"J Wang","year":"2024","unstructured":"Wang J, Mao J, Li C, Xiang H, Wang X, Wang S, Wang Z, Chen Y, Li Y, No KT et al (2024) Interface-aware molecular generative framework for protein-protein interaction modulators. J Cheminform 16(1):1\u201318","journal-title":"J Cheminform"},{"issue":"6637","key":"1077_CR38","doi-asserted-by":"publisher","first-page":"1123","DOI":"10.1126\/science.ade2574","volume":"379","author":"Z Lin","year":"2023","unstructured":"Lin Z, Akin H, Rao R, Hie B, Zhu Z, Lu W, Smetanin N, Verkuil R, Kabeli O, Shmueli Y et al (2023) Evolutionary-scale prediction of atomic-level protein structure with a language model. Science 379(6637):1123\u20131130","journal-title":"Science"},{"issue":"15","key":"1077_CR39","doi-asserted-by":"publisher","first-page":"2016239118","DOI":"10.1073\/pnas.2016239118","volume":"118","author":"A Rives","year":"2021","unstructured":"Rives A, Meier J, Sercu T, Goyal S, Lin Z, Liu J, Guo D, Ott M, Zitnick CL, Ma J et al (2021) Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences. Proc Natl Acad Sci 118(15):2016239118","journal-title":"Proc Natl Acad Sci"},{"key":"1077_CR40","doi-asserted-by":"crossref","unstructured":"Rao RM, Liu J, Verkuil R, Meier J, Canny J, Abbeel P, Sercu T, Rives A (2021) Msa transformer. In: International Conference on Machine Learning, pp. 8844\u20138856 . PMLR","DOI":"10.1101\/2021.02.12.430858"},{"key":"1077_CR41","first-page":"29287","volume":"34","author":"J Meier","year":"2021","unstructured":"Meier J, Rao R, Verkuil R, Liu J, Sercu T, Rives A (2021) Language models enable zero-shot prediction of the effects of mutations on protein function. Adv Neural Inf Process Syst 34:29287\u201329303","journal-title":"Adv Neural Inf Process Syst"},{"key":"1077_CR42","doi-asserted-by":"crossref","unstructured":"Zhou G, Gao K, Hu J, Liu M, Gao J (2023) Uni-mol: a universal 3d molecular representation learning framework. In: International Conference on Learning Representations","DOI":"10.26434\/chemrxiv-2022-jjm0j-v4"},{"issue":"7","key":"1077_CR43","first-page":"3195","volume":"44","author":"A Elnaggar","year":"2021","unstructured":"Elnaggar A, Heinzinger M, Dallago C, Rehawi G, Wang Y, Jones L, Gibbs T, Feher T, Angerer C, Steinegger M et al (2021) Prottrans: towards cracking the language of life\u2019s code through self-supervised deep learning and high performance computing. IEEE Trans Pattern Anal Mach Intell 44(7):3195\u20133211","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"1077_CR44","unstructured":"Long M, Cao Z, Wang J, Jordan MI (2018) Conditional adversarial domain adaptation. Adv Neural Inf Process Syst 31:1640\u20131650"},{"issue":"3","key":"1077_CR45","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1023\/A:1022627411411","volume":"20","author":"C Cortes","year":"1995","unstructured":"Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20:273\u2013297","journal-title":"Mach Learn"},{"key":"1077_CR46","doi-asserted-by":"crossref","unstructured":"Chen T, Guestrin C (2016) Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785\u2013794","DOI":"10.1145\/2939672.2939785"},{"issue":"7","key":"1077_CR47","first-page":"579","volume":"8","author":"M-C Popescu","year":"2009","unstructured":"Popescu M-C, Balas VE, Perescu-Popescu L, Mastorakis N (2009) Multilayer perceptron and neural networks. WSEAS Trans Circuits Syst 8(7):579\u2013588","journal-title":"WSEAS Trans Circuits Syst"},{"key":"1077_CR48","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman L (2001) Random forests. Mach Learn 45:5\u201332","journal-title":"Mach Learn"},{"issue":"23","key":"1077_CR49","doi-asserted-by":"publisher","first-page":"7363","DOI":"10.1021\/acs.jcim.3c01527","volume":"63","author":"H Sun","year":"2023","unstructured":"Sun H, Wang J, Wu H, Lin S, Chen J, Wei J, Lv S, Xiong Y, Wei D-Q (2023) A multimodal deep learning framework for predicting ppi-modulator interactions. J Chem Inf Model 63(23):7363\u20137372","journal-title":"J Chem Inf Model"},{"issue":"9","key":"1077_CR50","doi-asserted-by":"publisher","first-page":"2277","DOI":"10.1126\/sciadv.aax2277","volume":"5","author":"L Wang","year":"2019","unstructured":"Wang L, Zhang L, Li L, Jiang J, Zheng Z, Shang J, Wang C, Chen W, Bao Q, Xu X et al (2019) Small-molecule inhibitor targeting the hsp90-cdc37 protein-protein interaction in colorectal cancer. Sci Adv 5(9):2277","journal-title":"Sci Adv"},{"key":"1077_CR51","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1016\/j.canlet.2018.07.012","volume":"434","author":"X Chen","year":"2018","unstructured":"Chen X, Liu P, Wang Q, Li Y, Fu L, Fu H, Zhu J, Chen Z, Zhu W, Xie C et al (2018) Dcz3112, a novel hsp90 inhibitor, exerts potent antitumor activity against her2-positive breast cancer through disruption of hsp90-cdc37 interaction. Cancer Lett 434:70\u201380","journal-title":"Cancer Lett"},{"issue":"1","key":"1077_CR52","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1002\/jcc.27218","volume":"45","author":"TE Balius","year":"2024","unstructured":"Balius TE, Tan YS, Chakrabarti M (2024) Dock 6: incorporating hierarchical traversal through precomputed ligand conformations to enable large-scale docking. J Comput Chem 45(1):47\u201363","journal-title":"J Comput Chem"},{"issue":"1","key":"1077_CR53","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1002\/pro.3235","volume":"27","author":"TD Goddard","year":"2018","unstructured":"Goddard TD, Huang CC, Meng EC, Pettersen EF, Couch GS, Morris JH, Ferrin TE (2018) Ucsf chimerax: meeting modern challenges in visualization and analysis. Protein Sci 27(1):14\u201325","journal-title":"Protein Sci"},{"issue":"1","key":"1077_CR54","doi-asserted-by":"publisher","first-page":"46733","DOI":"10.1038\/srep46733","volume":"7","author":"L-W Duan","year":"2017","unstructured":"Duan L-W, Zhang H, Zhao M-T, Sun J-X, Chen W-L, Lin J-P, Liu X-Q (2017) A non-canonical binding interface in the crystal structure of hiv-1 gp120 core in complex with cd4. Sci Rep 7(1):46733","journal-title":"Sci Rep"},{"issue":"6686","key":"1077_CR55","doi-asserted-by":"publisher","first-page":"648","DOI":"10.1038\/31405","volume":"393","author":"PD Kwong","year":"1998","unstructured":"Kwong PD, Wyatt R, Robinson J, Sweet RW, Sodroski J, Hendrickson WA (1998) Structure of an hiv gp120 envelope glycoprotein in complex with the cd4 receptor and a neutralizing human antibody. Nature 393(6686):648\u2013659","journal-title":"Nature"},{"issue":"16","key":"1077_CR56","doi-asserted-by":"publisher","first-page":"9026","DOI":"10.1073\/pnas.97.16.9026","volume":"97","author":"DG Myszka","year":"2000","unstructured":"Myszka DG, Sweet RW, Hensley P, Brigham-Burke M, Kwong PD, Hendrickson WA, Wyatt R, Sodroski J, Doyle ML (2000) Energetics of the hiv gp120-cd4 binding reaction. Proc Natl Acad Sci 97(16):9026\u20139031","journal-title":"Proc Natl Acad Sci"},{"key":"1077_CR57","doi-asserted-by":"publisher","first-page":"1090643","DOI":"10.3389\/fchem.2022.1090643","volume":"10","author":"K Ikeda","year":"2023","unstructured":"Ikeda K, Maezawa Y, Yonezawa T, Shimizu Y, Tashiro T, Kanai S, Sugaya N, Masuda Y, Inoue N, Niimi T et al (2023) Dlip-ppi library: an integrated chemical database of small-to-medium-sized molecules targeting protein-protein interactions. Front Chem 10:1090643","journal-title":"Front Chem"},{"issue":"13","key":"1077_CR58","doi-asserted-by":"publisher","first-page":"5041","DOI":"10.1021\/acs.jcim.3c01905","volume":"64","author":"F Cankara","year":"2024","unstructured":"Cankara F, Senyuz S, Sayin AZ, Gursoy A, Keskin O (2024) Dippi: a curated data set for drug-like molecules in protein-protein interfaces. J Chem Inf Model 64(13):5041\u20135051","journal-title":"J Chem Inf Model"},{"issue":"19\u201320","key":"1077_CR59","doi-asserted-by":"publisher","first-page":"958","DOI":"10.1016\/j.drudis.2013.05.003","volume":"18","author":"CM Labb\u00e9","year":"2013","unstructured":"Labb\u00e9 CM, Laconde G, Kuenemann MA, Villoutreix BO, Sperandio O (2013) ippi-db: a manually curated and interactive database of small non-peptide inhibitors of protein-protein interactions. Drug Discov Today 18(19\u201320):958\u2013968","journal-title":"Drug Discov Today"},{"key":"1077_CR60","unstructured":"ChemDiv Inc (2024) ChemDiv: Research and Discovery Screening Libraries. Commercial compound library. https:\/\/www.chemdiv.com\/. Accessed 14 Aug 2025"},{"issue":"6","key":"1077_CR61","doi-asserted-by":"publisher","first-page":"926","DOI":"10.1093\/bioinformatics\/btu739","volume":"31","author":"BE Suzek","year":"2015","unstructured":"Suzek BE, Wang Y, Huang H, McGarvey PB, Wu CH, Consortium U (2015) Uniref clusters: a comprehensive and scalable alternative for improving sequence similarity searches. Bioinformatics 31(6):926\u2013932","journal-title":"Bioinformatics"},{"issue":"1","key":"1077_CR62","doi-asserted-by":"publisher","first-page":"2542","DOI":"10.1038\/s41467-018-04964-5","volume":"9","author":"M Steinegger","year":"2018","unstructured":"Steinegger M, S\u00f6ding J (2018) Clustering huge protein sequence sets in linear time. Nat Commun 9(1):2542","journal-title":"Nat Commun"},{"issue":"7","key":"1077_CR63","doi-asserted-by":"publisher","first-page":"603","DOI":"10.1038\/s41592-019-0437-4","volume":"16","author":"M Steinegger","year":"2019","unstructured":"Steinegger M, Mirdita M, S\u00f6ding J (2019) Protein-level assembly increases protein sequence recovery from metagenomic samples manyfold. Nat Methods 16(7):603\u2013606","journal-title":"Nat Methods"},{"issue":"5","key":"1077_CR64","doi-asserted-by":"publisher","first-page":"742","DOI":"10.1021\/ci100050t","volume":"50","author":"D Rogers","year":"2010","unstructured":"Rogers D, Hahn M (2010) Extended-connectivity fingerprints. J Chem Inf Model 50(5):742\u2013754","journal-title":"J Chem Inf Model"}],"container-title":["Journal of Cheminformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13321-025-01077-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s13321-025-01077-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13321-025-01077-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,9]],"date-time":"2025-09-09T21:58:22Z","timestamp":1757455102000},"score":1,"resource":{"primary":{"URL":"https:\/\/jcheminf.biomedcentral.com\/articles\/10.1186\/s13321-025-01077-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,29]]},"references-count":64,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["1077"],"URL":"https:\/\/doi.org\/10.1186\/s13321-025-01077-2","relation":{},"ISSN":["1758-2946"],"issn-type":[{"type":"electronic","value":"1758-2946"}],"subject":[],"published":{"date-parts":[[2025,8,29]]},"assertion":[{"value":"8 March 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 August 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 August 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no Competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"134"}}