{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T05:16:09Z","timestamp":1780636569131,"version":"3.54.1"},"reference-count":192,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"2","license":[{"start":{"date-parts":[[2024,2,1]],"date-time":"2024-02-01T00:00:00Z","timestamp":1706745600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2024,2,1]],"date-time":"2024-02-01T00:00:00Z","timestamp":1706745600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,2,1]],"date-time":"2024-02-01T00:00:00Z","timestamp":1706745600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62106219"],"award-info":[{"award-number":["62106219"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004731","name":"Natural Science Foundation of Zhejiang Province","doi-asserted-by":"publisher","award":["QY19E050003"],"award-info":[{"award-number":["QY19E050003"]}],"id":[{"id":"10.13039\/501100004731","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Artif. Intell."],"published-print":{"date-parts":[[2024,2]]},"DOI":"10.1109\/tai.2023.3251977","type":"journal-article","created":{"date-parts":[[2023,3,3]],"date-time":"2023-03-03T13:28:55Z","timestamp":1677850135000},"page":"459-479","source":"Crossref","is-referenced-by-count":24,"title":["Deep Learning Methods for Small Molecule Drug Discovery: A Survey"],"prefix":"10.1109","volume":"5","author":[{"given":"Wenhao","family":"Hu","sequence":"first","affiliation":[{"name":"Zhejiang University&#x2013;University of Illinois Urbana-Champaign Institute, Zhejiang University, Zhejiang, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-6833-9492","authenticated-orcid":false,"given":"Yingying","family":"Liu","sequence":"additional","affiliation":[{"name":"Zhejiang University&#x2013;University of Illinois Urbana-Champaign Institute, Zhejiang University, Zhejiang, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1464-7537","authenticated-orcid":false,"given":"Xuanyu","family":"Chen","sequence":"additional","affiliation":[{"name":"Zhejiang University&#x2013;University of Illinois Urbana-Champaign Institute, Zhejiang University, Zhejiang, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenhao","family":"Chai","sequence":"additional","affiliation":[{"name":"Zhejiang University&#x2013;University of Illinois Urbana-Champaign Institute, Zhejiang University, Zhejiang, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8367-905X","authenticated-orcid":false,"given":"Hangyue","family":"Chen","sequence":"additional","affiliation":[{"name":"Hangzhou Dianzi University, Zhejiang, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3297-1293","authenticated-orcid":false,"given":"Hongwei","family":"Wang","sequence":"additional","affiliation":[{"name":"Zhejiang University&#x2013;University of Illinois Urbana-Champaign Institute and the College of Computer Science and Technology, Zhejiang University, Zhejiang, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8403-1538","authenticated-orcid":false,"given":"Gaoang","family":"Wang","sequence":"additional","affiliation":[{"name":"Zhejiang University&#x2013;University of Illinois Urbana-Champaign Institute and the College of Computer Science and Technology, Zhejiang University, Zhejiang, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbab430"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1021\/acscentsci.9b00576"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.2174\/092986712803530467"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1002\/med.21255"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1021\/ci00024a021"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-40245-7_21"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1021\/ci034160g"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1007\/s11095-008-9609-0"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1002\/qsar.200510135"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-019-0122-0"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/tnnls.2022.3229161"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1002\/wcms.1478"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.3389\/fgene.2019.01041"},{"key":"ref14","article-title":"Why do tree-based models still outperform deep learning on tabular data?","author":"Grinsztajn","year":"2022"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1002\/minf.201900038"},{"issue":"4","key":"ref16","first-page":"559","article-title":"From virtuality to reality-virtual screening in lead discovery and lead optimization: A medicinal chemistry perspective","volume":"11","author":"Rester","year":"2008","journal-title":"Curr. Opin. Drug Discov. Develop."},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-7643-8117-2_6"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1016\/j.cbpa.2021.04.009"},{"key":"ref19","article-title":"AtomNet: A deep convolutional neural network for bioactivity prediction in structure-based drug discovery","author":"Wallach","year":"2015"},{"key":"ref20","article-title":"PADME: A deep learning-based framework for drug-target interaction prediction","author":"Feng","year":"2018"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1038\/nbt.1990"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1038\/nchembio.530"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1021\/ci400709d"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/468"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1039\/C9CC05122H"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbab391"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1002\/wcms.61"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1016\/j.drudis.2014.10.012"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1002\/minf.201501008"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1007\/s11030-021-10217-3"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1080\/17460441.2021.1925247"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1016\/j.ddtec.2020.11.009"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jmedchem.1c00927"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1039\/C9ME00039A"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jmedchem.9b02120"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1016\/j.eng.2021.03.019"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1021\/ci010132r"},{"key":"ref38","article-title":"PubChem Substructure Fingerprint","year":"2022"},{"key":"ref39","article-title":"RDKit","year":"2022"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1186\/1758-2946-3-33"},{"key":"ref41","article-title":"Chemistry development kit (CDK)","year":"2022"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1145\/3107411.3107424"},{"key":"ref43","first-page":"2323","article-title":"Junction tree variational autoencoder for molecular graph generation","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Jin","year":"2018"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1021\/ci00057a005"},{"key":"ref45","article-title":"SMILES transformer: Pre-trained molecular fingerprint for low data drug discovery","author":"Honda","year":"2019"},{"key":"ref46","doi-asserted-by":"crossref","DOI":"10.26434\/chemrxiv-2021-5fwjd","article-title":"Generative pre-training from molecules","author":"Adilov","year":"2021"},{"issue":"8","key":"ref47","first-page":"9","article-title":"Language models are unsupervised multitask learners","volume":"1","author":"Radford","year":"2019","journal-title":"OpenAI Blog"},{"key":"ref48","article-title":"Utilizing edge features in graph neural networks via variational information maximization","author":"Chen","year":"2019"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1186\/s13321-017-0235-x"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1021\/acscentsci.7b00512"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1002\/minf.201700111"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.9b00943"},{"key":"ref53","article-title":"Molecular generation with recurrent neural networks (RNNS)","author":"Bjerrum","year":"2017"},{"key":"ref54","article-title":"In silico generation of novel, drug-like chemical matter using the LSTM neural network","author":"Ertl","year":"2017"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP43922.2022.9747088"},{"key":"ref56","article-title":"All SMILES variational autoencoder","author":"Alperstein","year":"2019"},{"key":"ref57","article-title":"Augmenting genetic algorithms with deep neural networks for exploring the chemical space","author":"Nigam","year":"2019"},{"key":"ref58","first-page":"15476","article-title":"BOSS: Bayesian optimization over string spaces","author":"Moss","year":"2020","journal-title":"Proc. Neural Inf. Process. Syst."},{"key":"ref59","article-title":"Learning multimodal graph-to-graph translation for molecular optimization","author":"Jin","year":"2018"},{"key":"ref60","first-page":"4839","article-title":"Hierarchical generation of molecular graphs using structural motifs","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Jin","year":"2020"},{"key":"ref61","article-title":"Learning to extend molecular scaffolds with structural motifs","author":"Maziarz","year":"2021"},{"key":"ref62","first-page":"6412","article-title":"Graph convolutional policy network for goal-directed molecular graph generation","author":"You","year":"2018","journal-title":"Proc. 32nd Int. Conf. Neural Inf. Process. Syst."},{"key":"ref63","first-page":"7924","article-title":"Hit and lead discovery with explorative RL and fragment-based molecule generation","author":"Yang","year":"2021","journal-title":"Proc. Int. Neural Inf. Process. Syst."},{"key":"ref64","first-page":"4849","article-title":"Multi-objective molecule generation using interpretable substructures","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Jin","year":"2020"},{"key":"ref65","first-page":"1","article-title":"Molecule optimization by explainable evolution","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Chen","year":"2021"},{"key":"ref66","article-title":"MARS: Markov molecular sampling for multi-objective drug discovery","author":"Xie","year":"2021"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1023\/A:1020281327116"},{"key":"ref68","article-title":"Differentiable scaffolding tree for molecular optimization","author":"Fu","year":"2021"},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-019-47148-x"},{"key":"ref70","article-title":"GraphAF: A flow-based autoregressive model for molecular graph generation","author":"Shi","year":"2020"},{"key":"ref71","first-page":"3393","article-title":"ChemBO: Bayesian optimization of small organic molecules with synthesizable recommendations","volume-title":"Proc. Int. Conf. Artif. Intell. Statist.","author":"Korovina","year":"2020"},{"key":"ref72","article-title":"Cells: Cost-effective evolution in latent space for goal-directed molecular generation","author":"Chen","year":"2021"},{"key":"ref73","article-title":"Spatial graph attention and curiosity-driven policy for antiviral drug discovery","author":"Wu","year":"2021"},{"key":"ref74","article-title":"Symmetry-aware actor-critic for 3D molecular design","author":"Simm","year":"2020"},{"key":"ref75","article-title":"A generative model for molecular distance geometry","author":"Simm","year":"2019"},{"key":"ref76","first-page":"11537","article-title":"An end-to-end framework for molecular conformation generation via bilevel programming","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Xu","year":"2021"},{"key":"ref77","first-page":"9558","article-title":"Learning gradient fields for molecular conformation generation","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Shi","year":"2021"},{"key":"ref78","article-title":"Direct molecular conformation generation","author":"Zhu","year":"2022"},{"key":"ref79","article-title":"GeoDiff: A geometric diffusion model for molecular conformation generation","author":"Xu","year":"2022"},{"key":"ref80","first-page":"1","article-title":"An autoregressive flow model for 3D molecular geometry generation from scratch","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Luo","year":"2021"},{"key":"ref81","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-019-56773-5"},{"key":"ref82","first-page":"1","article-title":"Energy-inspired molecular conformation optimization","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Guan","year":"2021"},{"key":"ref83","article-title":"Learning neural generative dynamics for molecular conformation generation","author":"Xu","year":"2021"},{"key":"ref84","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"ref85","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/W14-4012"},{"key":"ref86","article-title":"SMILES2Vec: An interpretable general-purpose deep neural network for predicting chemical properties","author":"Goh","year":"2017"},{"key":"ref87","doi-asserted-by":"publisher","DOI":"10.1155\/2022\/8464452"},{"key":"ref88","doi-asserted-by":"publisher","DOI":"10.1109\/BIBM47256.2019.8983340"},{"key":"ref89","article-title":"CheMixNet: Mixed DNN architectures for predicting chemical properties using multiple molecular representations","author":"Paul","year":"2018"},{"key":"ref90","article-title":"Chemception: A deep neural network with minimal chemistry knowledge matches the performance of expert-developed QSAR\/QSPR models","author":"Goh","year":"2017"},{"key":"ref91","first-page":"3581","article-title":"Semi-supervised learning with deep generative models","author":"Kingma","year":"2014","journal-title":"Proc. Int. Neural Inf. Process. Syst."},{"key":"ref92","article-title":"BERT: Pre-training of deep bidirectional transformers for language understanding","author":"Devlin","year":"2018"},{"key":"ref93","article-title":"RoBERTa: A robustly optimized bert pretraining approach","author":"Liu","year":"2019"},{"key":"ref94","doi-asserted-by":"publisher","DOI":"10.1109\/ICISCT52966.2021.9670046"},{"key":"ref95","article-title":"ChemBERTa: Large-scale self-supervised pretraining for molecular property prediction","author":"Chithrananda","year":"2020"},{"key":"ref96","doi-asserted-by":"publisher","DOI":"10.1145\/3233547.3233548"},{"key":"ref97","doi-asserted-by":"publisher","DOI":"10.1145\/3307339.3342186"},{"issue":"3","key":"ref98","first-page":"199","article-title":"Circular fingerprints: Flexible molecular descriptors with applications from physical chemistry to ADME","volume":"9","author":"Glen","year":"2006","journal-title":"IDrugs"},{"key":"ref99","article-title":"Neural machine translation by jointly learning to align and translate","author":"Bahdanau","year":"2014"},{"key":"ref100","article-title":"Semi-supervised classification with graph convolutional networks","author":"Kipf","year":"2016"},{"key":"ref101","doi-asserted-by":"publisher","DOI":"10.1038\/sdata.2014.22"},{"key":"ref102","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33011052"},{"key":"ref103","article-title":"Pre-training molecular graph representation with 3D geometry","author":"Liu","year":"2021"},{"key":"ref104","article-title":"Strategies for pre-training graph neural networks","author":"Hu","year":"2019"},{"key":"ref105","first-page":"12559","article-title":"Self-supervised graph transformer on large-scale molecular data","author":"Rong","year":"2020","journal-title":"Proc. Int. Conf. Neural Inf. Process. Syst."},{"key":"ref106","first-page":"5812","article-title":"Graph contrastive learning with augmentations","author":"You","year":"2020","journal-title":"Proc. Int. Conf. Neural Inf. Process. Syst."},{"key":"ref107","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-022-00447-x"},{"key":"ref108","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i4.20377"},{"key":"ref109","first-page":"1263","article-title":"Neural message passing for quantum chemistry","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Gilmer","year":"2017"},{"key":"ref110","first-page":"1","article-title":"Spherical message passing for 3D molecular graphs","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Liu","year":"2021"},{"key":"ref111","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403117"},{"key":"ref112","article-title":"Graph neural networks with learnable structural and positional representations","author":"Dwivedi","year":"2021"},{"issue":"3","key":"ref113","first-page":"4","article-title":"Deep graph infomax","volume":"2","author":"Velickovic","year":"2019","journal-title":"Proc. Int. Conf. Learn. Representations (Poster)"},{"key":"ref114","article-title":"Learning deep representations by mutual information estimation and maximization","author":"Hjelm","year":"2018"},{"key":"ref115","article-title":"How powerful are graph neural networks?","author":"Xu","year":"2018"},{"key":"ref116","article-title":"InfoGraph: Unsupervised and semi-supervised graph-level representation learning via mutual information maximization","author":"Sun","year":"2019"},{"key":"ref117","first-page":"1","article-title":"Conformation-guided molecular representation with hamiltonian neural networks","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Li","year":"2020"},{"key":"ref118","first-page":"16346","article-title":"Deep molecular representation learning via fusing physical and chemical information","author":"Yang","year":"2021","journal-title":"Proc. Int. Conf. Neural Inf. Process. Syst."},{"key":"ref119","article-title":"Molecule attention transformer","author":"Maziarka","year":"2020"},{"key":"ref120","doi-asserted-by":"publisher","DOI":"10.1002\/chem.201605499"},{"key":"ref121","doi-asserted-by":"publisher","DOI":"10.1021\/acscentsci.7b00355"},{"key":"ref122","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.8b00801"},{"key":"ref123","doi-asserted-by":"publisher","DOI":"10.1038\/nature25978"},{"key":"ref124","first-page":"8872","article-title":"Retrosynthesis prediction with conditional graph logic network","author":"Dai","year":"2019","journal-title":"Proc. Int. Conf. Neural Inf. Process. Syst."},{"key":"ref125","doi-asserted-by":"publisher","DOI":"10.1021\/jacsau.1c00246"},{"key":"ref126","doi-asserted-by":"publisher","DOI":"10.1021\/acscentsci.7b00303"},{"key":"ref127","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-30493-5_78"},{"key":"ref128","article-title":"Learning to make generalizable and diverse predictions for retrosynthesis","author":"Chen","year":"2019"},{"key":"ref129","article-title":"Data transfer approaches to improve seq-to-seq retrosynthesis","author":"Ishiguro","year":"2020"},{"key":"ref130","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.9b00949"},{"key":"ref131","article-title":"Leveraging reaction-aware substructures for retrosynthesis and reaction prediction","author":"Zhao","year":"2022"},{"key":"ref132","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.1c00537"},{"key":"ref133","first-page":"8818","article-title":"A graph to graphs framework for retrosynthesis prediction","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Shi","year":"2020"},{"key":"ref134","first-page":"9405","article-title":"Learning graph models for retrosynthesis prediction","author":"Somnath","year":"2021","journal-title":"Proc. Int. Conf. Neural Inf. Process. Syst."},{"key":"ref135","article-title":"SemiRetro: Semi-template framework boosts deep retrosynthesis prediction","author":"Gao","year":"2022"},{"key":"ref136","first-page":"11248","article-title":"RetroXpert: Decompose retrosynthesis prediction like a chemist","author":"Yan","year":"2020","journal-title":"Proc. Int. Conf. Neural Inf. Process. Syst."},{"key":"ref137","article-title":"Energy-based view of retrosynthesis","author":"Sun","year":"2020"},{"key":"ref138","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i1.16131"},{"key":"ref139","article-title":"Retroformer: Pushing the limits of interpretable end-to-end retrosynthesis transformer","author":"Wan","year":"2022"},{"key":"ref140","doi-asserted-by":"publisher","DOI":"10.1021\/acscentsci.6b00219"},{"key":"ref141","doi-asserted-by":"publisher","DOI":"10.1021\/acscentsci.7b00064"},{"key":"ref142","doi-asserted-by":"publisher","DOI":"10.1002\/chem.201604556"},{"key":"ref143","first-page":"2604","article-title":"Predicting organic reaction outcomes with Weisfeiler-Lehman network","author":"Jin","year":"2017","journal-title":"Proc. 31st Int. Conf. Neural Inf. Process. Syst."},{"key":"ref144","doi-asserted-by":"publisher","DOI":"10.1039\/C8SC04228D"},{"key":"ref145","doi-asserted-by":"crossref","DOI":"10.26434\/chemrxiv.11659563.v1","article-title":"Integrating deep neural networks and symbolic inference for organic reactivity prediction","author":"Qian","year":"2020"},{"key":"ref146","article-title":"A generative model for electron paths","author":"Bradshaw","year":"2018"},{"key":"ref147","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330958"},{"key":"ref148","first-page":"904","article-title":"Non-autoregressive electron redistribution modeling for reaction prediction","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Bi","year":"2021"},{"key":"ref149","doi-asserted-by":"publisher","DOI":"10.1039\/C8SC02339E"},{"key":"ref150","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gkaa971"},{"key":"ref151","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gkr777"},{"key":"ref152","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.5b00559"},{"key":"ref153","doi-asserted-by":"publisher","DOI":"10.1002\/anie.200462457"},{"issue":"2","key":"ref154","doi-asserted-by":"crossref","first-page":"342","DOI":"10.1021\/ci600423u","article-title":"Virtual exploration of the chemical universe up to 11 atoms of C, N, O, F: Assembly of 26.4 million structures (110.9 million stereoisomers) and analysis for new ring systems, stereochemistry, physicochemical properties, compound classes, and drug discovery","volume":"47","author":"Fink","year":"2007","journal-title":"J. Chem. Inf. Model."},{"key":"ref155","doi-asserted-by":"publisher","DOI":"10.1021\/ja902302h"},{"key":"ref156","doi-asserted-by":"publisher","DOI":"10.1021\/ci300415d"},{"key":"ref157","doi-asserted-by":"publisher","DOI":"10.1038\/s41597-022-01288-4"},{"key":"ref158","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.6b00564"},{"key":"ref159","doi-asserted-by":"publisher","DOI":"10.1039\/C7SC02664A"},{"key":"ref160","doi-asserted-by":"publisher","DOI":"10.1038\/s41587-020-0418-2"},{"key":"ref161","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.8b00839"},{"key":"ref162","first-page":"2021","article-title":"FS-Mol: A few-shot learning dataset of molecules","volume-title":"Proc. 35th Conf. Neural Inf. Process. Syst. Datasets Benchmarks Track (Round 2)","author":"Stanley"},{"key":"ref163","doi-asserted-by":"publisher","DOI":"10.1021\/acscentsci.6b00367"},{"key":"ref164","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.6b00290"},{"key":"ref165","article-title":"Data-efficient graph grammar learning for molecular generation","author":"Guo","year":"2022"},{"key":"ref166","first-page":"1608","article-title":"Retro*: Learning retrosynthetic planning with neural guided A* search","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Chen","year":"2020"},{"key":"ref167","doi-asserted-by":"publisher","DOI":"10.3389\/fchem.2019.00509"},{"key":"ref168","doi-asserted-by":"publisher","DOI":"10.12688\/f1000research.9611.1"},{"key":"ref169","doi-asserted-by":"publisher","DOI":"10.1016\/j.ddtec.2020.09.003"},{"key":"ref170","doi-asserted-by":"publisher","DOI":"10.1039\/C9SC05704H"},{"key":"ref171","doi-asserted-by":"publisher","DOI":"10.1039\/C9SC04944D"},{"key":"ref172","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.0c00403"},{"key":"ref173","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-020-00236-4"},{"key":"ref174","doi-asserted-by":"publisher","DOI":"10.1038\/s41573-019-0024-5"},{"key":"ref175","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611977172.9"},{"key":"ref176","article-title":"MolGenSurvey: A systematic survey in machine learning models for molecule design","author":"Du","year":"2022"},{"key":"ref177","doi-asserted-by":"publisher","DOI":"10.1016\/j.drudis.2020.11.027"},{"key":"ref178","first-page":"92","article-title":"Conformal prediction of small-molecule drug resistance in cancer cell lines","volume-title":"Proc. Conformal Probab. Prediction Appl.","author":"Hernndez-Hernndez","year":"2022"},{"key":"ref179","doi-asserted-by":"publisher","DOI":"10.1016\/j.cels.2020.09.007"},{"key":"ref180","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-021-22951-1"},{"key":"ref181","doi-asserted-by":"publisher","DOI":"10.1039\/D0SC04184J"},{"key":"ref182","doi-asserted-by":"publisher","DOI":"10.1021\/acscentsci.8b00176"},{"key":"ref183","doi-asserted-by":"publisher","DOI":"10.1039\/C8SC00148K"},{"key":"ref184","first-page":"1","article-title":"Deep learning as an opportunity in virtual screening","volume":"27","author":"Unterthiner","year":"2017","journal-title":"Proc. Deep Learn. Workshop NIPS"},{"key":"ref185","doi-asserted-by":"publisher","DOI":"10.3389\/fenvs.2015.00080"},{"key":"ref186","first-page":"1","article-title":"Context-enriched molecule representations improve few-shot drug discovery","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Schimunek","year":"2023"},{"key":"ref187","article-title":"N-gram graph: Simple unsupervised representation for graphs, with applications to molecules","volume":"32","author":"Liu","year":"2019","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref188","doi-asserted-by":"publisher","DOI":"10.1021\/acscentsci.6b00367"},{"key":"ref189","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.1c01065"},{"key":"ref190","doi-asserted-by":"publisher","DOI":"10.1186\/s13321-017-0203-5"},{"key":"ref191","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-28954-6_18"},{"key":"ref192","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.8b00263"}],"container-title":["IEEE Transactions on Artificial Intelligence"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9078688\/10433275\/10058590.pdf?arnumber=10058590","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,23]],"date-time":"2025-08-23T01:08:48Z","timestamp":1755911328000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10058590\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2]]},"references-count":192,"journal-issue":{"issue":"2"},"URL":"https:\/\/doi.org\/10.1109\/tai.2023.3251977","relation":{},"ISSN":["2691-4581"],"issn-type":[{"value":"2691-4581","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2]]}}}