{"id":"https://openalex.org/W7204938300","doi":"https://doi.org/10.26434/chemrxiv.15008138/v1","title":"VisXAI: Structure-Mapped Explainable Artificial Intelligence for Molecular Machine Learning","display_name":"VisXAI: Structure-Mapped Explainable Artificial Intelligence for Molecular Machine Learning","publication_year":2026,"publication_date":"2026-09-01","ids":{"openalex":"https://openalex.org/W7204938300","doi":"https://doi.org/10.26434/chemrxiv.15008138/v1"},"language":"en","primary_location":{"id":"doi:10.26434/chemrxiv.15008138/v1","doi":"https://doi.org/10.26434/chemrxiv.15008138/v1","is_oa":true,"landing_page_url":"https://doi.org/10.26434/chemrxiv.15008138/v1","pdf_url":"https://chemrxiv.org/doi/pdf/10.26434/chemrxiv.15008138/v1","source":{"id":"https://openalex.org/S4393918830","display_name":"ChemRxiv","issn_l":"2573-2293","issn":["2573-2293"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"posted-content"},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://chemrxiv.org/doi/pdf/10.26434/chemrxiv.15008138/v1","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5148880570","display_name":"Jiaming Yao","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jiaming Yao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5143984288","display_name":"Samanyu Kulkarni","orcid":"https://orcid.org/0009-0007-9660-6056","observed_orcids":["https://orcid.org/0009-0007-9660-6056"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Samanyu Kulkarni","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0009-0007-9660-6056","affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.9692000150680542,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.9692000150680542,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10211","display_name":"Computational Drug Discovery Methods","score":0.027699999511241913,"subfield":{"id":"https://openalex.org/subfields/3002","display_name":"Drug Discovery"},"field":{"id":"https://openalex.org/fields/30","display_name":"Pharmacology, Toxicology and Pharmaceutics"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T11948","display_name":"Machine Learning in Materials Science","score":0.0006000000284984708,"subfield":{"id":"https://openalex.org/subfields/2505","display_name":"Materials Chemistry"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/explainable-ai","display_name":"explainable AI","score":0.9340000152587891},{"id":"https://openalex.org/keywords/molecular-machine-learning","display_name":"molecular machine learning","score":0.9710000157356262},{"id":"https://openalex.org/keywords/attribution","display_name":"attribution","score":0.35199999809265137},{"id":"https://openalex.org/keywords/morgan-fingerprints","display_name":"Morgan fingerprints","score":0.6389999985694885},{"id":"https://openalex.org/keywords/maccs-fingerprints","display_name":"MACCS fingerprints","score":0.9929999709129333},{"id":"https://openalex.org/keywords/graph-neural-networks","display_name":"graph neural networks","score":0.8230000138282776},{"id":"https://openalex.org/keywords/treeshap","display_name":"TreeSHAP","score":0.847000002861023},{"id":"https://openalex.org/keywords/grad-cam","display_name":"Grad-CAM","score":0.8460000157356262},{"id":"https://openalex.org/keywords/integrated-gradients","display_name":"integrated gradients","score":0.9229999780654907},{"id":"https://openalex.org/keywords/attention-visualization","display_name":"attention visualization","score":0.49799999594688416},{"id":"https://openalex.org/keywords/molecular-visualization","display_name":"molecular visualization","score":0.4099999964237213},{"id":"https://openalex.org/keywords/logp-prediction","display_name":"logP prediction","score":0.5690000057220459}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8151000142097473},{"id":"https://openalex.org/C519991488","wikidata":"https://www.wikidata.org/wiki/Q28865","display_name":"Python (programming language)","level":2,"score":0.7343000173568726},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5392000079154968},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.5102999806404114},{"id":"https://openalex.org/C2780022179","wikidata":"https://www.wikidata.org/wiki/Q1986794","display_name":"Molecular graph","level":3,"score":0.44190001487731934},{"id":"https://openalex.org/C544833334","wikidata":"https://www.wikidata.org/wiki/Q2005","display_name":"JavaScript","level":2,"score":0.42559999227523804},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.421099990606308},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.40130001306533813},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.3971000015735626},{"id":"https://openalex.org/C160145156","wikidata":"https://www.wikidata.org/wiki/Q778586","display_name":"Executable","level":2,"score":0.3750999867916107},{"id":"https://openalex.org/C101468663","wikidata":"https://www.wikidata.org/wiki/Q1620158","display_name":"Modular design","level":2,"score":0.375},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.352400004863739},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.35109999775886536},{"id":"https://openalex.org/C126766683","wikidata":"https://www.wikidata.org/wiki/Q6896006","display_name":"Molecular graphics","level":3,"score":0.34459999203681946},{"id":"https://openalex.org/C21442007","wikidata":"https://www.wikidata.org/wiki/Q1027879","display_name":"Graphics","level":2,"score":0.32670000195503235},{"id":"https://openalex.org/C4924752","wikidata":"https://www.wikidata.org/wiki/Q184148","display_name":"Plug-in","level":2,"score":0.32580000162124634},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.29319998621940613},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.2913999855518341},{"id":"https://openalex.org/C2777135195","wikidata":"https://www.wikidata.org/wiki/Q3870431","display_name":"Chalcone synthase","level":4,"score":0.28859999775886536},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2766999900341034},{"id":"https://openalex.org/C73752529","wikidata":"https://www.wikidata.org/wiki/Q79872","display_name":"Object-oriented programming","level":2,"score":0.27459999918937683},{"id":"https://openalex.org/C150202949","wikidata":"https://www.wikidata.org/wiki/Q107602","display_name":"Pointer (user interface)","level":2,"score":0.2667999863624573},{"id":"https://openalex.org/C137922610","wikidata":"https://www.wikidata.org/wiki/Q2093","display_name":"Document Object Model","level":3,"score":0.25929999351501465},{"id":"https://openalex.org/C21119673","wikidata":"https://www.wikidata.org/wiki/Q3714476","display_name":"Dot matrix","level":2,"score":0.2581000030040741},{"id":"https://openalex.org/C61423126","wikidata":"https://www.wikidata.org/wiki/Q187432","display_name":"Scripting language","level":2,"score":0.2540999948978424}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.26434/chemrxiv.15008138/v1","doi":"https://doi.org/10.26434/chemrxiv.15008138/v1","is_oa":true,"landing_page_url":"https://doi.org/10.26434/chemrxiv.15008138/v1","pdf_url":"https://chemrxiv.org/doi/pdf/10.26434/chemrxiv.15008138/v1","source":{"id":"https://openalex.org/S4393918830","display_name":"ChemRxiv","issn_l":"2573-2293","issn":["2573-2293"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"posted-content"}],"best_oa_location":{"id":"doi:10.26434/chemrxiv.15008138/v1","doi":"https://doi.org/10.26434/chemrxiv.15008138/v1","is_oa":true,"landing_page_url":"https://doi.org/10.26434/chemrxiv.15008138/v1","pdf_url":"https://chemrxiv.org/doi/pdf/10.26434/chemrxiv.15008138/v1","source":{"id":"https://openalex.org/S4393918830","display_name":"ChemRxiv","issn_l":"2573-2293","issn":["2573-2293"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"posted-content"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7204938300.pdf","grobid_xml":"https://content.openalex.org/works/W7204938300.grobid-xml"},"referenced_works_count":10,"referenced_works":["https://openalex.org/W1975147762","https://openalex.org/W2044834685","https://openalex.org/W2200017991","https://openalex.org/W2918342466","https://openalex.org/W2962858109","https://openalex.org/W2999615587","https://openalex.org/W3022265721","https://openalex.org/W3085380432","https://openalex.org/W3093934881","https://openalex.org/W4313648381"],"related_works":["https://openalex.org/W4313648381","https://openalex.org/W4220975973","https://openalex.org/W4402643187","https://openalex.org/W6902116599","https://openalex.org/W4401628761","https://openalex.org/W4398230033","https://openalex.org/W4406362282","https://openalex.org/W7128718261","https://openalex.org/W3131648425","https://openalex.org/W7204484178"],"abstract_inverted_index":{"Background.":[0],"Molecular":[1],"machine-learning":[2],"models":[3],"use":[4],"representations":[5],"such":[6],"as":[7,145],"fingerprints,":[8],"tokenized":[9,88],"simplified":[10],"molecular":[11,19,70,245],"input":[12],"line":[13],"entry":[14],"system":[15],"(SMILES)":[16],"strings,":[17],"and":[18,45,53,77,84,96,108,121,133,186,206,217,237,259,267],"graphs.":[20],"Explanation":[21],"methods":[22],"then":[23,40],"return":[24],"importance":[25],"values":[26,38],"for":[27,58,69,82,214,240,270],"fingerprint":[28],"bits,":[29],"sequence":[30,113,182],"tokens,":[31],"graph":[32,35,101],"nodes,":[33],"or":[34,159,163,229],"edges.":[36],"These":[37],"must":[39],"be":[41,143],"mapped":[42],"to":[43],"atoms":[44],"bonds.":[46],"This":[47],"mapping":[48,238,266],"depends":[49],"on":[50],"the":[51,127,177,215,248],"representation":[52,179],"is":[54],"often":[55],"implemented":[56],"separately":[57],"each":[59,271],"model.":[60],"Results.":[61],"We":[62],"present":[63],"VisXAI,":[64],"a":[65,118,154,166,187,226,234],"modular":[66],"Python":[67,203,216],"framework":[68,253],"featurization,":[71],"trainedmodel":[72],"interfaces,":[73],"attribution,":[74],"structural":[75],"mapping,":[76],"visualization.":[78],"It":[79,232],"supports":[80,92,105,254],"TreeSHAP":[81],"Morgan":[83],"MACCS":[85],"fingerprints.":[86],"For":[87,98],"SMILES":[89],"models,":[90,183],"it":[91,104],"attention,":[93],"Integrated":[94,106],"Gradients,":[95],"Grad-CAM.":[97,109],"PyTorch":[99],"Geometric":[100],"neural":[102],"networks,":[103],"Gradients":[107],"Pretrained":[110],"Hugging":[111],"Face":[112],"checkpoints":[114],"are":[115],"supported":[116],"through":[117],"tokenizer":[119],"adapter":[120],"model":[122,255],"loader.":[123],"Every":[124],"pathway":[125],"produces":[126],"same":[128,249],"explanation":[129],"object":[130],"containing":[131],"atom":[132],"optional":[134],"bond":[135],"scores":[136],"in":[137,153,247],"RDKit":[138],"index":[139],"space.":[140],"Explanations":[141],"can":[142],"rendered":[144],"static":[146],"two-dimensional":[147],"scalable":[148],"vector":[149],"graphics":[150],"(SVG),":[151],"compared":[152],"notebook":[155],"dashboard":[156],"with":[157,191,210],"shared":[158],"per-panel":[160],"color":[161],"scales,":[162],"explored":[164],"using":[165],"linked":[167],"molecule/SMILES":[168],"widget":[169,218],"inspired":[170],"by":[171],"XSMILES.":[172],"Six":[173],"executable":[174],"notebooks":[175],"cover":[176],"three":[178],"families,":[180],"pretrained":[181],"interactive":[184],"comparison,":[185,258],"logP":[188],"case":[189],"study":[190],"an":[192],"exact":[193],"Crippen":[194],"reference":[195],"decomposition.":[196],"The":[197,252],"current":[198],"source":[199],"tree":[200],"defines":[201],"619":[202],"test":[204],"cases":[205],"44":[207],"JavaScript":[208],"tests,":[209],"separate":[211,265],"continuous-integration":[212],"jobs":[213],"front":[219],"ends.":[220],"Conclusions.":[221],"VisXAI":[222],"does":[223],"not":[224],"introduce":[225],"predictive":[227],"architecture":[228],"attribution":[230],"algorithm.":[231],"provides":[233],"tested":[235],"integration":[236],"layer":[239],"expressing":[241],"different":[242],"types":[243],"of":[244],"explanations":[246],"chemical":[250],"coordinates.":[251],"analysis,":[256],"method":[257],"reproducible":[260],"molecular-XAI":[261],"workflows":[262],"without":[263],"requiring":[264],"visualization":[268],"code":[269],"representation.":[272]},"counts_by_year":[],"updated_date":"2026-11-09T13:22:03.289127","created_date":"2026-09-02T00:00:00"}