{"id":"https://openalex.org/W7161717185","doi":"https://doi.org/10.48550/arxiv.2605.17693","title":"Fine-tuning Pocket-Aware Diffusion Models via Denoising Policy Optimization","display_name":"Fine-tuning Pocket-Aware Diffusion Models via Denoising Policy Optimization","publication_year":2026,"publication_date":"2026-05-17","ids":{"openalex":"https://openalex.org/W7161717185","doi":"https://doi.org/10.48550/arxiv.2605.17693"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.17693","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.17693","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.17693","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5136485730","display_name":"Yuan Xue","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xue, Yuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009587907","display_name":"Daniel Kudenko\u22c6","orcid":"https://orcid.org/0000-0003-3359-3255"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kudenko, Daniel","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5027689420","display_name":"Megha Khosla","orcid":"https://orcid.org/0000-0002-0319-3181"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Khosla, Megha","raw_affiliation_strings":[],"raw_orcid":null,"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":false,"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/T10211","display_name":"Computational Drug Discovery Methods","score":0.7161999940872192,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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/T10211","display_name":"Computational Drug Discovery Methods","score":0.7161999940872192,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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/T11948","display_name":"Machine Learning in Materials Science","score":0.05779999867081642,"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"}},{"id":"https://openalex.org/T10044","display_name":"Protein Structure and Dynamics","score":0.044599998742341995,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6226000189781189},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.6115999817848206},{"id":"https://openalex.org/keywords/markov-decision-process","display_name":"Markov decision process","score":0.4650000035762787},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.45329999923706055},{"id":"https://openalex.org/keywords/reduction","display_name":"Reduction (mathematics)","score":0.4366999864578247},{"id":"https://openalex.org/keywords/markov-chain","display_name":"Markov chain","score":0.41190001368522644},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.40700000524520874},{"id":"https://openalex.org/keywords/optimization-problem","display_name":"Optimization problem","score":0.3917999863624573},{"id":"https://openalex.org/keywords/markov-process","display_name":"Markov process","score":0.38499999046325684}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6430000066757202},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6226000189781189},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.6115999817848206},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.4936000108718872},{"id":"https://openalex.org/C106189395","wikidata":"https://www.wikidata.org/wiki/Q176789","display_name":"Markov decision process","level":3,"score":0.4650000035762787},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.45329999923706055},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.4366999864578247},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.43549999594688416},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.41190001368522644},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.40700000524520874},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3937999904155731},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.3917999863624573},{"id":"https://openalex.org/C159886148","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov process","level":2,"score":0.38499999046325684},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3781999945640564},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.36980000138282776},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.3425000011920929},{"id":"https://openalex.org/C2775924081","wikidata":"https://www.wikidata.org/wiki/Q55608371","display_name":"Control (management)","level":2,"score":0.3398999869823456},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.33480000495910645},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.33320000767707825},{"id":"https://openalex.org/C147764199","wikidata":"https://www.wikidata.org/wiki/Q6865248","display_name":"Minification","level":2,"score":0.33250001072883606},{"id":"https://openalex.org/C69357855","wikidata":"https://www.wikidata.org/wiki/Q163214","display_name":"Diffusion","level":2,"score":0.32710000872612},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.32420000433921814},{"id":"https://openalex.org/C68710425","wikidata":"https://www.wikidata.org/wiki/Q5275442","display_name":"Diffusion process","level":3,"score":0.3068000078201294},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.28200000524520874},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2728999853134155},{"id":"https://openalex.org/C55660270","wikidata":"https://www.wikidata.org/wiki/Q5164377","display_name":"Constrained optimization","level":2,"score":0.26440000534057617},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.25440001487731934},{"id":"https://openalex.org/C110121322","wikidata":"https://www.wikidata.org/wiki/Q865811","display_name":"Distribution (mathematics)","level":2,"score":0.25290000438690186}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.17693","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.17693","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.17693","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.17693","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Structure-based":[0],"drug":[1,31],"design":[2],"has":[3,36],"been":[4],"accelerated":[5],"by":[6],"pocket-aware":[7,72,100],"3D":[8],"generative":[9],"models,":[10],"yet":[11],"most":[12],"methods":[13],"primarily":[14],"fit":[15],"the":[16,93,98,110,121,133,146],"training":[17],"distribution":[18],"and":[19,89,139,162],"may":[20],"fall":[21],"short":[22],"of":[23,97],"satisfying":[24],"multiple":[25,48,82],"properties":[26,112],"required":[27],"in":[28,154,168],"real-world":[29],"therapeutic":[30],"discovery.":[32],"Recently,":[33],"increasing":[34],"attention":[35],"focused":[37],"on":[38,120,145],"structure-based":[39],"molecule":[40,141],"optimization":[41,80],"(SBMO),":[42],"which":[43],"targets":[44],"fine-grained":[45],"control":[46],"over":[47,81],"specified":[49],"molecular":[50],"properties.":[51],"In":[52],"this":[53],"paper,":[54],"we":[55],"present":[56],"DEPPA,":[57],"a":[58,70,104,128],"novel":[59],"SBMO":[60],"approach":[61],"building":[62],"upon":[63],"Denoising":[64],"Diffusion":[65],"Policy":[66],"Optimization":[67],"for":[68],"fine-tuning":[69,135],"pre-trained":[71],"diffusion":[73,101],"model":[74,102],"via":[75],"reinforcement":[76],"learning.":[77],"DEPPA":[78,126,151],"enables":[79],"properties,":[83],"including":[84],"binding":[85,155],"affinity,":[86],"drug-likeness,":[87],"synthesizability":[88],"diversity.":[90],"We":[91],"formulate":[92],"reverse":[94],"denoising":[95,130],"process":[96],"pretrained":[99],"as":[103,115],"multi-step":[105],"Markov":[106],"Decision":[107],"Process,":[108],"where":[109],"desired":[111],"that":[113,150],"serve":[114],"reward":[116],"signals":[117],"are":[118],"evaluated":[119],"final":[122],"generated":[123],"ligand":[124],"molecules.":[125],"incorporates":[127],"coarse":[129],"scheduler":[131],"during":[132],"RL":[134],"to":[136],"achieve":[137],"efficient":[138],"effective":[140],"optimization.":[142],"Experimental":[143],"results":[144],"CrossDocked2020":[147],"benchmark":[148],"demonstrate":[149],"outperforms":[152],"baselines":[153],"affinity":[156],"(Vina":[157],"Score":[158],"-8.5":[159],"kcal/mol),":[160],"drug-likeness":[161],"diversity":[163],"while":[164],"exhibiting":[165],"competitive":[166],"performance":[167],"synthesizability.":[169],"The":[170],"source":[171],"code":[172],"is":[173],"available":[174],"at":[175],"https://github.com/xy9485/DePPA":[176],".":[177]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-20T00:00:00"}