{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-molecular-design","slug":"molecular-design","name":"Molecular design","type":"repo","url":"https://github.com/AspirinCode/papers-for-molecular-design-using-DL","page_url":"https://unfragile.ai/molecular-design","categories":["research-search"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-molecular-design__cap_0","uri":"capability://memory.knowledge.curated.paper.collection.for.molecular.design.with.dl","name":"curated-paper-collection-for-molecular-design-with-dl","description":"Maintains an organized, categorized repository of peer-reviewed papers and research artifacts focused on applying generative AI and deep learning to molecular design tasks. 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Enables domain-focused literature review and helps researchers understand the state-of-the-art within their specific chemistry problem.","intents":["Find all papers on deep learning for drug discovery and lead optimization","Understand what generative approaches have been applied to protein design","Discover papers on materials discovery using generative models","Identify benchmark datasets and evaluation metrics specific to my domain","Compare different deep learning approaches to the same chemistry problem"],"best_for":["domain experts (chemists, biologists) evaluating ML approaches for their field","research teams building domain-specific molecular design pipelines","biotech companies assessing deep learning maturity in their target application","academic groups conducting systematic literature reviews in molecular design"],"limitations":["Clustering is manually curated — no automatic domain inference from paper content","Some papers may apply to multiple domains but appear in only one category","No quantitative metrics on domain maturity (number of papers, citation impact per domain)","Emerging domains (e.g., de novo enzyme design) may be underrepresented","No temporal tracking of when each domain became active in deep learning research"],"requires":["Domain knowledge in chemistry or biology to interpret results","Understanding of specific molecular design tasks within each domain"],"input_types":["application domain names (drug discovery, protein design, materials science, etc.)","specific task names (lead optimization, scaffold hopping, property prediction, etc.)","combination queries across domains"],"output_types":["domain-filtered paper lists","task-specific paper collections","domain-to-methodology mappings","domain maturity summaries"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-molecular-design__cap_4","uri":"capability://memory.knowledge.molecular.representation.technique.reference","name":"molecular-representation-technique-reference","description":"Documents and cross-references the different molecular representations used by papers in the collection (SMILES strings, molecular graphs, 3D coordinates, fingerprints, molecular descriptors, reaction SMARTS) and maps which generative models operate on which representations. 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Enables researchers to understand standard evaluation practices and select appropriate benchmarks for their work.","intents":["Find benchmark datasets used for evaluating molecular generation models","Understand what metrics are standard for evaluating drug discovery models","Discover how different papers measure synthesizability or drug-likeness","Compare evaluation methodologies across papers in my domain","Identify which benchmarks are most commonly used for my application"],"best_for":["researchers designing evaluation frameworks for molecular generation","teams selecting benchmark datasets for model validation","practitioners implementing standard evaluation pipelines","reviewers assessing molecular design paper quality"],"limitations":["Registry is manually maintained — no automated extraction of datasets and metrics from papers","Limited to datasets and metrics mentioned in curated papers","No quantitative analysis of which benchmarks are most predictive of real-world performance","Doesn't track dataset versions or changes over time","No integration with actual benchmark repositories or download links","Missing emerging evaluation approaches (e.g., human expert evaluation, wet-lab validation)"],"requires":["Understanding of molecular design evaluation criteria","Familiarity with common chemistry datasets and metrics"],"input_types":["dataset names (ZINC, ChEMBL, PubChem, etc.)","metric names (validity, novelty, synthesizability, binding affinity, etc.)","application domain or task type"],"output_types":["dataset descriptions and availability information","metric definitions and calculation methods","dataset-to-paper mappings","metric-to-paper mappings","evaluation methodology summaries"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":22,"verified":false,"data_access_risk":"low","permissions":["Git client to clone repository","Web browser or markdown viewer to navigate paper listings","No API keys or external dependencies","Familiarity with molecular design terminology and deep learning architectures","Access to the repository's markdown structure","Background in deep learning fundamentals","Understanding of molecular representations (SMILES, molecular graphs, 3D structures)","Domain knowledge in chemistry or biology to interpret results","Understanding of specific molecular design tasks within each domain","Understanding of molecular chemistry and structure"],"failure_modes":["Static curated list — no automated paper ingestion or real-time literature monitoring","No full-text search or semantic similarity matching across papers","Depends on manual categorization; may have gaps in emerging sub-domains","No integrated paper metadata extraction (citations, impact metrics, code availability)","Requires external tools to access actual paper PDFs or implementations","Cross-reference is manually maintained — no automated extraction of methodology-application pairs from paper abstracts","Limited to papers already in the curated collection","No quantitative comparison of performance across different methods for the same task","Temporal information (when techniques were introduced) not explicitly tracked","No integration with citation networks to identify influential papers per methodology","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.22,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:03.578Z","last_scraped_at":"2026-05-03T14:00:20.516Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=molecular-design","compare_url":"https://unfragile.ai/compare?artifact=molecular-design"}},"signature":"/MSpkmsyArndcjrSW+ecQ1AzTgFTjqC8QlKNbNwm4xTSInrHoJsR4ogTh6mzuyqoUmKJ3Oo5AWwT9Ker+jp7CA==","signedAt":"2026-06-23T06:29:11.467Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/molecular-design","artifact":"https://unfragile.ai/molecular-design","verify":"https://unfragile.ai/api/v1/verify?slug=molecular-design","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}