{
 "title": "Adamax evidence card dataset",
 "version": "1.1",
 "cut_off": "2026-10-25",
 "license": "CC BY 4.0",
 "publisher": "Condor Research Editorial",
 "pubmed_counts": {
  "semax[tiab]": 210,
  "P021 OR peptide 021": 30,
  "N-acetyl Semax (records identified)": 2,
  "Adamax peptide": 0
 },
 "census_raw": {
  "fecha_corte": "2026-10-25",
  "compuesto": "Adamax (peptido vendido como analogo de Semax)",
  "nota": "Clasificacion de relevancia a mano a partir de titulos; 'trata_adamax' = literatura sobre la molecula concreta.",
  "pubmed": [
   {
    "query": "Adamax[All Fields]",
    "count": 39,
    "ids": [
     "42236259",
     "42151246",
     "42151239",
     "41803168",
     "41789888",
     "41326571",
     "41266780",
     "41241764",
     "41193653",
     "41129968",
     "41023344",
     "40989412",
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     "40839497",
     "40783578",
     "40587439",
     "40310446",
     "39381813",
     "39224246",
     "38434054",
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     "38228639",
     "38195582",
     "38031738",
     "37960498",
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     "37679099",
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     "33643406",
     "33195909",
     "32906742",
     "32635409",
     "30963340"
    ],
    "translation": "\"Adamax\"[All Fields]",
    "clasificacion": {
     "homonimo_optimizador_AdaMax_(ML)": 39,
     "trata_adamax_peptido": 0
    },
    "registros": {
     "42236259": {
      "year": "2026",
      "title": "Diabetes Management Through Glucose Dynamics Analysis Network: A Novel Approach for Accurate Blood Glucose Level Forecasting.",
      "journal": "Diabetes Obes Metab",
      "first_author": "Sawant PM"
     },
     "42151246": {
      "year": "2026",
      "title": "Optimized CNN-based ensemble deep learning approach for potato leaf disease detection with data augmentation.",
      "journal": "Sci Rep",
      "first_author": "Jain A"
     },
     "42151239": {
      "year": "2026",
      "title": "A robust stacked ensemble strategy with multi-optimizer CNN models for skin cancer classification.",
      "journal": "Sci Rep",
      "first_author": "Panwar A"
     },
     "41803168": {
      "year": "2026",
      "title": "Anomaly-based intrusion detection on benchmark datasets for network security: a comprehensive evaluation.",
      "journal": "Sci Rep",
      "first_author": "Kumar LKS"
     },
     "41789888": {
      "year": "2026",
      "title": "Prediction of penicillin concentration using time series forecasting and machine learning techniques.",
      "journal": "Drug Dev Ind Pharm",
      "first_author": "Rekkas-Ventiris M"
     },
     "41326571": {
      "year": "2025",
      "title": "Artificial Intelligence-based fine-tuning model for fall activity recognition in disabled persons within an IoT environment.",
      "journal": "Sci Rep",
      "first_author": "Alzahrani A"
     },
     "41266780": {
      "year": "2025",
      "title": "Effective skin cancer classification by modified and optimized inception-ResNet-V2 model.",
      "journal": "Sci Rep",
      "first_author": "Umapathi K"
     },
     "41241764": {
      "year": "2026",
      "title": "Neural Controlled Differential Equation and Its Application in Pharmacokinetics and Pharmacodynamics.",
      "journal": "CPT Pharmacometrics Syst Pharmacol",
      "first_author": "Wu Z"
     },
     "41193653": {
      "year": "2025",
      "title": "End-to-end CNN-based detection of permanent first molars and prediction of root development stages from panoramic radiographs.",
      "journal": "Sci Rep",
      "first_author": "Kayaci ST"
     },
     "41129968": {
      "year": "2026",
      "title": "PerturbSynX: Deep learning framework for predicting drug combination synergy scores using drug induced gene perturbation data.",
      "journal": "Comput Biol Chem",
      "first_author": "C A H"
     },
     "41023344": {
      "year": "2025",
      "title": "Enhancing SDN security with deep learning and F-balanced cross-entropy for DDoS detection.",
      "journal": "Sci Rep",
      "first_author": "Naeen HM"
     },
     "40989412": {
      "year": "2025",
      "title": "Research on furniture image classification based on MobileNetNAK.",
      "journal": "PeerJ Comput Sci",
      "first_author": "Zhang D"
     },
     "40854933": {
      "year": "2025",
      "title": "Bayesian optimized CNN ensemble for efficient potato blight detection using fuzzy image enhancement.",
      "journal": "Sci Rep",
      "first_author": "Jain A"
     },
     "40839497": {
      "year": "2025",
      "title": "Adaptive Learning Rate Methods for Complex-Valued Neural Networks.",
      "journal": "IEEE Trans Neural Netw Learn Syst",
      "first_author": "Mayer KS"
     },
     "40783578": {
      "year": "2025",
      "title": "Deep learning model for early acute lymphoblastic leukemia detection using microscopic images.",
      "journal": "Sci Rep",
      "first_author": "Anand V"
     },
     "40587439": {
      "year": "2025",
      "title": "Augmented Multimodal Fusion for Optimized Brain Tumor Detection: Evaluation and Comparative Analysis.",
      "journal": "J Vis Exp",
      "first_author": "Tuteja P"
     },
     "40310446": {
      "year": "2025",
      "title": "Improving TMJ Diagnosis: A Deep Learning Approach for Detecting Mandibular Condyle Bone Changes.",
      "journal": "Diagnostics (Basel)",
      "first_author": "Azlağ Pekince K"
     },
     "39381813": {
      "year": "2024",
      "title": "Brain tumor classification using fine-tuned transfer learning models on magnetic resonance imaging (MRI) images.",
      "journal": "Digit Health",
      "first_author": "Rasa SM"
     },
     "39224246": {
      "year": "2024",
      "title": "Leveraging electrocardiography signals for deep learning-driven cardiovascular disease classification model.",
      "journal": "Heliyon",
      "first_author": "Alqahtani H"
     },
     "38434054": {
      "year": "2024",
      "title": "Optimizing home energy management: Robust and efficient solutions powered by attention networks.",
      "journal": "Heliyon",
      "first_author": "Nutakki M"
     },
     "38259883": {
      "year": "2024",
      "title": "Indoor surface classification for mobile robots.",
      "journal": "PeerJ Comput Sci",
      "first_author": "Demirtaş A"
     },
     "38228639": {
      "year": "2024",
      "title": "An intelligent LinkNet-34 model with EfficientNetB7 encoder for semantic segmentation of brain tumor.",
      "journal": "Sci Rep",
      "first_author": "Sulaiman A"
     },
     "38195582": {
      "year": "2024",
      "title": "Using meta-analysis and CNN-NLP to review and classify the medical literature for normal tissue complication probability in head and neck cancer.",
      "journal": "Radiat Oncol",
      "first_author": "Lee TF"
     },
     "38031738": {
      "year": "2024",
      "title": "Signal-to-noise ratio to assess magnitude, kinetics and impact on pharmacokinetics of the immune response to an adalimumab biosimilar.",
      "journal": "Bioanalysis",
      "first_author": "Guerrieri D"
     },
     "37960498": {
      "year": "2023",
      "title": "Automatic Calibration of Microscopic Traffic Simulation Models Using Artificial Neural Networks.",
      "journal": "Sensors (Basel)",
      "first_author": "Daguano RF"
     },
     "37895472": {
      "year": "2023",
      "title": "An Intelligent Attention-Based Transfer Learning Model for Accurate Differentiation of Bone Marrow Stains to Diagnose Hematological Disorder.",
      "journal": "Life (Basel)",
      "first_author": "Alshahrani H"
     },
     "37823024": {
      "year": "2023",
      "title": "Enhancing accuracy in brain stroke detection: Multi-layer perceptron with Adadelta, RMSProp and AdaMax optimizers.",
      "journal": "Front Bioeng Biotechnol",
      "first_author": "Uppal M"
     },
     "37679099": {
      "year": "2023",
      "title": "A hybrid deep learning-based intrusion detection system for IoT networks.",
      "journal": "Math Biosci Eng",
      "first_author": "Khan NW"
     },
     "37089598": {
      "year": "2023",
      "title": "A two-stage renal disease classification based on transfer learning with hyperparameters optimization.",
      "journal": "Front Med (Lausanne)",
      "first_author": "Badawy M"
     },
     "36765839": {
      "year": "2023",
      "title": "Hyperparameter Optimizer with Deep Learning-Based Decision-Support Systems for Histopathological Breast Cancer Diagnosis.",
      "journal": "Cancers (Basel)",
      "first_author": "Obayya M"
     },
     "36085670": {
      "year": "2022",
      "title": "Comparing Prediction of Early TBI Mortality with Multilayer Perceptron Neural Network and Convolutional Neural Network.",
      "journal": "Annu Int Conf IEEE Eng Med Biol Soc",
      "first_author": "Guimaraes KAA"
     },
     "36003775": {
      "year": "2022",
      "title": "SkinNet-16: A deep learning approach to identify benign and malignant skin lesions.",
      "journal": "Front Oncol",
      "first_author": "Ghosh P"
     },
     "35632016": {
      "year": "2022",
      "title": "A New Intrusion Detection System for the Internet of Things via Deep Convolutional Neural Network and Feature Engineering.",
      "journal": "Sensors (Basel)",
      "first_author": "Ullah S"
     },
     "35535200": {
      "year": "2022",
      "title": "Emotion Analysis Model of Microblog Comment Text Based on CNN-BiLSTM.",
      "journal": "Comput Intell Neurosci",
      "first_author": "Li A"
     },
     "33643406": {
      "year": "2021",
      "title": "Automatic Impervious Surface Area Detection Using Image Texture Analysis and Neural Computing Models with Advanced Optimizers.",
      "journal": "Comput Intell Neurosci",
      "first_author": "Hoang ND"
     },
     "33195909": {
      "year": "2020",
      "title": "Mine Gas Concentration Forecasting Model Based on an Optimized BiGRU Network.",
      "journal": "ACS Omega",
      "first_author": "Liang R"
     },
     "32906742": {
      "year": "2020",
      "title": "Comparative Analysis of Machine Learning Models for Nanofluids Viscosity Assessment.",
      "journal": "Nanomaterials (Basel)",
      "first_author": "Shateri M"
     },
     "32635409": {
      "year": "2020",
      "title": "State-of-the-Art CNN Optimizer for Brain Tumor Segmentation in Magnetic Resonance Images.",
      "journal": "Brain Sci",
      "first_author": "Yaqub M"
     },
     "30963340": {
      "year": "2019",
      "title": "Diagnosis of Autism Spectrum Disorders in Young Children Based on Resting-State Functional Magnetic Resonance Imaging Data Using Convolutional Neural Networks.",
      "journal": "J Digit Imaging",
      "first_author": "Aghdam MA"
     }
    }
   },
   {
    "query": "Adamax[tiab]",
    "count": 39,
    "ids": [
     "42236259",
     "42151246",
     "42151239",
     "41803168",
     "41789888",
     "41326571",
     "41266780",
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     "41193653",
     "41129968",
     "41023344",
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     "40854933",
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     "38434054",
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     "38228639",
     "38195582",
     "38031738",
     "37960498",
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     "37823024",
     "37679099",
     "37089598",
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     "36085670",
     "36003775",
     "35632016",
     "35535200",
     "33643406",
     "33195909",
     "32906742",
     "32635409",
     "30963340"
    ],
    "translation": "\"Adamax\"[Title/Abstract]"
   },
   {
    "query": "Adamax[tiab] AND (peptide[tiab] OR Semax[tiab] OR nootropic[tiab] OR ACTH[tiab] OR adamantane[tiab])",
    "count": 0,
    "ids": [],
    "translation": "\"Adamax\"[Title/Abstract] AND (\"peptide\"[Title/Abstract] OR \"Semax\"[Title/Abstract] OR \"nootropic\"[Title/Abstract] OR \"ACTH\"[Title/Abstract] OR \"adamantane\"[Title/Abstract])"
   },
   {
    "query": "adamantyl[tiab] AND Semax[tiab]",
    "count": 0,
    "ids": [],
    "translation": "\"adamantyl\"[Title/Abstract] AND \"Semax\"[Title/Abstract]"
   },
   {
    "query": "adamantane[tiab] AND Semax[tiab]",
    "count": 0,
    "ids": [],
    "translation": "\"adamantane\"[Title/Abstract] AND \"Semax\"[Title/Abstract]"
   },
   {
    "query": "\"Ac-MEHFPGP\"[tiab]",
    "count": 0,
    "ids": [],
    "translation": "\"Ac-MEHFPGP\"[tiab]"
   },
   {
    "query": "\"N-acetyl Semax\"[tiab]",
    "count": 0,
    "ids": [],
    "translation": "\"N-acetyl Semax\"[tiab]"
   },
   {
    "query": "Semax[tiab] OR \"Met-Glu-His-Phe-Pro-Gly-Pro\"[tiab]",
    "count": 215,
    "ids": [
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     "42885627",
     "42752426",
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     "24385169",
     "23888777",
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     "22096989",
     "21953224",
     "21476267",
     "21268834",
     "21113455"
    ],
    "translation": "\"Semax\"[Title/Abstract] OR \"Met-Glu-His-Phe-Pro-Gly-Pro\"[Title/Abstract]"
   },
   {
    "query": "Semax[tiab] AND (analog*[tiab] OR derivative*[tiab] OR acetyl*[tiab] OR modified[tiab])",
    "count": 73,
    "ids": [
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     "41479572",
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     "23113251",
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     "22708068",
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     "18431004",
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     "17353092",
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     "16523722",
     "16362768",
     "16212268",
     "15895862",
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     "15354819",
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     "12918351",
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     "12447470",
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     "11245825",
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     "10741256",
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     "11517472",
     "8974577",
     "8924838",
     "8392718",
     "1851003"
    ],
    "translation": "\"Semax\"[Title/Abstract] AND (\"analog*\"[Title/Abstract] OR \"derivative*\"[Title/Abstract] OR \"acetyl*\"[Title/Abstract] OR \"modified\"[Title/Abstract])"
   },
   {
    "query": "\"Peptide 021\"[tiab] OR P021[tiab] OR \"Ac-DGGLAG-NH2\"[tiab]",
    "count": 31,
    "ids": [
     "41945082",
     "41740658",
     "39592934",
     "38515714",
     "38468256",
     "37042028",
     "36291618",
     "34057082",
     "33566495",
     "32854771",
     "31813141",
     "31803044",
     "31339938",
     "33072980",
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     "28132216",
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     "27400746",
     "27083660",
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     "24702821",
     "24134156",
     "22678720",
     "20600002",
     "12031783",
     "1728415"
    ],
    "translation": "\"Peptide 021\"[Title/Abstract] OR \"P021\"[Title/Abstract] OR \"Ac-DGGLAG-NH2\"[Title/Abstract]"
   }
  ],
  "europepmc": [
   {
    "query": "Adamax AND Semax",
    "count": 0,
    "muestra": []
   },
   {
    "query": "\"Ac-MEHFPGP\"",
    "count": 0,
    "muestra": []
   },
   {
    "query": "MEHFPGP AND adamant*",
    "count": 0,
    "muestra": []
   },
   {
    "query": "Adamax AND (peptide OR nootropic OR melanocortin OR BDNF) AND SRC:MED",
    "count": 19,
    "muestra": [
     [
      "MED",
      "40394056",
      "2025",
      "Heuristic optimization in classification atoms in molecules using GCN via uniform simulate"
     ],
     [
      "MED",
      "40730777",
      "2025",
      "A hybrid filtering and deep learning approach for early Alzheimer's disease identification"
     ],
     [
      "MED",
      "41472108",
      "2025",
      "Host-Microbe Interactions: Prospects of Machine Learning and Deep Learning Technologies in"
     ],
     [
      "MED",
      "40756902",
      "2025",
      "IDRdecoder: a machine learning approach for rational drug discovery toward intrinsically d"
     ],
     [
      "MED",
      "39527593",
      "2024",
      "A modular protein language modelling approach to immunogenicity prediction."
     ]
    ]
   },
   {
    "query": "(adamantane OR adamantyl OR adamantan*) AND (Semax OR \"ACTH(4-10)\" OR \"ACTH 4-10\" OR \"ACTH(4-7)\")",
    "count": 3,
    "muestra": [
     [
      "MED",
      "41048238",
      "2025",
      "\"Brain doping\" substances: prohibited or not in sports?"
     ],
     [
      "PMC",
      "PMC7167816",
      "2006",
      "Poster Presentation Abstracts"
     ],
     [
      "PMC",
      "PMC7167823",
      "2008",
      "Poster Presentations"
     ]
    ]
   },
   {
    "query": "Adamax AND SRC:PPR",
    "count": 19,
    "muestra": [
     [
      "PPR",
      "PPR1344436",
      "2026",
      "Investigating the Effects of Batch Normalization on Multi-Level Image Classification"
     ],
     [
      "PPR",
      "PPR953946",
      "2024",
      "Artificial Intelligence Based Fine Tuning Model Based Fall Activity Recognition System for"
     ],
     [
      "PPR",
      "PPR1202529",
      "2024",
      "The Data-Optimized Oblique Mercator Projection"
     ],
     [
      "PPR",
      "PPR1094120",
      "2025",
      "Ontology pre-training improves machine learning-based predictions for metabolites"
     ],
     [
      "PPR",
      "PPR642953",
      "2023",
      "Improved Adaptive Momentum Method"
     ]
    ]
   },
   {
    "query": "Semax AND SRC:PPR",
    "count": 4,
    "muestra": [
     [
      "PPR",
      "PPR1329822",
      "2026",
      "Increasing Direct-to-Consumer Use of Semax and Selank Peptides Decoded from LLM-Curated Cl"
     ],
     [
      "PPR",
      "PPR1109934",
      "2025",
      "Effect of ACTH4-10Pro8-Gly9-Pro10 on anti-inflammatory cytokine (IL-4, IL-10, IL-13) expre"
     ],
     [
      "PPR",
      "PPR618727",
      "2023",
      "Effect of ACTH4-10Pro8-Gly9-Pro10 on anti-inflammatory cytokine (IL-4, IL-10, IL-13) expre"
     ],
     [
      "PPR",
      "PPR245064",
      "2020",
      "SequenceBouncer: A method to remove outlier entries from a multiple sequence alignment"
     ]
    ]
   },
   {
    "query": "Adamax AND (Semax OR nootropic OR peptide) AND SRC:PPR",
    "count": 0,
    "muestra": []
   }
  ],
  "clinicaltrials_gov": {
   "dataTimestamp": "2026-10-23T09:00:04",
   "busquedas": [
    {
     "param": "query.term",
     "query": "Adamax",
     "count": 0,
     "ids": []
    },
    {
     "param": "query.intr",
     "query": "Adamax",
     "count": 0,
     "ids": []
    },
    {
     "param": "query.term",
     "query": "Adamax peptide",
     "count": 0,
     "ids": []
    },
    {
     "param": "query.term",
     "query": "Ac-MEHFPGP",
     "count": 0,
     "ids": []
    },
    {
     "param": "query.term",
     "query": "adamantane Semax",
     "count": 0,
     "ids": []
    },
    {
     "param": "query.term",
     "query": "Semax",
     "count": 0,
     "ids": []
    },
    {
     "param": "query.intr",
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     "count": 0,
     "ids": []
    },
    {
     "param": "query.cond",
     "query": "Semax",
     "count": 0,
     "ids": []
    },
    {
     "param": "query.term",
     "query": "Selank",
     "count": 10,
     "ids": [
      "NCT00464269",
      "NCT05832060",
      "NCT05076045",
      "NCT00504881",
      "NCT05411185"
     ]
    },
    {
     "param": "query.term",
     "query": "Peptide 021",
     "count": 54,
     "ids": [
      "NCT04064411",
      "NCT01319773",
      "NCT06275061",
      "NCT00084604",
      "NCT00673790"
     ]
    },
    {
     "param": "query.term",
     "query": "P021 CNTF peptide",
     "count": 0,
     "ids": []
    }
   ],
   "nota": "'Peptide 021' (54) tambien es ruido: CT.gov expande '021' como numero de protocolo. Las 6 coincidencias de 'Met-Glu-His-Phe-Pro-Gly-Pro' (query.term) y las de 'Selank' son ruido de texto (cetonas, preacondicionamiento isquemico, etc.), no ensayos de Semax/Selank."
  },
  "google_patents": [
   {
    "query": "\"Adamax\" peptide",
    "fuente": "registro manual de sesion",
    "count": 45,
    "nota": "todo homonimo (optimizador AdaMax, ML/genomica)"
   },
   {
    "query": "Adamax Semax",
    "fuente": "registro manual de sesion",
    "count": 0
   },
   {
    "query": "Semax adamantane",
    "fuente": "registro manual de sesion",
    "count": 5,
    "nota": "RU2460529C1 (Semax solo como comparador), US20060270742A1, EP4404912A1 (Ferring, ACTH), UA51684U, US20260146254A1: ninguno describe Adamax"
   },
   {
    "query": "Semax adamantyl",
    "fuente": "registro manual de sesion",
    "count": 6,
    "nota": "mismos resultados; ninguno describe Adamax"
   },
   {
    "query": "\"MEHFPGP\" adamantane",
    "fuente": "registro manual de sesion",
    "count": 0
   },
   {
    "query": "Ac-DGGLAG-NH2",
    "fuente": "registro manual de sesion",
    "count": 6,
    "nota": "incluye US9327011B2 (Research Foundation for Mental Hygiene, prioridad 2007-03-16, neurotrophic peptides/P021)"
   },
   {
    "query": "adamantane neurotrophic peptide ciliary neurotrophic factor Wanka Iqbal",
    "fuente": "registro manual de sesion",
    "count": 4,
    "nota": "US20110160141A1, US20130157946A1, US20130143809A1 (2007-03-16) y US20130165625A1 (Giessen/Wanka, prioridad 2010-06-22, 3-aminoadamantane-1-carboxylic acid peptides; abandonada). Texto de US20130165625A1: 0 menciones de MEHFPGP/Semax/ACTH"
   }
  ],
  "cyberleninka": [
   {
    "query": "адамакс",
    "found": 2,
    "muestra": [
     [
      2023,
      "Применение нейросетевого моделирования в задачах прогнозирования уровня паводка "
     ],
     [
      2010,
      "Направления повышения конкурентоспособности информационных технологий в России"
     ]
    ]
   },
   {
    "query": "Адамакс семакс",
    "found": 0,
    "muestra": []
   },
   {
    "query": "семакс адамантан",
    "found": 8,
    "muestra": [
     [
      2022,
      "СРАВНИТЕЛЬНОЕ ИЗУЧЕНИЕ ВЛИЯНИЯ ПРОИЗВОДНЫХ <b>АДАМАНТАНА</b> НА ПОВЕДЕНИЕ МЫШЕЙ "
     ],
     [
      2016,
      "ВЛИЯНИЕ ПОТЕНЦИАЛЬНОГО НЕЙРОПРОТЕКТОРА ACETYL-(D-LYS)-LYS-ARG-ARG-AMIDE (КК-1) Н"
     ],
     [
      2019,
      "Амантадин при болезни Гентингтона: pros and cons"
     ]
    ]
   },
   {
    "query": "Adamax Semax",
    "found": 0,
    "muestra": []
   },
   {
    "query": "Semax",
    "found": 260,
    "muestra": [
     [
      2021,
      "CORRECTION OF HYPERTENSIVE RETINAL CHANGES IN RATS WITH <b>SEMAX</b>"
     ],
     [
      2019,
      "Selank and <b>Semax</b> as potential hepatoprotectors in Medical treatment of tu"
     ],
     [
      2012,
      "Correction of long-lasting negative effects of neonatal isolation in white rats "
     ]
    ]
   },
   {
    "query": "семакс",
    "found": 1000,
    "muestra": [
     [
      2026,
      "ЭФФЕКТ И ЗНАЧЕНИЕ НООТРОПОВ НА ПРИМЕРЕ «<b>СЕМАКС</b>»"
     ],
     [
      2014,
      "Результаты исследования нейропротекторной эффективности препарата «<b>Семакс</b>"
     ],
     [
      2017,
      "Адаптогенная активность <b>семакса</b> и селанка: экспериментальное исследование"
     ]
    ]
   }
  ],
  "elibrary": {
   "estado": "No consultado: elibrary.ru devuelve HTTP 403 a curl; no se elude.",
   "query_pendiente": [
    "адамакс",
    "семакс адамантан"
   ]
  },
  "grls_ru": {
   "estado": "No consultado: el formulario del GRLS (grls.minzdrav.gov.ru) exige codigo de imagen (CAPTCHA); no se elude."
  },
  "contexto_no_es_adamax": {
   "nota": "Todo esto trata Semax, N-acetil-Semax, otros analogos, P021 o adamantano en general. No es literatura de Adamax.",
   "registros": {
    "16996037": {
     "year": "2006",
     "title": "Semax, an analog of ACTH(4-10) with cognitive effects, regulates BDNF and trkB expression in the rat hippocampus.",
     "journal": "Brain Res",
     "first_author": "Dolotov OV"
    },
    "16635254": {
     "year": "2006",
     "title": "Semax, an analogue of adrenocorticotropin (4-10), binds specifically and increases levels of brain-derived neurotrophic factor protein in rat basal forebrain.",
     "journal": "J Neurochem",
     "first_author": "Dolotov OV"
    },
    "1851003": {
     "year": "1991",
     "title": "N-terminal degradation of ACTH(4-10) and its synthetic analog semax by the rat blood enzymes.",
     "journal": "Biochem Biophys Res Commun",
     "first_author": "Potaman VN"
    },
    "8392718": {
     "year": "1993",
     "title": "Degradation of ACTH/MSH(4-10) and its synthetic analog semax by rat serum enzymes: an inhibitor study.",
     "journal": "Peptides",
     "first_author": "Potaman VN"
    },
    "11443939": {
     "year": "2001",
     "title": "[Semax and selank inhibit the enkephalin-degrading enzymes from human serum]].",
     "journal": "Bioorg Khim",
     "first_author": "Kost NV"
    },
    "27586814": {
     "year": "2016",
     "title": "Influence of the N-terminus acetylation of Semax, a synthetic analog of ACTH(4-10), on copper(II) and zinc(II) coordination and biological properties.",
     "journal": "J Inorg Biochem",
     "first_author": "Magrì A"
    },
    "23652441": {
     "year": "2013",
     "title": "Stability of Semax acetyl to proteolysis in various biological media.",
     "journal": "Dokl Biol Sci",
     "first_author": "Shevchenko KV"
    },
    "16212268": {
     "year": "2005",
     "title": "[Effect of modification of the N-terminal region of molecule on the expression of neotropic effect of semax analogues].",
     "journal": "Izv Akad Nauk Ser Biol",
     "first_author": "Glazova NIu"
    },
    "23821053": {
     "year": "2013",
     "title": "Study of proteolysis of Semax analogues with different N-terminal amino acids by carboxypeptidases.",
     "journal": "Dokl Biol Sci",
     "first_author": "Shevchenko KV"
    },
    "22096989": {
     "year": "2011",
     "title": "[Proteolysis of semax analogues with different N-terminal amino acids by aminopeptidases].",
     "journal": "Bioorg Khim",
     "first_author": "Shevchenko KV"
    },
    "39442746": {
     "year": "2024",
     "title": "Antidepressant-like and antistress effects of the ACTH(4-10) synthetic analogs Semax and Melanotan II on male rats in a model of chronic unpredictable stress.",
     "journal": "Eur J Pharmacol",
     "first_author": "Inozemtseva LS"
    },
    "34576218": {
     "year": "2021",
     "title": "Antistress Action of Melanocortin Derivatives Associated with Correction of Gene Expression Patterns in the Hippocampus of Male Rats Following Acute Stress.",
     "journal": "Int J Mol Sci",
     "first_author": "Filippenkov IB"
    },
    "32580520": {
     "year": "2020",
     "title": "Novel Insights into the Protective Properties of ACTH((4-7))PGP (Semax) Peptide at the Transcriptome Level Following Cerebral Ischaemia-Reperfusion in Rats.",
     "journal": "Genes (Basel)",
     "first_author": "Filippenkov IB"
    },
    "41479572": {
     "year": "2025",
     "title": "The Potential of the Peptide Drug Semax and Its Derivative for Correcting Pathological Impairments in the Animal Model of Alzheimer's Disease.",
     "journal": "Acta Naturae",
     "first_author": "Radchenko AI"
    },
    "40496623": {
     "year": "2025",
     "title": "Semax, a Copper Chelator Peptide, Decreases the Cu(II)-Catalyzed ROS Production and Cytotoxicity of aβ by Metal Ion Stripping and Redox Silencing.",
     "journal": "Bioinorg Chem Appl",
     "first_author": "Tomasello MF"
    },
    "29798983": {
     "year": "2018",
     "title": "[The efficacy of semax in the tretament of patients at different stages of ischemic stroke].",
     "journal": "Zh Nevrol Psikhiatr Im S S Korsakova",
     "first_author": "Gusev EI"
    },
    "11517472": {
     "year": "1997",
     "title": "[Effectiveness of semax in acute period of hemispheric ischemic stroke (a clinical and electrophysiological study)].",
     "journal": "Zh Nevrol Psikhiatr Im S S Korsakova",
     "first_author": "Gusev EI"
    },
    "20600002": {
     "year": "2010",
     "title": "Neurotrophic peptides incorporating adamantane improve learning and memory, promote neurogenesis and synaptic plasticity in mice.",
     "journal": "FEBS Lett",
     "first_author": "Li B"
    },
    "24702821": {
     "year": "2014",
     "title": "Rescue of cognitive-aging by administration of a neurogenic and/or neurotrophic compound.",
     "journal": "Neurobiol Aging",
     "first_author": "Bolognin S"
    },
    "25046994": {
     "year": "2014",
     "title": "Disease modifying effect of chronic oral treatment with a neurotrophic peptidergic compound in a triple transgenic mouse model of Alzheimer's disease.",
     "journal": "Neurobiol Dis",
     "first_author": "Kazim SF"
    },
    "23432396": {
     "year": "2013",
     "title": "The lipophilic bullet hits the targets: medicinal chemistry of adamantane derivatives.",
     "journal": "Chem Rev",
     "first_author": "Wanka L"
    },
    "41048238": {
     "year": "2025",
     "title": "\"Brain doping\" substances: prohibited or not in sports?",
     "journal": "Biol Sport",
     "first_author": "Pokrywka A"
    }
   }
  }
 },
 "tables": {
  "identity": "Table 1 in page",
  "claims": [
   [
    "1",
    "It is acetylated Semax capped with the adamantyl residue of P21",
    "No source for the combination; the adamantyl residue itself is documented for P021<sup><a href=\"#ref-2\">2</a></sup>"
   ],
   [
    "2",
    "It is the most potent Semax derivative",
    "No source"
   ],
   [
    "3",
    "It raises BDNF and TrkB signalling",
    "By analogy only: Semax in rat hippocampus<sup><a href=\"#ref-6\">6</a></sup>"
   ],
   [
    "4",
    "The adamantane group improves lipophilicity, brain penetration and enzymatic stability",
    "By analogy only: general adamantane chemistry<sup><a href=\"#ref-11\">11</a></sup>; no data for this peptide"
   ],
   [
    "5",
    "N-acetylation improves stability and brain penetration",
    "Partly contradicted: acetylation changed metal binding and removed a protective effect in cells<sup><a href=\"#ref-8\">8</a></sup>; no penetration data"
   ],
   [
    "6",
    "It is more potent than Semax",
    "No source"
   ],
   [
    "7",
    "It inherits Semax’s decades of research and safety record",
    "Contradicted: end modifications change effects<sup><a href=\"#ref-12\">12</a></sup>; Adamax has no literature"
   ],
   [
    "8",
    "It has been studied preclinically or clinically",
    "Contradicted: Table 2"
   ],
   [
    "9",
    "It comes from Russian research or is approved in Russia",
    "Contradicted for Adamax: the Russian medicine is Semax; Adamax is attributed to a vendor<sup><a href=\"#ref-4\">4</a></sup>"
   ],
   [
    "10",
    "It is unregulated",
    "Partly contradicted: named by Medsafe in a pending classification proposal<sup><a href=\"#ref-13\">13</a></sup>"
   ],
   [
    "11",
    "Adamantylation makes it heat-stable",
    "No source"
   ],
   [
    "12",
    "It works better in combination with other peptides",
    "No source; anecdotal only"
   ],
   [
    "13",
    "Personal reports of effects",
    "Anecdotal, uncontrolled, with the identity of the product used unverifiable (Table 1)"
   ],
   [
    "14",
    "It is a chemical hypothesis rather than a tested compound",
    "Consistent with Table 2"
   ],
   [
    "15",
    "It is a deliberate, rational design",
    "No source: no design document, patent or identifiable author"
   ],
   [
    "16",
    "Research guides link Semax studies as Adamax evidence",
    "Observed pattern: real citation, transferred claim (see Vendor A, above)"
   ]
  ]
 },
 "data_dictionary": {
  "pubmed_counts": "Record counts on the cut-off date with the literal queries in census_raw",
  "claims": "[number, claim, strongest source] as in Table 4"
 },
 "vendor_pages_read_2026_10_25": {
  "Vendor A": "https://luxepeptides.is/adamax",
  "Vendor B": "https://pspeptides.com (Adamax product page)",
  "Vendor C": "https://limitlesslifenootropics.com (Adamax product page)"
 },
 "pubmed_counts_sensitivity": {
  "semax[tiab]": 210,
  "semax with sequence terms": 215,
  "P021[tiab] OR peptide 021[tiab]": "30-31"
 },
 "computed_identity": {
  "tool": "RDKit 2026.09.1",
  "date": "2026-10-25",
  "proton_mass": 1.007276467,
  "note": "Computed 2D descriptors; Crippen cLogP is an atom-additive estimate, not a measured partition coefficient, and is unreliable in absolute terms for peptides. Use for comparison between closely related structures only.",
  "molecules": [
   {
    "key": "adamax_A",
    "name": "Adamax as most often described: Ac-MEHFPGP-[3-aminoadamantane-1-carboxamide]",
    "smiles": "CSCC[C@H](NC(C)=O)C(=O)N[C@@H](CCC(=O)O)C(=O)N[C@@H](Cc1c[nH]cn1)C(=O)N[C@@H](Cc1ccccc1)C(=O)N1CCC[C@H]1C(=O)NCC(=O)N1CCC[C@H]1C(=O)NC12CC3CC(C1)CC(C(N)=O)(C3)C2",
    "formula": "C50H69N11O11S",
    "formula_source": "Wikipedia rev. 1352277748; vendor value 1,032.24",
    "mw_average": 1032.23,
    "monoisotopic": 1031.4899,
    "ions": {
     "[M+1H]1+": 1032.4971,
     "[M+2H]2+": 516.7522,
     "[M+3H]3+": 344.8372
    },
    "clogp_crippen": -0.19,
    "tpsa": 324.3,
    "hbd": 9,
    "hba": 12,
    "rotatable_bonds": 24,
    "heavy_atoms": 73
   },
   {
    "key": "adamax_B",
    "name": "Vendor B reading: Ac-MEHFPGPAG-NH2 (Ala-Gly, no adamantane)",
    "smiles": "CSCC[C@H](NC(C)=O)C(=O)N[C@@H](CCC(=O)O)C(=O)N[C@@H](Cc1c[nH]cn1)C(=O)N[C@@H](Cc1ccccc1)C(=O)N1CCC[C@H]1C(=O)NCC(=O)N1CCC[C@H]1C(=O)N[C@@H](C)C(=O)NCC(N)=O",
    "formula": "C44H62N12O12S",
    "formula_source": "own calculation from the stated sequence",
    "mw_average": 983.12,
    "monoisotopic": 982.4331,
    "ions": {
     "[M+1H]1+": 983.4404,
     "[M+2H]2+": 492.2238,
     "[M+3H]3+": 328.485
    },
    "clogp_crippen": -3.02,
    "tpsa": 353.4,
    "hbd": 10,
    "hba": 13,
    "rotatable_bonds": 26,
    "heavy_atoms": 69
   },
   {
    "key": "adamax_C",
    "name": "Vendor C stated sequence: Ac-MEHFPGPAG-OH (free acid)",
    "smiles": "CSCC[C@H](NC(C)=O)C(=O)N[C@@H](CCC(=O)O)C(=O)N[C@@H](Cc1c[nH]cn1)C(=O)N[C@@H](Cc1ccccc1)C(=O)N1CCC[C@H]1C(=O)NCC(=O)N1CCC[C@H]1C(=O)N[C@@H](C)C(=O)NCC(=O)O",
    "formula": "C44H61N11O13S",
    "formula_source": "own calculation from the stated sequence",
    "mw_average": 984.1,
    "monoisotopic": 983.4171,
    "ions": {
     "[M+1H]1+": 984.4244,
     "[M+2H]2+": 492.7158,
     "[M+3H]3+": 328.813
    },
    "clogp_crippen": -2.42,
    "tpsa": 347.6,
    "hbd": 10,
    "hba": 13,
    "rotatable_bonds": 26,
    "heavy_atoms": 69
   },
   {
    "key": "semax",
    "name": "Semax: H-MEHFPGP-OH",
    "smiles": "CSCC[C@H](N)C(=O)N[C@@H](CCC(=O)O)C(=O)N[C@@H](Cc1c[nH]cn1)C(=O)N[C@@H](Cc1ccccc1)C(=O)N1CCC[C@H]1C(=O)NCC(=O)N1CCC[C@H]1C(=O)O",
    "formula": "C37H51N9O10S",
    "formula_source": "PubChem CID 9811102",
    "mw_average": 813.93,
    "monoisotopic": 813.348,
    "ions": {
     "[M+1H]1+": 814.3552,
     "[M+2H]2+": 407.6813,
     "[M+3H]3+": 272.1233
    },
    "clogp_crippen": -1.22,
    "tpsa": 286.3,
    "hbd": 8,
    "hba": 11,
    "rotatable_bonds": 21,
    "heavy_atoms": 57
   },
   {
    "key": "ac_semax_nh2",
    "name": "N-acetyl Semax amidate: Ac-MEHFPGP-NH2",
    "smiles": "CSCC[C@H](NC(C)=O)C(=O)N[C@@H](CCC(=O)O)C(=O)N[C@@H](Cc1c[nH]cn1)C(=O)N[C@@H](Cc1ccccc1)C(=O)N1CCC[C@H]1C(=O)NCC(=O)N1CCC[C@H]1C(N)=O",
    "formula": "C39H54N10O10S",
    "formula_source": "PubChem CID 172638603",
    "mw_average": 854.99,
    "monoisotopic": 854.3745,
    "ions": {
     "[M+1H]1+": 855.3818,
     "[M+2H]2+": 428.1945,
     "[M+3H]3+": 285.7988
    },
    "clogp_crippen": -1.64,
    "tpsa": 295.2,
    "hbd": 8,
    "hba": 11,
    "rotatable_bonds": 22,
    "heavy_atoms": 60
   },
   {
    "key": "p021",
    "name": "Peptide 021: Ac-DGGL-[3-aminoadamantane-1-carboxamide]",
    "smiles": "CC(=O)N[C@@H](CC(=O)O)C(=O)NCC(=O)NCC(=O)N[C@@H](CC(C)C)C(=O)NC12CC3CC(C1)CC(C(N)=O)(C3)C2",
    "formula": "C27H42N6O8",
    "formula_source": "PubChem CID 56589645",
    "mw_average": 578.67,
    "monoisotopic": 578.3064,
    "ions": {
     "[M+1H]1+": 579.3137,
     "[M+2H]2+": 290.1605,
     "[M+3H]3+": 193.7761
    },
    "clogp_crippen": -1.33,
    "tpsa": 225.9,
    "hbd": 7,
    "hba": 7,
    "rotatable_bonds": 14,
    "heavy_atoms": 41
   },
   {
    "key": "gb115",
    "name": "GB-115: N-(6-phenylhexanoyl)glycyl-L-tryptophan amide",
    "smiles": "NC(=O)[C@H](Cc1c[nH]c2ccccc12)NC(=O)CNC(=O)CCCCCc1ccccc1",
    "formula": "C25H30N4O3",
    "formula_source": "PubChem (census.json)",
    "mw_average": 434.54,
    "monoisotopic": 434.2318,
    "ions": {
     "[M+1H]1+": 435.2391,
     "[M+2H]2+": 218.1232,
     "[M+3H]3+": 145.7512
    },
    "clogp_crippen": 2.6,
    "tpsa": 117.1,
    "hbd": 4,
    "hba": 3,
    "rotatable_bonds": 12,
    "heavy_atoms": 32
   },
   {
    "key": "osavampator",
    "name": "Osavampator (TAK-653)",
    "smiles": "CC1=CN2CCS(=O)(=O)N=C2C(c2ccc(OC3CCCCC3)cc2)=N1",
    "formula": "C19H23N3O3S",
    "formula_source": "PubChem CID 56655833",
    "mw_average": 373.48,
    "monoisotopic": 373.146,
    "ions": {
     "[M+1H]1+": 374.1533,
     "[M+2H]2+": 187.5803,
     "[M+3H]3+": 125.3893
    },
    "clogp_crippen": 3.11,
    "tpsa": 71.3,
    "hbd": 0,
    "hba": 5,
    "rotatable_bonds": 3,
    "heavy_atoms": 26
   }
  ]
 }
}