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🏷️ Tags (8587 usages)
⚗️ Metals 2487
▸ Metals — Platinum (109)
apoptosis (297)Pt (214)pt (24)ferroptosis (22)oxaliplatin (21)cisplatin (21)pyroptosis (7)necroptosis (6)transcription (6)carboplatin (5)transcription factors (5)transcriptional regulation (5)platinum (4)lead optimization (3)transcription regulation (3)metabolic adaptation (3)pt(ii) complexes (2)transcriptional regulatory interactions (2)ferroptosis induction (2)transcription initiation (2)transcription-coupled repair (2)adaptive binding (2)cellular adaptation (2)post-transcriptional regulation (2)pt(dach)methionine (1)transcription-coupled nucleotide excision repair (tc-ner) (1)triptolide (1)molecular optimization (1)pt(dach)cl4 (1)innate apoptotic immunity (1)pta (1)oligopeptides (1)transcription-coupled ner (1)ferroptosis suppressor protein 1 (fsp1) (1)apoptotic cells (1)platinumbased (1)hptab (1)signaling-transcriptional mechanisms (1)oncogene transcription inhibition (1)pt2 (1)admet optimization (1)receptor (1)pten (1)platinum(ii) (1)chain-of-thought prompt engineering (1)tetrapeptides (1)apoptotic function (1)adaptive immune response (1)gpt-2 (1)platinum drugs (1)ptii complex (1)platinum complexes (1)transcriptomics (1)cell metabolism disruption (1)peptide (1)pt(s,s-dab) (1)pt(r,r-dab) (1)pt3(hptab) (1)estrogen receptor (1)transcriptional addiction (1)transcription stress (1)septicemia (1)optical spectroscopies (1)receptors (1)selective serotonin reuptake inhibitors (ssri) (1)transcription-coupled nucleotide excision repair (1)pt(r,r-dach) (1)chiroptical response (1)diplatinum helicate (1)cyclometalated 1,3-bis(8-quinolyl) phenyl chloroplatinum(ii) (1)transcriptional activity (1)pt1 (1)disrupting a base pair (1)platinum-containing drugs (1)gpt-4 (1)transcriptional stalling (1)transcription inhibition (1)apoptotic (1)eukaryotic transcription (1)base pairing disruption (1)apoptosis-related disorders (1)coordination chemistry is not relevant, but bioinorganic and medicinal chemistry are related concepts (1)chatgpt (1)apoptosis induction (1)platinum(ii)-based (1)transcriptional activation (1)platinum-based compounds (1)inhibition of transcription factors (1)molecular descriptors (1)pt(dach)oxalato (1)polypeptide chains (1)pt(dach)cl2 (1)glp-1 receptor agonists (1)chiroptical applications (1)pt(s,s-dach) (1)cell-penetrating peptides (1)cysteine uptake (1)therapeutic optimization (1)shape description methods (1)transcription blockage (1)antiferroptotic (1)rna transcription (1)electronic absorption (1)cellular adaptation to hypoxia (1)ferroptosis suppressor protein 1 (1)apoptosis evasion (1)phosphopeptide-based kinome analysis (1)anti-apoptotic (1)gpt (1)
▸ Metals — Cobalt (185)
coordination-chemistry (102)Co (64)coordination chemistry (55)colorectal cancer (19)computational biology (7)spectroscopy (7)computational chemistry (6)computational modeling (6)pharmacology (6)co (5)pharmacovigilance (5)cryo-electron microscopy (4)glucose (4)colon cancer (4)metal complexes (4)glycolysis (4)oncology (4)pharmacokinetics (4)conformational change (3)glycocalyx (3)oncometabolite (3)complex i (3)oncosis (3)oncogenesis (2)polypharmacology (2)in-silico (2)plant secondary metabolites (2)computational approaches (2)in silico (2)convolutional neural networks (2)complex iii (2)natural compounds (2)pharmacodynamics (2)mitochondrial complex i (2)aerobic glycolysis (2)oncogene (2)covid-19 (2)microviscosity (1)pharmacometabolomics (1)complex formation (1)redox control (1)fatty alcohols (1)influence on physicochemical properties (1)fluorescence recovery after photobleaching (1)convolutional neural network (1)conditional lethality (1)picolinic acid (1)sars-cov-1 (1)metabolic control (1)pharmacological inhibition (1)pharmacokinetic (1)therapeutic controversy (1)multicolor emission (1)co2 fixation (1)protein complex (1)oncogenes (1)recombination (1)confocal microscopy (1)metal-ligand cooperation (1)cell surface recognition (1)sarcoma (1)network pharmacology (1)covalent interaction (1)escherichia coli (1)cobalamin (1)reversible compartmentalization (1)oncogene promoter regions (1)cellular compartments (1)coulometric karl fischer apparatus (1)combinatorial treatment (1)heme-containing enzymes (1)coimmunoprecipitation assay (1)glycosphingolipids (1)comorbidities (1)glycolytic activity (1)computational metabolomics (1)conformational isomerization (1)constitutive induction (1)confocal imaging (1)alcoholic hepatitis (1)knowledge discovery (1)oncogenic mutation (1)cobaltocene (1)coordination (1)computational approach (1)inorganic compounds (1)toxicology (1)conformational stability (1)connectivity mapping (1)mitochondrial uncoupling protein 2 (1)pharmacokinetic analyses (1)membrane permeability comparison (1)computer models (1)pathological conditions (1)dna condensation (1)4-octyl-itaconate (4-oi) (1)glucose dependence (1)cockayne's syndrome (1)atomic force microscope (1)complex diseases (1)dna conformational distortion (1)computational prediction (1)health economics (1)viscometry (1)conformational transitions (1)anticoagulant (1)glycome (1)oncogenic pathways (1)mitochondrial quality control (1)spin-orbit coupling (1)cytosolic ca21 concentration (1)cobamide (1)glycobiology (1)coimmunoprecipitation (1)dual protein expansion microscopy (1)brightfield microscopy (1)complexes (1)fluorescence recovery after photobleaching (frap) (1)glucose deprivation resistance (1)physicochemical properties (1)cell-like compartments (1)expansion microscopy (1)anticoagulants (1)ascorbic acid (1)oncogenic signaling (1)collective intelligence (1)cordycepin (1)genetic encoding (1)co2 (1)coupled-cluster computations (1)atp-competitive inhibitors (1)non-covalent interaction (1)computational methods (1)conformational states (1)conformational transition (1)electronic health records (1)sars-cov-2 (1)computational models (1)pharmacodynamic (1)text encoder (1)social cognition (1)sensory nerve conduction velocity (1)covalent binding (1)oncogene-mediated cellular transformation (1)fluorescence microscopy (1)glycolysis pathway (1)electronic conductometry (1)conformational landscapes (1)inductively coupled plasma mass spectrometry (1)itaconate (1)co(terpy)2+ (1)nmr spectroscopy (1)computational analysis (1)inductively coupled plasma mass spectrometer (1)coenzyme q10 (1)cell communication (1)colony formation assay (1)physico-chemical mechanisms (1)recognition (1)glycolytic enzymes (1)systems pharmacology (1)atomic force microscopy (1)computational methodologies (1)oncogenic (1)click expansion microscopy (1)glycosylation (1)n-(2-picolyl)salicylimine (1)ewing sarcoma (1)computational study (1)anticoagulation (1)confocal laser scanning microscopy (1)immuno-oncology (1)genome conformation profiling (1)somatic comorbidities (1)uv-vis spectroscopy (1)in silico analysis (1)co-immunoprecipitation (1)caco-2 cell monolayers (1)scoping review (1)conformational switch (1)damage recognition (1)entity recognition (1)energy conversion (1)noncovalent interactions (1)computer analysis (1)
▸ Metals — Iron (60)
▸ Metals — Ruthenium (86)
Ru (41)drug discovery (27)drug-delivery (23)drug resistance (11)prodrug (9)drug-drug interactions (9)drugs (7)adverse drug reactions (7)structural biology (7)drug repurposing (6)drug delivery (5)drug (5)drug development (5)g-quadruplex dna (4)ru (4)protein structure (3)drug interactions (3)structural analysis (3)drug screening (3)drug-target interaction prediction (3)g-quadruplex (3)drug design (3)drug repositioning (2)metallodrugs (2)structural data (2)drug-target interaction (2)serum (1)structure-based virtual screening (1)recruitment (1)hexammineruthenium(iii) (1)drug testing (1)spectrum diagrams (1)drug therapy (1)drug safety monitoring (1)drug sensitivity and resistance testing (1)drug safety assessment (1)structure (1)structural insights (1)adverse drug reaction detection (1)drug sensitization (1)drug target (1)truncations (1)drug-drug interaction prediction (1)protein structure-function relationship (1)pyruvate (1)drug-drug interaction identification (1)phenotypic drug screening (1)spontaneous adverse drug reaction reports (1)structural basis (1)antiviral drug discovery (1)drug tolerance (1)green rust (1)structural modeling (1)small-molecule drugs (1)structural methods (1)drug-nutrient interactions (1)adverse drug events (1)computational drug discovery (1)metal-based drugs (1)structural rearrangement (1)protein structure analysis (1)virus (1)small-molecule oral drugs (1)targeted drug delivery (1)adverse drug reaction (1)chemical drugs (1)doxorubicin (1)drug resistance reduction (1)drug-likeness (1)drug interaction prediction (1)drug target identification (1)macromolecular structure determination (1)resorufin (1)drug interaction analysis (1)drug combinations (1)non-steroidal anti-inflammatory drugs (nsaids) (1)structural bioinformatics (1)structure prediction (1)drug response (1)drug interaction screening (1)ruthenium(ii)-based (1)drug detection (1)structure-function analysis (1)metal-based drug (1)protocellular structures (1)drug interaction identification (1)
▸ Metals — Copper (63)
▸ Metals — Gold (19)
▸ Metals — Iridium (29)
▸ Metals — Others (17)
▸ Metals — Palladium (13)
▸ Metals — Zinc (5)
▸ Metals — Other (17)
🔬 Methods 1116
▸ Methods — Other experimental (213)
synthesis (244)ML (51)docking (23)natural language processing (12)in vitro (7)in vivo (6)morphological profiling (4)literature search (4)benchmarking (4)network analysis (4)image-based profiling (3)biochemical analysis (3)text analysis (3)bibliometric analysis (3)api (2)incites (2)vosviewer (2)experimental (2)theoretical studies (2)high-throughput screening (2)sequence analysis (2)information extraction (2)pubmed (2)cck-8 assay (2)statistics (2)lectin array (2)statistical approach (2)literature review (2)genetic (2)icite (2)lectin microarray (2)semantic search (2)data visualization (1)in vivo studies (1)target-based approaches (1)permeability measurement (1)gene expression profile (1)patch clamp (1)cnns (1)knockout mouse studies (1)cpg island methylator phenotype (1)in vitro models (1)immunoblot (1)bret2 (1)preclinical models (1)graph theory (1)gnns (1)passive rheology (1)nonequilibrium sensitivity analysis (1)ex vivo (1)multilayer network integration (1)inhibition assay (1)go analysis (1)experimental data analysis (1)caspase activity (1)nct (1)esm (1)web of science (1)gene expression microarray (1)uv light exposure (1)text2sql (1)decision-making (1)short tandem repeat profiling (1)in-vitro (1)analytical determination methods (1)perturbation (1)immunospecific antibodies (1)overexpression (1)mechanistic analysis (1)nuclease digestion (1)enzymatic reaction (1)excision assay (1)nuclear magnetic resonance (not explicitly mentioned but implied through study of variants) (1)pampa assay (1)experimental studies (1)null models (1)binding studies (1)clinical analysis (1)semi-supervised learning (1)efficacy analyses (1)supervised learning (1)electric field application (1)mouse model (1)estimates (1)isothermal calorimetry (1)rational design (1)learning to rank (1)gene expression analysis (1)fluorometry (1)octanol-aqueous shake-flask method (1)polypharmacy regimens (1)predictive models (1)xr-seq (1)graph learning (1)human studies (1)in vivo lung perfusion (1)merip-seq (1)uv-detection (1)atp hydrolysis (1)clinical methods (1)data processing (1)glovebox-bound apparatus (1)hoechst 33,258 staining (1)mutational analyses (1)semantic retrieval (1)solid-phase microextraction (1)immunization (1)pathscan array (1)quantitative phase behavior (1)natural bond orbital (nbo) analysis (1)ai (1)immunological analysis (1)cellular assays (1)synthetic biology tools (1)nanotherapeutic approaches (1)splicing regulation profiling (1)genome-wide screening (1)loss-of-function screens (1)histochemical staining (1)resazurin reduction assay (1)stopped-flow ph jump experiments (1)protein language model (1)experimental validation (1)matrix factorization (1)giao method (1)multi-head attention mechanism (1)rnns (1)phase ii trial (1)calorimetry (1)high throughput screening (1)trp emission (1)self-supervised learning (1)chemocentric approach (1)graph-based learning (1)tcga analysis (1)theoretical framework (1)machine-learning algorithms (1)ablation experiments (1)boolean logic (1)guanidine hydrochloride denaturation (1)ic50 index (1)statistical analysis (1)quantification (1)ensemble learning (1)in vitro study (1)relation search (1)relation extraction (1)image segmentation (1)genetic studies (1)genome-wide analysis (1)knockdown (1)ccsd(t) (1)biochemical characterization (1)performance evaluation (1)nbo 3.1 (1)rocplotter (1)mitoplast preparation (1)cryoem (1)entity annotation (1)modeling (1)systems engineering (1)database analysis (1)radiation exposure (1)prognostic tools (1)mouse models (1)nuclear magnetic resonance (1)proximity ligation assays (1)mp2(fc)/6–311 +  + (2d,2p) (1)personalized treatments (1)ncbi e-utilities (1)gradient boosting machines (1)kegg analysis (1)genetic algorithm (1)algorithms (1)experimental design (1)system-level/network analyses (1)visualized analysis (1)aimall (1)radiotherapy (1)laboratory methods (1)displacement assay (1)electrophoretic retardation measurements (1)seahorse platform (1)normoxia (1)mixture modeling (1)high-throughput (1)experimental methods (1)slot blot (1)magnetic tweezers (1)thermal denaturation (1)global genome ner (1)genetic profiling (1)mutation analysis (1)algorithm development (1)modelling (1)cell migration assay (1)methylome profiling (1)biochemical studies (1)patch clamping (1)umbrella review (1)zotero (1)immunoblotting (1)statistical methods (1)cellular models (1)miclip (1)fluorometric assay (1)enzymatic assays (1)genetic analysis (1)photophysical (1)biomedical information retrieval (1)logistic regression (1)in-vivo (1)mutational status analysis (1)
▸ Methods — Computational (31)
▸ Methods — Crystallography / Structure (4)
▸ Methods — Cell biology (21)
▸ Methods — Spectroscopy (19)
▸ Methods — Genomics / Omics (25)
▸ Methods — Mass spec / Chromatography (6)
▸ Methods — Clinical / Epidemiology (8)
▸ Methods — Electrochemistry (5)
▸ Methods — Other (1)
🎯 Targets 980
▸ Targets — Mitochondria (15)
▸ Targets — Other (157)
protein (58)enzyme (19)heme (11)gene expression (10)nucleus (9)genome (5)cardiolipin (5)enzymes (5)are (4)nucleolus (4)genetic variants (4)tfiih (4)lipids (4)signal transduction (4)cytoplasm (4)cellular metabolism (4)cell metabolism (3)cell surface (3)ribosome (3)metalloproteins (3)cells (3)cell (3)fumarate hydratase (2)dihydroorotate dehydrogenase (2)ubiquinone (2)stress response (2)tubulin (2)cytosol (2)polysulfides (2)cytochrome c oxidase (2)xpb (2)aif (2)genes (2)ribosome biogenesis (2)chromophore (1)none (1)substrates (1)clinical notes (1)acsl4 (1)protein phosphatase 2a (1)dpscs (1)albumin (1)tissues (1)trxr (1)substrate (1)platelet aggregation (1)tbk1 (1)metabolic phenotype (1)lab results (1)intracellular ph (1)sqr (1)cellular biochemistry (1)target (1)healthy cells (1)sting (1)gene targets (1)variants (1)three-way junction (1)heme-oxygenase1 (1)ddr1 (1)cajal bodies (1)target genes (1)upr (1)mif (1)heme a3 (1)nucleic acids (1)intracellular substrates (1)hydrogen sulfide (h2s) (1)mt1-mmp (1)gene (1)plasma proteins (1)adenine (1)metabolic signatures (1)nuclear foci (1)mscs (1)caspase cascade (1)p65 (1)dna synthesis (1)ddb2 (1)nuclear factor (1)hmga2 (1)ecm (1)diseases (1)spliceosomal proteins (1)neurons (1)smn protein (1)nadh/nad(p)h (1)rtk clusters (1)reactive species (1)metal (1)translation initiation (1)ligand (1)lipid droplet (1)metabolic enzymes (1)pkcd (1)protein kinases (1)peripheral nervous system (1)stem cells (1)cellular targets (1)metalloenzyme (1)chemical reactions (1)4ebp1 (1)procaspase 3 (1)ump synthase (1)rbx1 (1)literature-based evidence (1)ras (1)metabolic biomarkers (1)guanine (1)metal centers (1)ccr7 (1)cytochrome p450 2e1 (1)cell nucleus (1)lung tissue (1)ph (1)stress granules (1)erythrocytes (1)hexokinase 2 (1)nucleic acid (1)nitrogen species (1)four-way junction (1)nucleolar protein (1)p21 (1)mek1/2 (1)membrane potential (1)polysulfides (h2sn) (1)mek (1)annexin v (1)atp production (1)actin (1)traf5 (1)tme (1)cytoskeleton (1)proteoforms (1)cell cycle (1)p47phox (1)metabolome (1)cellular (1)aldoa (1)oxidants (1)zbp1 (1)cellular machines (1)atp (1)actin filaments (1)disease network (1)lipid damage (1)focal adhesions (1)p97 (1)protein sequence (1)xpc (1)whole cell (1)p38 (1)plectin (1)plasmids (1)propidium iodide (1)nadph oxidase 1 (nox1) (1)hdac enzymes (1)
▸ Targets — Nucleic acids (44)
▸ Targets — Membrane / Transport (15)
▸ Targets — Enzymes / Kinases (18)
▸ Targets — Transcription factors (5)
🦠 Diseases 880
▸ Diseases — Cancer (69)
▸ Diseases — Other (41)
▸ Diseases — Neurodegenerative (18)
▸ Diseases — Inflammatory / Immune (6)
▸ Diseases — Metabolic (5)
▸ Diseases — Cardiovascular (6)
▸ Diseases — Hepatic / Renal (8)
⚙️ Mechanisms 800
▸ Mechanisms — ROS / Redox (65)
▸ Mechanisms — Other (96)
cell cycle arrest (16)enzyme inhibition (12)phosphorylation (5)gene expression regulation (5)cell cycle regulation (4)persulfidation (3)detoxification (3)ligand dissociation (2)sequence variants (2)mechanism of action (2)resistance (2)inactivation (2)invasion inhibition (1)er stress responses (1)hormesis (1)invasiveness (1)epithelial-to-mesenchymal transition inhibition (1)oxygen-dependent metabolism (1)aquation (1)paracellular permeability (1)translation efficiency (1)denaturation (1)sequestration (1)oxidative post-translational modification (1)lipid metabolism (1)duplex unwinding (1)unfolded protein response (1)antioxidation (1)calcium regulation (1)radical formation (1)oxidative damage (1)splicing regulation (1)cell growth arrest (1)protein destabilization (1)multivalent interactions (1)protein phosphatase 2a modulation (1)protein dislocation (1)cell growth suppression (1)proteotoxic stress (1)protein rearrangements (1)p21 translation inhibition (1)gg-ner (1)pseudohypoxia (1)hypoxic response (1)electron shuttle (1)low-barrier hydrogen bond (1)kinase inhibition (1)synthetic lethality (1)stress responses (1)mutagenesis (1)subcellular relocalization (1)weak interactions (1)proton ejection (1)metabolic fuel selection (1)posttranslational modification (1)regulatory interactions (1)proton pumps (1)genetic regulation (1)protein unfolding (1)nucleolar homeostasis (1)ligand switch (1)ribosomopathies (1)oxidation-reduction (1)induced fit (1)localization (1)genetic mutation (1)mode of action (1)nucleolar stress response (1)cell killing capacity (1)ligand exchange (1)bond breaking (1)kinase activation (1)modulation (1)diadduct formation (1)cytoskeleton modulation (1)radical-mediated reaction (1)electron self-exchange (1)protein shuttling (1)pore formation (1)cellular metabolism regulation (1)nuclear export processes (1)ion selectivity (1)cell survival suppression (1)stabilization (1)cell damage (1)mitochondrial bioenergetics (1)gene therapy (1)cytochrome p450 2e1 inhibition (1)oxidative metabolic phenotype (1)phosphorylation regulation (1)aggregation (1)downregulation (1)glutamate exchange (1)acidosis (1)dysregulated gene expression (1)glycan expression (1)
▸ Mechanisms — Signaling (51)
▸ Mechanisms — Immune modulation (21)
▸ Mechanisms — DNA damage / Repair (5)
▸ Mechanisms — Epigenetic (18)
▸ Mechanisms — Cell death (7)
▸ Mechanisms — Protein interaction (14)
▸ Mechanisms — Metabolic rewiring (8)
🔗 Ligands 659
▸ Ligands — N-donor (25)
▸ Ligands — Heterocyclic (9)
▸ Ligands — C-donor / NHC (4)
▸ Ligands — S-donor (14)
▸ Ligands — O-donor (7)
▸ Ligands — Other (8)
▸ Ligands — P-donor (2)
▸ Ligands — Peptide / Protein (4)
▸ Ligands — Macrocyclic (3)
▸ Ligands — Polydentate (5)
🧠 Concepts 612
▸ Concepts — Other biomedical (178)
medicinal chemistry (122)photoactivated (27)cell biology (13)chemotherapy (11)metabolism (10)biochemistry (9)artificial intelligence (7)large language models (7)systems biology (6)information retrieval (5)precision medicine (5)gene regulation (5)data mining (5)chemoprevention (4)cheminformatics (4)therapeutic target (4)mitophagy (4)immunology (4)genetics (4)biomedical research (3)large language model (3)biomedical literature (3)hydrogen bonding (3)post-translational modifications (3)chemotherapy resistance (3)variant interpretation (3)immunometabolism (3)physiology (2)clinical practice (2)evidence extraction (2)biotransformation (2)metabolic regulation (2)physiological relevance (2)chemical biology (2)cell cycle progression (2)immunomodulation (2)biophysics (2)protein modification (2)biopharmaceutics (2)immunity (2)in vitro modeling (2)post-translational modification (2)targeted therapy (2)predictive modeling (2)therapy resistance (2)desiccant efficiency (1)multimodal data integration (1)stereochemistry (1)variant evaluation (1)epithelial-mesenchymal transition (1)metalloprotein (1)genetic screening (1)self-assembly (1)personalized therapy (1)protein function prediction (1)cellular mechanisms (1)protein targeting (1)evidence-based medicine (1)photophysics (1)protein modifications (1)translational research (1)paracellular transport (1)helicase mechanism (1)chemiosmosis (1)polarizability (1)nonequilibrium (1)genotype characterization (1)nuclear shape (1)nutrient dependency (1)metabolic engineering (1)interactome (1)therapies (1)probing (1)multiscale analysis (1)reactive species interactome (1)tissue-specific (1)pharmaceutics (1)knowledge extraction (1)metabolic activities (1)protein function (1)chemical ontology (1)proton delocalization (1)permeability (1)biomarkers (1)prediction tool (1)mechanisms of action (1)protein-ligand binding affinity prediction (1)short hydrogen bonds (1)chemical language models (1)biomedical informatics (1)organelle function (1)microbiome (1)pathogenesis (1)mechanistic framework (1)biosignatures (1)cellular stress response (1)ion-selective electrodes (1)multimodal fusion (1)gasotransmitter (1)carbon metabolism (1)bioengineering (1)ion association (1)enzyme mechanism (1)symmetry breaking (1)micropolarity (1)genome stability (1)scaffold (1)global health (1)clinical implications (1)cellular neurobiology (1)mesh indexing (1)llm (1)therapeutic strategy (1)ner (1)dissipative behavior (1)enzymology (1)pretrained model (1)longevity (1)profiling approaches (1)multimodal information integration (1)therapeutic implications (1)astrobiology (1)protein sequence analysis (1)selective degradation (1)mechanical properties (1)biomedical literature search (1)metabolism regulation (1)extracellular vesicles (1)protein chemistry (1)foundation model (1)data science (1)low-barrier hydrogen bonds (1)variant detection (1)synthetic biology (1)therapeutic innovation (1)therapeutic targeting (1)metabolic dependencies (1)protein data bank (1)cellular biology (1)phenotypic screening (1)immunoengineering (1)database (1)thermochemistry (1)therapeutic approaches (1)medical subject heading (1)network biology (1)inorganic chemistry (1)immunoregulation (1)ageing (1)protein interaction networks (1)hormone mimics (1)therapeutics (1)chemotherapy efficacy (1)metabolite-mediated regulation (1)regulatory landscape (1)chemical informatics (1)mental well-being (1)personalized medicine (1)cell plasticity (1)protein science (1)metabolic therapy (1)cell polarity (1)bioavailability (1)biomedicine (1)cellular stress (1)network medicine (1)energy transduction (1)boron helices (1)nucleolar biology (1)sialic acid (1)organic solvent drying (1)phenotypic analysis (1)in vivo perfusion (1)polypharmacy (1)hyperglycemia (1)phenotypic screens (1)mechanobiology (1)nuclear organization (1)
▸ Concepts — Bioinorganic (7)
▸ Concepts — Thermodynamics / Kinetics (10)
▸ Concepts — Evolution / Origin of life (9)
▸ Concepts — Nanomedicine / Delivery (2)
▸ Concepts — Cancer biology (1)
📦 Other 583
▸ Other (169)
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183 articles with selected tags
2016 · Nucleic Acids Research · Oxford University Press · added 2026-04-21
IID (Integrated Interactions Database) is the first database providing tissue-specific protein–protein interactions (PPIs) for model organisms and human. IID covers six species (S. cerevisiae (yeast), Show more
IID (Integrated Interactions Database) is the first database providing tissue-specific protein–protein interactions (PPIs) for model organisms and human. IID covers six species (S. cerevisiae (yeast), C. elegans (worm), D. melonogaster (fly), R. norvegicus (rat), M. musculus (mouse) and H. sapiens (human)) and up to 30 tissues per species. Users query IID by providing a set of proteins or PPIs from any of these organisms, and specifying species and tissues where IID should search for interactions. If query proteins are not from the selected species, IID enables Show less
📄 PDF DOI: 10.1093/nar/gkv1115
bioinformatics cancer computational prediction data visualization database disease gene identification drug discovery gene
Yuan Q, Gao J, Wu D +3 more · 2016 · Bioinformatics · Oxford University Press · added 2026-04-20
Identifying drug-target interactions is an important task in drug discovery. To reduce heavy time and financial cost in experimental way, many computational approaches have been proposed. Although the Show more
Identifying drug-target interactions is an important task in drug discovery. To reduce heavy time and financial cost in experimental way, many computational approaches have been proposed. Although these approaches have used many different principles, their performance is far from satisfactory, especially in predicting drug-target interactions of new candidate drugs or targets. Show less
📄 PDF DOI: 10.1093/bioinformatics/btw244
bioinformatics cancer computational approaches drug drug discovery drug-target interaction prediction drugs ensemble learning
2016 · · added 2026-04-20
The deregulation of gene expression is a characteristic of cancer cells, and malignant cells require very high levels of transcription to maintain their cancerous phenotype and survive. Therefore, com Show more
The deregulation of gene expression is a characteristic of cancer cells, and malignant cells require very high levels of transcription to maintain their cancerous phenotype and survive. Therefore, components of the basal transcription machinery may be considered as targets to preferentially kill cancerous cells. TFIIH is a multisubunit basal transcription factor that also functions in nucleotide excision repair. The recent discoveries of some small molecules that interfere with TFIIH and that preferentially kill cancer cells have increased researchers' interest to elucidate the complex mechanisms by which TFIIH operates. In this review, we summarize the knowledge generated during the 25 years of TFIIH research, highlighting the recent advances in TFIIH structural and mechanistic analyses that suggest the potential of TFIIH as a target for cancer treatment. Show less
📄 PDF DOI: 10.7150/jca.16966
anticancer cancer cancer treatment dna gene expression mechanistic analysis medicinal chemistry nucleotide excision repair
2016 · · American Society for Biochemistry and Molecular Biology · added 2026-04-20
The enzyme cytochrome c oxidase (CcO) or complex IV (EC 1.9.3.1) is a large transmembrane protein complex that serves as the last enzyme in the respiratory electron transport chain of eukaryotic mitoc Show more
The enzyme cytochrome c oxidase (CcO) or complex IV (EC 1.9.3.1) is a large transmembrane protein complex that serves as the last enzyme in the respiratory electron transport chain of eukaryotic mitochondria. CcO promotes the switch from glycolytic to oxidative phosphorylation (OXPHOS) metabolism and has been associated with increased self-renewal characteristics in gliomas. Increased CcO activity in tumors has been associated with tumor progression after chemotherapy failure, and patients with primary glioblastoma multiforme and high tumor CcO activity have worse clinical outcomes than those with low tumor CcO activity. Therefore, CcO is an attractive target for cancer therapy. We report here the characterization of a CcO inhibitor (ADDA 5) that was identified using a high throughput screening paradigm. ADDA 5 demonstrated specificity for CcO, with no inhibition of other mitochondrial complexes or other relevant enzymes, and biochemical characterization showed that this compound is a non-competitive inhibitor of cytochrome c When tested in cellular assays, ADDA 5 dose-dependently inhibited the proliferation of chemosensitive and chemoresistant glioma cells but did not display toxicity against non-cancer cells. Furthermore, treatment with ADDA 5 led to significant inhibition of tumor growth in flank xenograft mouse models. Importantly, ADDA 5 inhibited CcO activity and blocked cell proliferation and neurosphere formation in cultures of glioma stem cells, the cells implicated in tumor recurrence and resistance to therapy in patients with glioblastoma. In summary, we have identified ADDA 5 as a lead CcO inhibitor for further optimization as a novel approach for the treatment of glioblastoma and related cancers. Show less
📄 PDF DOI: 10.1074/jbc.m116.749978
adda 5 anticancer biochemical characterization cancer cell cycle arrest cellular assays cytochrome c oxidase enzyme inhibition
Chunfang Gu, Miranda S. C. Wilson, Henning J. Jessen +2 more · 2016 · Public Library of Science · PLOS · added 2026-04-20
The HCT116 cell line, which has a pseudo-diploid karotype, is a popular model in the fields of cancer cell biology, intestinal immunity, and inflammation. In the current study, we describe two batches Show more
The HCT116 cell line, which has a pseudo-diploid karotype, is a popular model in the fields of cancer cell biology, intestinal immunity, and inflammation. In the current study, we describe two batches of diverged HCT116 cells, which we designate as HCT116NIH and HCT116UCL. Using both gel electrophoresis and HPLC, we show that HCT116UCL cells contain 6-fold higher levels of InsP8 than HCT116NIH cells. This observation is significant because InsP8 is one of a group of molecules collectively known as ‘inositol pyrophosphates’ (PP-InsPs)—highly ‘energetic’ and conserved regulators of cellular and organismal metabolism. Variability in the cellular levels of InsP8 within divergent HCT116 cell lines could have impacted the phenotypic data obtained in previous studies. This difference in InsP8 levels is more remarkable for being specific; levels of other inositol phosphates, and notably InsP6 and 5-InsP7, are very similar in both HCT116NIH and HCT116UCL lines. We also developed a new HPLC procedure to record 1-InsP7 levels directly (for the first time in any mammalian cell line); 1-InsP7 comprised <2% of total InsP7 in HCT116NIH and HCT116UCL lines. The elevated levels of InsP8 in the HCT116UCL lines were not due to an increase in expression of the PP-InsP kinases (IP6Ks and PPIP5Ks), nor to a decrease in the capacity to dephosphorylate InsP8. We discuss how the divergent PP-InsP profiles of the newly-designated HCT116NIH and HCT116UCL lines should be considered an important research opportunity: future studies using these two lines may uncover new features that regulate InsP8 turnover, and may also yield new directions for studying InsP8 function. Show less
📄 PDF DOI: 10.1371/journal.pone.0165286
cancer cellular cellular metabolism gel electrophoresis hplc inositol pyrophosphates insp6 insp7
2015 · · Impact Journals · added 2026-04-20
Colorectal cancer (CRC) is the third most prevalent cancer type worldwide with a mortality rate of approximately 50%. Elevated cell-surface expression of truncated carbohydrate structures such as Tn a Show more
Colorectal cancer (CRC) is the third most prevalent cancer type worldwide with a mortality rate of approximately 50%. Elevated cell-surface expression of truncated carbohydrate structures such as Tn antigen (GalNAcα-Ser/Thr) is frequently observed during tumor progression. We have previously demonstrated that the C-type lectin macrophage galactose-type lectin (MGL), expressed by human antigen presenting cells, can distinguish healthy tissue from CRC through its specific recognition of Tn antigen. Both MGL binding and oncogenic BRAF mutations have been implicated in establishing an immunosuppressive microenvironment. Here we aimed to evaluate whether MGL ligand expression has prognostic value and whether this was correlated to BRAF(V600E) mutation status. Using a cohort of 386 colon cancer patients we demonstrate that high MGL binding to stage III tumors is associated with poor disease-free survival, independent of microsatellite instability or adjuvant chemotherapy. In vitro studies using CRC cell lines showed an association between MGL ligand expression and the presence of BRAF(V600E). Administration of specific BRAF(V600E) inhibitors resulted in decreased expression of MGL-binding glycans. Moreover, a positive correlation between induction of BRAF(V600E) and MGL binding to epithelial cells of the gastrointestinal tract was found in vivo using an inducible BRAF(V600E) mouse model. We conclude that the BRAF(V600E) mutation induces MGL ligand expression, thereby providing a direct link between oncogenic transformation and aberrant expression of immunosuppressive glycans. The strong prognostic value of MGL ligands in stage III colon cancer patients, i.e. when tumor cells disseminate to lymph nodes, further supports the putative immune evasive role of MGL ligands in metastatic disease. Show less
📄 PDF DOI: 10.18632/oncotarget.4495
braf(v600e) cancer cell surface colon cancer glycan expression immunosuppressive immunosuppressive microenvironment in vitro
2014 · · added 2026-04-21
cancer cell signaling cellular transformation nadph oxidase 1 (nox1) oncogene-mediated cellular transformation oncogenic signaling oxidative stress p38
2014 · Oncotarget · Impact Journals · added 2026-04-21
Recent evidence showed that a variety of DNA damaging agents including 5-FU and L-OHP impairs ribosomal biogenesis activating a ribosomal stress pathway. Here, we demonstrate that in lung and colon ca Show more
Recent evidence showed that a variety of DNA damaging agents including 5-FU and L-OHP impairs ribosomal biogenesis activating a ribosomal stress pathway. Here, we demonstrate that in lung and colon cancer cell lines devoid of p53, the efficacy of 5-FU and L-OHP chemotherapy depends on rpL3 status. Specifically, we demonstrate that ribosomal stress induced by 5-FU and L-OHP is associated to up-regulation of rpL3 and its accumulation as ribosome-free form. We show that rpL3 participates in the cell response to chemotherapy acting as a critical regulator of cell cycle, apoptosis Show less
📄 PDF DOI: 10.18632/oncotarget.2591
5-fu anticancer cancer cell cycle arrest chemotherapy cytotoxic dna dna repair
2014 · · American Society for Biochemistry and Molecular Biology · added 2026-04-20
Inactivating mutations of the gene encoding the tricarboxylic acid cycle enzyme fumarate hydratase (FH) have been linked to an aggressive variant of hereditary kidney cancer (hereditary leiomyomatosis Show more
Inactivating mutations of the gene encoding the tricarboxylic acid cycle enzyme fumarate hydratase (FH) have been linked to an aggressive variant of hereditary kidney cancer (hereditary leiomyomatosis and renal cell cancer). These tumors accumulate markedly elevated levels of fumarate. Fumarate is among a growing list of oncometabolites identified in cancers with mutations of genes involved in intermediary metabolism. FH-deficient tumors are notable for their pronounced accumulation of the transcription factor hypoxia inducible factor-1α (HIF-1α) and aggressive behavior. To date, HIF-1α accumulation in hereditary leiomyomatosis and renal cell cancer tumors is thought to result from fumarate-dependent inhibition of prolyl hydroxylases and subsequent evasion from von Hippel-Lindau-dependent degradation. Here, we demonstrate a novel mechanism by which fumarate promotes HIF-1α mRNA and protein accumulation independent of the von Hippel-Lindau pathway. Here we demonstrate that fumarate promotes p65 phosphorylation and p65 accumulation at the HIF-1α promoter through non-canonical signaling via the upstream Tank binding kinase 1 (TBK1). Consistent with these data, inhibition of the TBK1/p65 axis blocks HIF-1α accumulation in cellular models of FH loss and markedly reduces cell invasion of FH-deficient RCC cancer cells. Collectively, our data demonstrate a novel mechanism by which pseudohypoxia is promoted in FH-deficient tumors and identifies TBK1 as a novel putative therapeutic target for the treatment of aggressive fumarate-driven tumors. Show less
📄 PDF DOI: 10.1074/jbc.m114.568162
apoptosis evasion bioinorganic cancer cell invasion cellular models fumarate hereditary leiomyomatosis and renal cell cancer hif-1α
Jörg Eder, Richard Sedrani, Christian Wiesmann · 2014 · Nature reviews. Drug discovery · Nature · added 2026-04-20
Analysis of the origins of new drugs approved by the US Food and Drug Administration (FDA) from 1999 to 2008 suggested that phenotypic screening strategies had been more productive than target-based a Show more
Analysis of the origins of new drugs approved by the US Food and Drug Administration (FDA) from 1999 to 2008 suggested that phenotypic screening strategies had been more productive than target-based approaches in the discovery of first-in-class small-molecule drugs. However, given the relatively recent introduction of target-based approaches in the context of the long time frames of drug development, their full impact might not yet have become apparent. Here, we present an analysis of the origins of all 113 first-in-class drugs approved by the FDA from 1999 to 2013, which shows that the majority (78) were discovered through target-based approaches (45 small-molecule drugs and 33 biologics). In addition, of 33 drugs identified in the absence of a target hypothesis, 25 were found through a chemocentric approach in which compounds with known pharmacology served as the starting point, with only eight coming from what we define here as phenotypic screening: testing a large number of compounds in a target-agnostic assay that monitors phenotypic changes. We also discuss the implications for drug discovery strategies, including viewing phenotypic screening as a novel discipline rather than as a neoclassical approach. Show less
no PDF DOI: 10.1038/nrd4336
anticancer biologics cancer chemocentric approach drug discovery enzyme medicinal chemistry phenotypic screening
2013 · · American Society for Biochemistry and Molecular Biology · added 2026-04-20
Ribosome biogenesis is a process required for cellular growth and proliferation. Processing of ribosomal RNA (rRNA) is highly sensitive to flavopiridol, a specific inhibitor of cyclin-dependent kinase Show more
Ribosome biogenesis is a process required for cellular growth and proliferation. Processing of ribosomal RNA (rRNA) is highly sensitive to flavopiridol, a specific inhibitor of cyclin-dependent kinase 9 (Cdk9). Cdk9 has been characterized as the catalytic subunit of the positive transcription elongation factor b (P-TEFb) of RNA polymerase II (RNAPII). Here we studied the connection between RNAPII transcription and rRNA processing. We show that inhibition of RNAPII activity by α-amanitin specifically blocks processing of rRNA. The block is characterized by accumulation of 3' extended unprocessed 47 S rRNAs and the entire inhibition of other 47 S rRNA-specific processing steps. The transcription rate of rRNA is moderately reduced after inhibition of Cdk9, suggesting that defective 3' processing of rRNA negatively feeds back on RNAPI transcription. Knockdown of Cdk9 caused a strong reduction of the levels of RNAPII-transcribed U8 small nucleolar RNA, which is essential for 3' rRNA processing in mammalian cells. Our data demonstrate a pivotal role of Cdk9 activity for coupling of RNAPII transcription with small nucleolar RNA production and rRNA processing. Show less
📄 PDF DOI: 10.1074/jbc.m113.483719
bioinorganic cancer cdk9 cell cycle arrest cyclin-dependent kinase 9 dna enzyme inhibition
2013 · Biochimica et Biophysica Acta (BBA) - Gene Regulatory Mechanisms · Elsevier · added 2026-04-21
📄 PDF DOI: 10.1016/j.bbagrm.2012.09.003
anticancer cancer chemotherapy cisplatin dna dna damage platinumbased pt
2013 · · Nature · added 2026-04-21
Cell lines are invaluable biomedical research tools, and recent literature has emphasized the importance of genotype authentication and characterization. In the present study, 24 out of 27 cell line i Show more
Cell lines are invaluable biomedical research tools, and recent literature has emphasized the importance of genotype authentication and characterization. In the present study, 24 out of 27 cell line identities were confirmed by short tandem repeat profiling. The molecular phenotypes of the 24 colon cancer cell lines were examined, and microsatellite instability (MSI) and CpG island methylator phenotype (CIMP) were determined, using the Bethesda panel mononucleotide repeat loci and two epimarker panels, respectively. Furthermore, the BRAF, KRAS and PIK3CA oncogenes were analyzed for mutations in known hotspots, while the entire coding sequences of the PTEN and TP53 tumor suppressors were investigated. Nine cell lines showed MSI. Thirteen and nine cell lines were found to be CIMP positive, using the Issa panel and the Weisenberger et al. panel, respectively. The latter was found to be superior for CIMP classification of colon cancer cell lines. Seventeen cell lines harbored disrupting TP53 mutations. Altogether, 20/24 cell lines had the mitogen-activated protein kinase pathway activating mutually exclusive KRAS or BRAF mutations. PIK3CA and PTEN mutations leading to hyperactivation of the phosphoinositide 3-kinase/AKT pathway were observed in 13/24 cell lines. Interestingly, in four cell lines there were no mutations in neither BRAF, KRAS, PIK3CA nor in PTEN. In conclusion, this study presents molecular features of a large number of colon cancer cell lines to aid the selection of suitable in vitro models for descriptive and functional research. Show less
📄 PDF DOI: 10.1038/oncsis.2013.35
biomedical research braf cancer cell line authentication cell line characterization colon cancer cpg island methylator phenotype dna
2013 · Encyclopedia of Scientific Dating Methods · Springer · added 2026-04-21
📄 PDF DOI: 10.1007/978-94-007-6326-5_20-1
anticancer antiviral bioinorganic brivudine cancer cell growth arrest cell metabolism conditional lethality
Michael G Kemp, Aziz Sancar · 2012 · Cell cycle (Georgetown, Tex.) · added 2026-04-20
Exposure of cells to UV light from the sun causes the formation of pyrimidine dimers in DNA that have the potential to lead to mutation and cancer. In humans, pyrimidine dimers are removed from the ge Show more
Exposure of cells to UV light from the sun causes the formation of pyrimidine dimers in DNA that have the potential to lead to mutation and cancer. In humans, pyrimidine dimers are removed from the genome in the form of ~30 nt-long oligomers by concerted dual incisions. Though nearly 50 y of excision repair research has uncovered many details of UV photoproduct damage recognition and removal, the fate of the excised oligonucleotides and, in particular, the ultimate fate of the chemically very stable pyrimidine dimers remain unknown. Physiologically relevant UV doses introduce hundreds of thousands of pyrimidine dimers in diploid human cells, which are excised from the genome within ~24 h. Once removed from the genome, "where do all the dimers go?" In a recent study we addressed this question. Although our study did not determine the fate of the dimer itself, it revealed that the excised ~30-mer is released from the duplex in a tight complex with the transcription/repair factor TFIIH. This finding combined with recent reports that base and oligonucleotide products of the base and double-strand break repair pathways also make stable complexes with the cognate repair enzymes, and that these complexes activate the MAP kinase and checkpoint signaling pathways, respectively, raises the possibility that TFIIH-30-mer excision complexes may play a role in signaling reactions in response to UV damage. Show less
no PDF DOI: 10.4161/cc.21126
bioinorganic cancer damage recognition dna dna binding dna repair excision repair medicinal chemistry
Keith M. Jacobs, Sandeep R. Bhave, Daniel J. Ferraro +3 more · 2012 · International Journal of Cell Biology · added 2026-04-21
Although glycogen synthase kinase-3 beta (GSK-3β) was originally named for its ability to phosphorylate glycogen synthase and regulate glucose metabolism, this multifunctional kinase is presently know Show more
Although glycogen synthase kinase-3 beta (GSK-3β) was originally named for its ability to phosphorylate glycogen synthase and regulate glucose metabolism, this multifunctional kinase is presently known to be a key regulator of a wide range of cellular functions. GSK-3βis involved in modulating a variety of functions including cell signaling, growth metabolism, and various transcription factors that determine the survival or death of the organism. Secondary to the role of GSK-3βin various diseases including Alzheimer’s disease, inflammation, diabetes, and cancer, small molecule inhibitors of GSK-3βare gaining significant attention. This paper is primarily focused on addressing the bifunctional or conflicting roles of GSK-3βin both the promotion of cell survival and of apoptosis. GSK-3βhas emerged as an important molecular target for drug development. Show less
📄 PDF DOI: 10.1155/2012/930710
anticancer bioinorganic cancer cell biology enzyme gsk-3β protein qpcr
Sporn, Michael B., Liby, Karen T. · 2012 · Nature Publishing Group · Nature · added 2026-04-20
NFE2-related factor 2 (NRF2) has apparently contradictory roles in cancer. Activation of NRF2 contributes to the chemopreventive effects of various clinically used drugs against various diseases inclu Show more
NFE2-related factor 2 (NRF2) has apparently contradictory roles in cancer. Activation of NRF2 contributes to the chemopreventive effects of various clinically used drugs against various diseases including cancer. However, NRF2 activity can also accelerate tumorigenesis in mouse models, thus highlighting a potential danger of NRF2 activation. This Opinion article discusses how these opposing roles might be reconciled. Show less
no PDF DOI: 10.1038/nrc3278
anticancer bioinorganic cancer chemoprevention medicinal chemistry nrf2 tumorigenesis
2011 · The AAPS Journal, · added 2026-04-21
Progress in an understanding of the genetic basis of cancer coupled to molecular pharmacology of potential new anticancer drugs calls for new approaches that are able to address key issues in the drug Show more
Progress in an understanding of the genetic basis of cancer coupled to molecular pharmacology of potential new anticancer drugs calls for new approaches that are able to address key issues in the drug development process, including pharmacokinetic (PK) and pharmacodynamic (PD) relationships. The incorporation of predictive preclinical PK/PD models into rationally designed early-stage clinical trials offers a promising way to relieve a significant bottleneck in the drug discovery pipeline. The aim of the current review is to discuss some considerations for how quantitative PK and PD analyses for anticancer drugs may be conducted and integrated into a global translational effort, and the importance of examining drug disposition and dynamics in target tissues to support the development of preclinical PK/PD models that can be subsequently extrapolated to predict pharmacologic characteristics in patients. In this article, we describe three different physiologically based (PB) PK modeling approaches, i.e., the whole-body PBPK model, the hybrid PBPK model, and the two-pore model for macromolecules, as well as their applications. General conclusions are that greater effort should be made to generate more clinical data that could validate scaled preclinical PB-PK/PD tumor-based models and, thus, stimulate a framework for preclinical to clinical translation. Finally, given the innovative techniques to measure tissue drug concentrations and associated biomarkers of drug responses, development of predictive PK/PD models will become a standard approach for drug discovery and development. Show less
📄 PDF DOI: 10.1208/s12248-011-9253-1
anticancer cancer drug discovery medicinal chemistry pharmacodynamic pharmacokinetic
2011 · · American Society for Biochemistry and Molecular Biology · added 2026-04-20
Hypoxia inducible factor-1 (HIF-1) is a key transcription factor required for cellular adaptation to hypoxia, although its physiological roles and activation mechanisms during normoxia have not been s Show more
Hypoxia inducible factor-1 (HIF-1) is a key transcription factor required for cellular adaptation to hypoxia, although its physiological roles and activation mechanisms during normoxia have not been studied sufficiently. The Warburg effect, which is a hallmark of malignant tumors that is characterized by increased activity of aerobic glycolysis, accompanies activation of HIF-1 during normoxia. Besides tumor cells that have multiple genetic and epigenetic alterations, normal macrophages also use glycolysis for ATP production by depending upon elevated HIF-1 activity even during normoxia. We recently found that activity of factor inhibiting HIF-1 (FIH-1) is specifically suppressed in macrophages by a nonproteolytic activity of membrane type-1 matrix metalloproteinase (MT1-MMP/MMP-14). Thus, MT1-MMP expressed in macrophages plays a significant role in regulating HIF-1 activity during normoxia. In the light of this finding, we examined here whether MT1-MMP contributes to the Warburg effect of tumor cells. All the tumor cell lines that express MT1-MMP exhibit increased glycolytic activity, and forced expression of MT1-MMP in MT1-MMP-negative tumor cells is sufficient to induce the Warburg effect. The cytoplasmic tail of MT1-MMP mediates the stimulation of aerobic glycolysis by increasing the expression of HIF-1 target genes. Specific intervention of the MT1-MMP-mediated activation of HIF-1 in tumor cells retarded tumor growth in mice. Systemic administration of a membrane-penetrating form of the cytoplasmic tail peptide in mice to inhibit HIF-1 activation competitively also exhibited a therapeutic effect on tumors. Show less
📄 PDF DOI: 10.1074/jbc.m110.188714
aerobic glycolysis anticancer bioinorganic cancer cellular adaptation to hypoxia glycolysis glycolytic activity hif-1
Xiao-Yi Kuai · 2010 · World Journal of Gastroenterology · added 2026-04-21
Key words: Mitochondrial uncoupling protein 2; Colon cancer; Uncoupling protein 2; Clinicopathologic characteristics AIM: To detect the expression of mitochondrial uncoupling protein 2 (UCP2) in colon Show more
Key words: Mitochondrial uncoupling protein 2; Colon cancer; Uncoupling protein 2; Clinicopathologic characteristics AIM: To detect the expression of mitochondrial uncoupling protein 2 (UCP2) in colon cancer and analyze the relation between UCP2 expression and clinical pathological features of colon cancer. Peer reviewer: Guangcun Huang, MD, PhD, Research Institute at Nationwide Children’s Hospital, Center for Clinical and Translational Research, Columbus, OH 43205, United States METHODS: Fifteen colon tissue samples and 15 its adjacent tissue samples were obtained from colon cancer Show less
📄 PDF DOI: 10.3748/wjg.v16.i45.5773
cancer cancer research colon cancer cytoplasm immunohistochemistry metastasis mitochondria mitochondrial dysfunction
Huanting Liu, Jana Rudolf, Kenneth A. Johnson +7 more · 2008 · Cell · Elsevier · added 2026-04-20
The XPD helicase (Rad3 in Saccharomyces cerevisiae) is a component of transcription factor IIH (TFIIH), which functions in transcription initiation and Nucleotide Excision Repair in eukaryotes, cataly Show more
The XPD helicase (Rad3 in Saccharomyces cerevisiae) is a component of transcription factor IIH (TFIIH), which functions in transcription initiation and Nucleotide Excision Repair in eukaryotes, catalyzing DNA duplex opening localized to the transcription start site or site of DNA damage, respectively. XPD has a 5' to 3' polarity and the helicase activity is dependent on an iron-sulfur cluster binding domain, a feature that is conserved in related helicases such as FancJ. The xpd gene is the target of mutation in patients with xeroderma pigmentosum, trichothiodystrophy, and Cockayne's syndrome, characterized by a wide spectrum of symptoms ranging from cancer susceptibility to neurological and developmental defects. The 2.25 A crystal structure of XPD from the crenarchaeon Sulfolobus tokodaii, presented here together with detailed biochemical analyses, allows a molecular understanding of the structural basis for helicase activity and explains the phenotypes of xpd mutations in humans. Show less
no PDF DOI: 10.1016/j.cell.2008.04.029
bioinorganic cancer cockayne's syndrome dna dna repair nucleotide excision repair structural biology transcription initiation
2007 · CHIMIA · Swiss Chemical Society · added 2026-04-20
📄 PDF DOI: 10.2533/chimia.2007.691
au bioinorganic cancer catalysis cisplatin coordination chemistry drug delivery fe
2005 · Biophysical Journal · added 2026-04-21
📄 PDF DOI: 10.1529/biophysj.104.054650
anticancer antitumor bioinorganic cancer cisplatin diamine diaminobutane dna
2002 · European Surgery-Acta Chirurgica Austriaca · added 2026-04-21
📄 PDF DOI: 10.1046/j.1563-2563.2002.02003.x
anticancer antioxidant cancer cancer biology cellular accumulation constitutive induction drug resistance enzyme
2002 · · Springer · added 2026-04-20
Oxaliplatin (Eloxatine) is a third-generation platinum compound which has shown a wide antitumour effect both in vitro and in vivo, a better safety profile than cisplatin and a lack of cross-resistanc Show more
Oxaliplatin (Eloxatine) is a third-generation platinum compound which has shown a wide antitumour effect both in vitro and in vivo, a better safety profile than cisplatin and a lack of cross-resistance with cisplatin and carboplatin. In this scenario, oxaliplatin may represent an innovative and challenging drug extending the antitumour activity in diseases such as gastrointestinal cancer that are not usually sensitive to these coordination complexes. Oxaliplatin has a non-hydrolysable diaminocyclohexane (DACH) carrier ligand which is maintained in the final cytotoxic metabolites of the drug. Like cisplatin, oxaliplatin targets DNA producing mainly 1,2-GG intrastrand cross-links. The cellular and molecular aspects of the mechanism of action of oxaliplatin have not yet been fully elucidated. However, the intrinsic chemical and steric characteristics of the DACH-platinum adducts appear to contribute to the lack of cross-resistance with cisplatin. To date, mismatch repair and replicative bypass appear to be the processes most likely involved in differentiating the molecular responses to these agents. Show less
📄 PDF DOI: 10.1007/pl00012514
anticancer antitumour cancer carboplatin cisplatin coordination chemistry diaminocyclohexane dna
2000 · Journal of molecular biology · added 2026-04-20
The anticancer activity of cisplatin derives from its ability to bind and cross-link DNA, with the major adduct being the 1,2-d(GpG) intrastrand cross-link. Here, the consequences of this adduct on th Show more
The anticancer activity of cisplatin derives from its ability to bind and cross-link DNA, with the major adduct being the 1,2-d(GpG) intrastrand cross-link. Here, the consequences of this adduct on the conformation, thermal stability, and energetics of duplex DNA are assessed, and the modulation of these parameters by the sequence context of the adduct is evaluated. The properties of a family of 15-mer DNA duplexes containing a single 1,2-d(GpG) cis-¿Pt(NH(3))(2)¿(2+) intrastrand cross-link are probed in different sequence contexts where the flanking base-pairs are systematically varied from T.A to C.G to A.T. By using a combination of spectroscopic and calorimetric techniques, the structural, thermal, and thermodynamic properties of each duplex, both with and without the cross-link, are characterized. Circular dichroism spectroscopic data reveal that the cross-link alters the structure of the host duplex in a manner consistent with a shift from a B-like to an A-like conformation. Thermal denaturation data reveal that the cross-link induces substantial thermal and thermodynamic destabilization of the host duplex. Significantly, the magnitudes of these cross-link-induced effects on duplex structure, thermal stability, and energetics are influenced by the bases that flank the adduct. The presence of flanking A.T base-pairs, relative to T.A or C.G base-pairs, enhances the extent of cross-link-induced alteration to an A-like conformation and dampens the extent of cross-link-induced duplex destabilization. These results are discussed in terms of available structural data, and in terms of the selective recognition of cisplatin-DNA adducts by HMG-domain proteins. Show less
no PDF DOI: 10.1006/jmbi.2000.3496
amine anticancer bioinorganic calorimetry cancer circular dichroism cisplatin coordination chemistry
2000 · · added 2026-04-21
anticancer bioinorganic cancer cisplatin inductively coupled plasma mass spectrometry medicinal chemistry neurotoxicity octanol-aqueous shake-flask method
1997 · · added 2026-04-21
anticancer bioinorganic biotransformation cancer chloride diaminocyclohexane in vitro in vivo
1976 · Extrapolation · added 2026-04-21
Polypharmacology has emerged as novel means in drug discovery for improving treatment response in clinical use. However, to really capitalize on the polypharmacological effects of drugs, there is a cr Show more
Polypharmacology has emerged as novel means in drug discovery for improving treatment response in clinical use. However, to really capitalize on the polypharmacological effects of drugs, there is a critical need to better model and understand how the complex interactions between drugs and their cellular targets contribute to drug efficacy and possible side effects. Network graphs provide a convenient modeling framework for dealing with the fact that most drugs act on cellular systems through targeting multiple proteins both through on-target and off-target binding. Network pharmacology models aim at addressing questions such as how and where in the disease network should one target to inhibit disease phenotypes, such as cancer growth, ideally leading to therapies that are less vulnerable to drug resistance and side effects by means of attacking the disease network at the systems level through synergistic and synthetic lethal interactions. Since the exponentially increasing number of potential drug target combinations makes pure experimental approach quickly unfeasible, this review depicts a number of computational models and algorithms that can effectively reduce the search space for determining the most promising combinations for experimental evaluation. Such computational-experimental strategies are geared toward realizing the full potential of multi-target treatments in different disease phenotypes. Our specific focus is on system-level network approaches to polypharmacology designs in anticancer drug discovery, where we give representative examples of how network-centric modeling may offer systematic strategies toward better understanding and even predicting the phenotypic responses to multi-target therapies. Show less
📄 PDF DOI: 10.3828/extr.1976.17.2.133
algorithms anticancer bioinformatics cancer cellular targets computational models disease network drug discovery
· MDPI · added 2026-04-21
Platinum-based anticancer anticancer drugs drugs represented represented by by cisplatin cisplatin play play important important roles roles in in the the treatment of of various various solid solid t Show more
Platinum-based anticancer anticancer drugs drugs represented represented by by cisplatin cisplatin play play important important roles roles in in the the treatment of of various various solid solid tumors. tumors. However, However, their their applications applications are are largely largely compromised compromised by by drug drug treatment resistanceand andside side effects. effects. Much Much effort effort has has been been made made to to circumvent circumvent the the drug drug resistance resistanceand Show less
📄 PDF DOI: 10.3390/xxxxx
anticancer cancer cisplatin coordination chemistry medicinal chemistry platinum(ii) pt