GlucokinaseDB (abbreviated as GKDB), is a manually curated database of glucokinase modulators.
GlucokinaseDB currently includes modulators of three distinct classes; activators, disruptors and inhibitors. Glucokinase activators (GKAs) cause activation of glucokinase (GK) by binding to the allosteric site. GK-GKRP disruptors prevent the formation of glucokinase-glucokinase regulatory protein complex thereby lifting endogenous repression of GK. Glucokinase inhibitors (GKIs) inhibit GK by competing against the substrate of GK, glucose.
Currently, GlucokinaseDB provides information about 1723 glucokinase modulators. Amongst these, 1476 are glucokinase activators (GKAs), 200 are GK-GKRP disruptors and 47 glucokinase inhibitors (GKIs).
Each modulator profile in GlucokinaseDB provides three categories of data; bioactivity, chemical and metadata. Bioactivity data includes the relevant activation/inhibition parameters of the modulator such EC50 values, IC50 values, Activation fold change etc. Chemical data includes the 2D/3D structure of modulator along with various calculated descriptors such as molecular weight, hydrophobicity, pharmacokinetic parameters, druglikeness profile etc. Metadata contains information extracted from the original reference of the modulator such as article DOI, publication year, cross-links to other databases etc.
A few areas of application of GlucokinaseDB can be the following:
As a detailed information repository of glucokinase modulators, GlucokinaseDB would be valuable for researchers and students working in the field of diabetic therapeutics and drug discovery in general. The planned expansion of the database to include further information regarding patents and clinical trials will further increase its applicability domain.
Following are few of the planned updates/expansion for GlucokinaseDB:
Anyone intending to submit a novel or possibly omitted glucokinase modulator should visit the 'Submit' page for details regarding user submission or contact the team (visit 'About Us' page for details).
The contact details of team involved in the design and development of GlucokinaseDB can be found on the About Us page.
| Activity, clinical and metadata | |
|---|---|
| Bioactivity data | |
| EC50 (μM) @ 2.5mM/L Glucose | Half maximal effective concentration of the compound at 2.5 mM/L of Glucose |
| EC50 (μM) @ 5mM/L Glucose | Half maximal effective concentration of the compound at 5 mM/L of Glucose |
| EC50 (μM) @ 10mM/L Glucose | Half maximal effective concentration of the compound at 10 mM/L of Glucose |
| Other modulation parameters | Other activity parameters such as activation fold |
| Clinical and metadata | |
| Clinical status | The current status of the compound in clinical trials/studies (if any) |
| Type | The broad general type of modulator: Glucokinase activator/GK-GKRP disruptor/Glucokinase inhibitor |
| Structure of GK-GKA complex (PDB) | The PDB ID of the compound-glucokinase complex |
| Publication year | The year of publication of the compound in literature |
| Company/Institution | The organizations/institutes involved in the development and evalutation of the compound |
| Primary reference | The first original publication/reference of the compound |
| DOI | The DOI of the primary reference |
| Nomenclature and cross-links | |
| Nomenclature | |
| IUPAC name | The IUPAC name of the compound |
| SMILES | The name of the compound in SMILES format |
| InChI | The name of the compound in InChI format |
| InChi-Key | The name of the compound in InChi-Key format |
| Database links | |
| PubChem ID | The PubChem ID of the compound |
| CHEMBL ID | The CHEMBL ID of the compound |
| ZINC ID | The ZINC ID of the compound |
| BindingDB ID | The BindingDB ID of the compound |
| Chemical properties | |
| Molecular formula | The molecular formula of the compound |
| Molecular weight | The molecular weight of the compound in Daltons |
| Heavy atoms | The number of heavy atoms (atoms which are not hydrogen) |
| Aromatic heavy atoms | The number of heavy atoms part of an aromatic system |
| Tri & tetra-cyclic heavy atoms | Number of atoms in 3- or 4-membered rings |
| Penta & hexa-cyclic heavy atoms | Number of atoms in 5- or 6-membered rings |
| Non-conjugated cyclic atoms | number of ring atoms not able to form conjugated aromatic systems (e.g. sp3 C). |
| Stereo centres | The number of chiral atoms |
| Fraction Csp3 | The fraction of carbon atoms which are sp3 hybridized (indicative of degree of saturation) |
| Non-conjugated amine groups | Number of non-conjugated amine groups |
| Amidines & guanindines | Number of amidine and guanidine groups. |
| Carboxylic acid groups | Number of carboxylic acid groups. |
| Non-conjugated amide groups | Number of non-conjugated amide groups. |
| Fluorine-SASA | Solvent-accessible surface area of fluorine atoms |
| Amide oxygens-SASA | Solvent-accessible surface area of amide oxygen atoms |
| Nitrogens & oxygens | Number of nitrogen and oxygen atoms. |
| Reactive functional groups | Number of reactive functional groups |
| Rotatable bonds | The number of freely-rotatable, non-restricted bonds |
| Dipole | Computed dipole moment of the molecule. |
| Dipole^2/Volume | Square of the dipole moment divided by the molecular volume. This is the key term in the Kirkwood-Onsager equation for the free energy of solvation of a dipole. |
| Cohesive interaction index | Index of cohesive interaction in solids. It is calculated by the following formula; [(accptHB(donorHB)^0.5)/Surface Area] |
| Globularity index | Globularity descriptor (perfectly spherical globularity = 1) |
| H-bond acceptors | The number of hydrogen bond acceptors |
| Mean H-bond acceptors | Estimated number of hydrogen bonds that would be accepted by the solute to water molecules in an aqueous solution (average of multiple conformations) |
| H-bond donors | The number of hydrogen bond donors |
| Mean H-bond donors | Estimated number of hydrogen bonds that would be donated by the solute to water molecules in an aqueous solution (average of multiple conformations) |
| Molar refractivity | The molar refractivity of the compound (a measure of total polarizability of the molecule) |
| TPSA | Topological Polar Surface Area: The total surface sum of all polar atoms in the molecules |
| Solvent accessible surface area (SASA) | Total solvent accessible surface area (SASA) in square angstroms using a probe with a 1.4 Å radius. |
| SASA-hydrophobic component (FOSA) | Hydrophobic component of the SASA (saturated carbon and attached hydrogen). |
| SASA-hydrophilic component (FISA) | Hydrophilic component of the SASA (SASA on N, O, H on heteroatoms, carbonyl C). |
| SASA-unsaturated carbon component (PISA) | π (carbon and attached hydrogen) component of the SASA. |
| SASA-weakly polar component (WPSA) | Weakly polar component of the SASA (halogens, P, and S). |
| Total solvent accessible volume | Total solvent-accessible volume in cubic angstroms using a probe with a 1.4 Å radius. |
| Ionization potential | PM3 calculated ionization potential (negative of HOMO energy). |
| Electron affinity | PM3 calculated electron affinity (negative of LUMO energy). |
| Polarizability | Predicted polarizability in cubic angstroms. |
| Lipophilicity and water solubility | |
| Lipophilicity properties | |
| iLOGP | Physics-based lipophilicity prediction (octanol/water coefficient) (SWISS-ADME) |
| XLOGP3 | Atomistic and knowledge based lipophilicity prediction (octanol/water coefficient) (SWISS-ADME) |
| WLOGP | Atomistic method for lipophilicity prediction (octanol/water coefficient) (SWISS-ADME) |
| MLOGP | Topological method for lipophilicity prediction (octanol/water coefficient) (SWISS-ADME) |
| Silicos-IT Log P | Hybrid fragmental/topological method for lipophilicity prediction (octanol/water coefficient) (SWISS-ADME) |
| Consensus Log P | The mean of the above five lipophilicity predictions (SWISS-ADME) |
| Hexadecanol/gas Log P | Predicted hexadecane/gas partition coefficient. (Qikprop-Schrödinger) |
| Octanol/gas Log P | Predicted octanol/gas partition coefficient. (Qikprop-Schrödinger) |
| Water/gas Log P | Predicted water/gas partition coefficient. (Qikprop-Schrödinger) |
| Octanol/water Log P | Predicted octanol/water partition coefficient. (Qikprop-Schrödinger) |
| Water solubility properties | |
| ESOL Log S | Topological method for water solubility prediction (LogS) (SWISS-ADME) |
| ESOL Solubility (mg/ml) | Topological method for water solubility prediction (mg/ml) (SWISS-ADME) |
| ESOL Solubility (mol/l) | Topological method for water solubility prediction (mol/l) (SWISS-ADME) |
| ESOL Class | Predicted solubility class based on the ESOL method (SWISS-ADME) |
| Ali Log S | Topological method for water solubility prediction (LogS) (SWISS-ADME) |
| Ali Solubility (mg/ml) | Topological method for water solubility prediction (mg/ml) (SWISS-ADME) |
| Ali Solubility (mol/l) | Topological method for water solubility prediction (mol/l) (SWISS-ADME) |
| Ali Class | Predicted solubility class based on the Ali method (SWISS-ADME) |
| Silicos-IT LogSw | Fragmental method for water solubility prediction (LogS) (SWISS-ADME) |
| Silicos-IT Solubility (mg/ml) | Fragmental method for water solubility prediction (mg/ml) (SWISS-ADME) |
| Silicos-IT Solubility (mol/l) | Fragmental method for water solubility prediction (mol/l) (SWISS-ADME) |
| Silicos-IT class | Predicted solubility class based on the Silicos-IT method (SWISS-ADME) |
| Aqueous solubility LogS | Predicted aqueous solubility, log S. (Qikprop-Scrödinger) |
| Conformation independent LogS | Conformation-independent predicted aqueous solubility, log S. (Qikprop-Scrödinger) |
| Pharmacokinetics and druglikeness properties | |
| Pharmacokinetics | |
| GI absorption (BIOLED-Egg) | Gastro-intestianal absorption prediction based on the BOILED-Egg model |
| Caco-2 permeability | Predicted apparent Caco-2 cell permeability in nm/sec. Caco-2 cells are a model for the gut-blood barrier. |
| Blood-brain barrier (BBB) permeant | Blood-brain barrier permeation prediction based on BOILED-Egg model |
| Blood-brain partition coefficient | Predicted brain/blood partition coefficient (if adminstered orally) |
| MDCK permeability | Predicted apparent MDCK cell permeability in nm/sec. |
| Central nervous system activity | Predicted central nervous system activity on a –2 (inactive) to +2 (active) scale. |
| P-glycoprotein substrate | P-glycoprotein substrate prediction based on an SVM-model |
| CYP1A2 inhibitor | CYP1A2 inhibitor prediction based on an SVM-model |
| CYP2C19 inhibitor | CYP2C19 inhibitor prediction based on an SVM-model |
| CYP2C9 inhibitor | CYP2C9 inhibitor prediction based on an SVM-model |
| CYP2D6 inhibitor | CYP2D6 inhibitor prediction based on an SVM-model |
| CYP3A4 inhibitor | CYP3A4 inhibitor prediction based on an SVM-model |
| Skin permeation log Kp (SWISS-ADME) | Skin permeation prediction based on a QSPR-model |
| Skin permeation log Kp (Qikprop) | Predicted skin permeability, log Kp. |
| Maximum transdermal transport rate | Predicted maximum transdermal transport rate, Kp × MW × S (μg cm–2 hr–1). Kp and S are obtained from the aqueous solubility and skin permeability, QPlogKp and QPlogS. |
| HERG K+ channel (IC50) | Predicted IC50 value for blockage of HERG K+ channels. |
| Likely metabolic reactions | Number of likely metabolic reactions. |
| Human serum albumin binding | Prediction of binding to human serum albumin |
| Human oral absorption | Predicted qualitative human oral absorption: 1, 2, or 3 for low, medium, or high (respectively). |
| Human oral absorption percentage | Predicted human oral absorption on 0 to 100% scale. |
| Druglikeness filters | |
| Lipinski violations | The number of violations as per Lipinski's rule of fives filter |
| Ghose violations | The number of violations as per Ghose filter |
| Veber violations | The number of violations as per Veber filter |
| Egan violations | The number of violations as per Egan filter |
| Muegge violations | The number of violations as per Muegge filter |
| Jorgensen violations | Number of violations of Jorgensen’s rule of three. |
| Other medicinal chemistry parameters | |
| Bioavailability Score | The Abbott bioavailability score (probability of >10% murine oral bioavailability) |
| PAINS alerts | The number of potential Pan Assay Interference Structures |
| Brenk alerts | The number of Brenk violations (indicative of toxic and metabolically unstable substructures) |
| Leadlikeness violations | The number of violations as per a filter for leadlikeness (Teague et al.) |
| Synthetic Accessibility | Potential difficulty in synthesizing the molecule based on fragmental contributions |