DruMAP consists of a database for DMPK parameters and programs that can predict many DMPK parameters from the chemical structure of a compound.


The current version contains the following chemicals and activity data:

Number of records
All registered compounds 30,971
Freebase compounds 27,500
Freebase compounds ignoring stereo structure 25,622
Physicochemical parameters
Parameter name Parameter type Species Our experimental data Curated public data Predicted data
Solubility (pH 7.4) Sol7.4 None 163 367 27,505
Solubility (pH 1.2) Sol1.2 None 163
Distribution coefficient (pH 7.4) logD7.4 None 120
In vitro parameters
Parameter name Parameter type Species Our experimental data Curated public data Predicted data
Fraction unbound in plasma fu,p Human 441 2,319 55,010
Rat 422 120 55,010
Fraction unbound in brain homogenate fu,brain Rat 443
Mammal 253 27,505
Blood-to-plasma concentration ratio Rb Human 213
Rat 163
Hepatic intrinsic clearance in liver microsome CLint Human 166 5,230 54,977
Rat 167
Probability metabolized by CYP1A2 CYP Human 27,505
Probability metabolized by CYP2C9 CYP Human 27,505
Probability metabolized by CYP2D6 CYP Human 27,505
Probability metabolized by CYP3A4 CYP Human 27,505
Site metabolized by CYP1A2 CYP Human 10,018
Site metabolized by CYP3A4 CYP Human 10,644
Permeability coefficient (Caco-2) Papp Human 4,408 27,505
Permeability coefficient (LLC-PK1) Papp Human 925 27,503
Rat 115
Pig 1,042
P-gp efflux ratio (LLC-PK1) ER Human 463
Rat 58
Pig 521
P-gp net efflux ratio (LLC-PK1) NER Human 446 27,505
Rat 57
In vivo parameters
Parameter name Parameter type Species In-house data Curated public data Common Thechnical Document Interview Form Predicted data
Drug concentration in plasma C Rat 1,335 16
Drug concentration in tissues C Rat 1,535 1,141
Initial drug concentration in plasma C0 Rat 49
Peak drug concentration Cmax Rat 366
Elimination half-life of a drug T1/2 Rat 415 65
Time to reach peak drug concentration Tmax Rat 366
Area under the drug concentration-time curve AUC Rat 830 70
Mean residence time of a drug in plasma MRT Rat 135
Brain-to-plasma concentration ratio Kp,brain Rat 100 27,505
Unbound brain-to-plasma concentration ratio Kp,uu,brain Rat 27,505
Volume of distribution Vd Human 102
Rat 39 5
Others 12
Apparent volume of distribution at oral administration Vd/F Human 197
Rat 96
Clearance CL Human 78
Rat 39 3
Others 4
Apparent clearance at oral administration CL/F Human 133
Rat 96
Renal clearance CLr Human 401 35 27,505
Bioavailability F Human 243
Rat 35 32
Others 33
Fraction absorbed Fa Human 945 27,505
Fraction excreted in urine fe Human 343 27,505
Excretion type in urine CR type Human 27,505
Toxicity data
Parameter name Parameter type Species Data provided by accompanying projects
IC50 for hERG channel IC50 Human 9,114
IC50 for Cav1.2 channel IC50 Human 204
IC50 for Kv1.5 channel IC50 Human 686
IC50 for Nav1.5 channel IC50 Human 1,321
Link to Hepatotoxicity database Human and rat 620

Predictive models

DruMAP provides predictive models for many PK parameters. For the current list of available models, see New Prediction page.

For in-house (commercial or academic) use of those programs, please consider the commercial version.

More Background

To address pharmacokinetic and toxicological issues in drug development, once the main source of late attrition of drug candidates, many pharmaceutical companies have now implemented early DMPK (Drug Metabolism and Pharmacokinetics) or early toxicological studies. However, such approaches are difficult to emulate in the academic drug discovery environment. Therefore, we began an initiative “Development of a Drug Discovery Informatics System” in collaboration with several other research groups. The main aim of this initiative is to develop more accurate prediction systems for DMPK and toxicological properties primarily targeting academic scientists. Our group’s focus is to develop a pharmacokinetics database and prediction models.

Any good prediction system depends on high-volume, high-quality training datasets. We collected pharmacokinetic and physicochemical parameters from the public bioactivity database, ChEMBL. However, since ChEMBL compiles data obtained in different experimental conditions, we developed a curation workflow to select the data measured in compatible conditions and to reformat the results as appropriate for our prediction system.

In addition to the public data, we have acquired both in vitro and in vivo experimental data under unified protocols. The in vitro experiments include physicochemical parameters such as solubility and distribution coefficient, and pharmacokinetic parameters such as metabolic stability, protein binding in plasma, protein binding in brain homogenate, and blood-to-plasma concentration ratio. In addition, we collected efflux ratio of P-glycoprotein (P-gp), which is the major transporter in gut and brain. The in vivo data include the drug concentrations in plasma and tissues after oral or intravenous administration of the drug and pharmacokinetic parameters calculated therefrom.

We are currently developing several new models and we plan to release them in due course. Send any questions or comments to drumap[at]nibiohn.go.jp (please replace [at] with @).