| What's ADMETlab
ADMETlab is a platform for systematic ADME/T evaluation of drug molecules. It provides a rapid way to predict pharmacokinetic properties by using 24 well-optimized SAR models. 5 druglikeness rules and 1 specific model are provided for users to analyze druglikeness. Further, it includes a well-organized ADME/T database (208,967 entries) that can be used to search similar compounds and make a comparison conveniently. It also can be used to make a comprehensive evaluation to obtain meaningful and constructive suggestions of molecular optimization by performing the systematic evaluation. In addition, ADMETlab created a user-friendly interface to accomplish these jobs just by clicking mouse.
| Main features
- Compared large datasets of most properties.
- Better and robust SAR models.
- Focus on drug molecules.
- Systematic analysis and comparison
- Provide constructive suggestions for molecular optimization
- Batch computation
- User-friendly interface
| Main functionalities
Similarity searching based on ADME/T database.
Systematic ADME/T assessment.
| Druglikeness rules
Druglikeness rules are several expert criterions that are used in drug design for how "druglike" a substance is with respect to factors like bioavailability. Here, we provide 5 selected rules:
- Lipinski's rules:
MW<=500; logP<=5; Hacc<=10; Hdon<=5
- Ghose's rules:
- 5.6< MclogP < -0.4 mean: 2.52; 160 < MW < 480 mean: 357;
40 < MR < 130 mean: 97; 20 < natoms < 70 mean: 48
- Oprea's rules:
nrings≥3; nrigidbond>=18; nRotbond≥6
- Veber's rules:
nRotbond<=10; tPSA<= 140 or Hacc and Hdon<=12
- Varma's rules:
MW<= 500; tPSA<=125; -5< logD < – 2; Hacc+Hdon<=9; nRotbond<=12
| Druglikeness model
We collected 6731 drugs from Drugbank database as samples of druglikeness. Then 6769 molecules was picked as negative samples from those molecules with IC50 or Ki less that 10000nm from CHEMBL database by using (Self-organizing feature Map,SOM) method. The SOM method ensures that the samples are picked from the not similar clusters compared with positive samples. Finally, we get a classification model with a accuracy of 0.801 for training set by 5-fold cross validation. This model can help us further distinguish between molecules with activities and potential drugs.
| Data summary
For all the ADMET-related properties, we collected corresponding data mainly by two approaches: the previous literatures and the DrugBank database (http://www.drugbank.ca). After several pretreatments, we totally obtained 24 datasets. The global overview of these ADMET datasets can be seen in the table below and you can click the "" to get the data files. Table S1.
Table S1. The number of end-points of each property
|Basic physicochemical property||LogS||2055||-||-||1541||514|
| Data sources
Note: you can contact us to get the datasets.
| Model summary
To obtain robust and reliable QSAR models for ADMET properties prediction, we constructed a series models and aimed to find a best one. Six methods (RF, SVM, RP, PLS, NB, DT) and seven types of descriptors (2D, Estate, MACCS, ECFP2, ECFP4, ECFP6, FP2) were applied in the modeling process. The best model for each property and its performance can be seen in tables below (Table S2, Table S3). You can click the “Detail” to get the concrete information about all obtained models if you need.
| Model Results
Table S2. The best regression models for the coressponding ADME/T related properties
|property||Method||Train size||Test size||mtry||R2||Q2||R2T||RMSEF||RMSECV||RMSET|
Table S3. The best classification models for the coressponding ADME/T related properties
|Property||Method||fingerprint||Five-fold cross validation||External validation dataset|
| Database contents
The database integrated all the ADMET entries from ChEMBL database, Drugbank database, EPA database and related records from several literatures along with all the data described above. We manually checked the correctness of the values and dropped redundant information which resulted in 208,967 entries. Each entrie including basic molecular properties( eg. , common name, SMILES, ALogp, PSA) and ADMET activities.
| Similarity search
QSAR models and similarity searches are both useful strategies to predict ADMET properties. Compared with QSAR modesl, The similarity searches in databases are fast and can easily be extended to include new information. Here, we provides 7 kinds of fingerprints to represent molecular information and 2 kinds of similarity metrics. Users can input molecules to estimate their properties by comparing with similar compounds.
Not just one property affects the behavior of drugs in body. Usually we are looking for molecules that possess relatively good performance through every stage of ADME/T. Here, we developed this module that allows users to evaluate most aspects of ADME/T process of one molcule. The results provides users an full impression and lead to constructive suggestions of molecular optimization.