ADMET Predictor™

ADMET property prediction and
QSAR model-building application

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Powerful ADMET Property Estimation, at your fingertips!

As a drug or agrochemical discovery scientist, your task is to create high quality lead compounds. As toxicologist in the pharmaceutical, chemicals, food, and cosmetic industries your task is to make sure that your products are safe for humans and the environment.  The ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties of your molecules are of vital importance.  The ability to quickly and accurately predict these properties simply from the 2D structure of the molecule is extremely helpful in making decisions that can determine the success of your project.  ADMET Predictor is state of the art ADMET property prediction software.

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What is ADMET Predictor?

ADMET Predictor is a software tool that quickly and accurately predicts over 140 properties including solubility, logP, pKa, sites of CYP metabolism, and Ames mutagenicity. The ADMET Modeler™ module in ADMET Predictor allows one to rapidly and easily create high quality QSAR/QSPR models based on your own data. The newest module offers advanced data mining, clustering, and matched molecular pair analysis.  The program has an intuitive user interface that allows one to easily manipulate and visualize data.

The six modules in ADMET Predictor are available:

Each module can be purchased individually.

What are we providing with ADMET Predictor?

The ADMET Predictor development team strives to build the best QSAR/QSPR models and fast, efficient software.  This dedication to science is a major reason why several of our models have ranked #1 in peer review articles.  ADMET Predictor provides:

  • Over 140 predicted properties
  • Model applicability domain
  • pKa prediction including all microstates
  • CYP metabolite generation and kinetic parameters
  • Confidence estimates for classification models
  • A revamped, intuitive interface with pulldown menus, quick access icons, and context specific menus
  • Interactive distribution and 2D and 3D scatters plots
  • Ability to create your own QSPR/QSAR models
  • Excellent customer support
How is ADMET Predictor being applied?

Biotechnology, pharmaceutical, and chemical companies license ADMET Predictor for diverse number of applications including:

  • Physicochemical property prediction of real and virtual compounds
  • Metabolite prediction
  • Toxicity prediction
  • Prediction of dose needed to achieve a specific blood level concentration
  • Analysis of high throughput screening data
  • Matched molecule pair analysis and activity cliff detection
  • SAR analysis including R-group creation and analysis
  • Similarity searching
  • Creating diverse compounds subsets
  • Enumerating combinatorial chemistry libraries
  • Calculation of various binding metrics, e.g. lipophillic ligand efficiency (LLE)
User Interface

While there isn’t an ADMET Predictor “Basic Module”, there are several tools available when one of the above modules is licensed.  These include tools for inputting structures and data, manipulating rows and columns, visualizing data, and performing data and structural queries.

Structure Input

ADMET Predictor can open several file types of files including:

  • SDF
  • RDF
  • MOL
  • SMILES
  • XTK
  • CTK

The last two formats, XTK and CTK, are Simulations Plus formats. The XTK format saves all of the properties and graphics so that one can easily start a new ADMET Predictor session in the same state as the previous session.

One can also enter and modify structures with our MedChem Designer™ chemical drawing software package.  Property data in structure files can be read into ADMET Predictor at the same time as the structures or one can import data from tab separated text files.

Data Visualization: Heat maps, Star, Distribution, and Scatter Plots

Cells can be colored with either Red-Yellow-Green or Blue-White-Red color progression schemes to create “heat maps”.  The attributes in these cells can contain either text or numeric data.

heat-map

Star plots are automatically created after ADMET properties are computed.  They give instant visual feedback on various properties of the molecule because the length of each wedge is related to the property’s value.

starplots

A distribution and scatter plot are always available on the right hand side of ADMET Predictor. The graphs can be undocked, resized and positioned anywhere on your computer screen. Rows that are selected in the spreadsheet can be highlighted in the distribution plot using “Two-color mode”. This is illustrated in the figure below.

histogram

An interactive, 2D scatter plot is also docked on the right side of ADMET Predictor. The axes can be log-scaled or linear and the points can be colored based on an attribute in the spreadsheet. There are a great deal of options including specifying the minimum and maximum axes range, point type (circle, square, triangle, etc.), point size, background color, and font size. The structure and data associated with a point in the graph can be displayed simply by hoovering the mouse over the point. Compounds highlighted in the spreadsheet can be highlighted in the scatter plot and vice versa. Out of scope predictions can also be highlighted. A best fit line can be computed and displayed along with various statistics (R2, RMSE, slope, etc.).

scatter

 

ADMET Risk™

ADMET Risk™

The original Rule of 5 is widely considered to be an important development in modern drug discovery (Lipinski, et al; 1997). The Rule of 5 takes on numeric values from 0 to 4 as a measure of the compounds potential absorption liability. As such, the Rule of 5 is a useful computational filter in drug candidate screening. In terms of ADMET Predictor descriptors and models, the Rule Of 5 model rules can be formulated as follow the following set of conditions:

  • MlogP > 4.15 (excessive lipophilicity)
  • MWt > 500 (large size)
  • HBDH > 5 (too many potential hydrogen bond donors)
  • M_NO > 10 (too many potential hydrogen bond acceptors)

Most commercial drugs suitable for oral dosing violate no more than one of the rules these conditions represent.

As an extension of that concept, Simulations Plus has created a series of “ADMET Risk” rule sets and calibrated them against our own ADMET models. They are parameterized to include thresholds for a wide range of calculated and predicted properties that represent potential obstacles to a compound being successfully developed as an orally bioavailable drug. These thresholds were obtained by focusing in on a specific subset of drugs in the World Drug Index (WDI). Similar to the methodology used by Lipinski et al., we removed irrelevant compounds from a 2008 version of the WDI. In particular, we removed phosphates, antiseptics, insecticides, emollients, laxatives, etc., as well as any compound that did not have an associated United States Adopted Name (USAN) or International Non-proprietary Name (INN) identifier. The structure of the principal component in salts was extracted and neutralized, after which duplicate structures were removed. This left us with a data set of 2,316 molecules, 8.3% of which violated more than one of Lipinski’s rules.

admet-risk-1

An illustration of “soft” thresholds for an inequality rule. The score starts increasing linearly from 0 at “start value” of the descriptor in the neighborhood of the boundary B, and reaches 1 at the “end value” value of the descriptor.

Rule of 5 only addresses a narrow slice of the full gamut of hurdles a compound must pass before it can become a drug. In addition, it relies on “hard” thresholds: a compound with a molecular weight of 499 satisfies the MWt rule but a compound with a molecular weight of 501 violates it.

We calculated a broad range of relevant molecular descriptors and ADMET property predictions for the focused subset of WDI and identified “soft” threshold ranges for each along the lines suggested by (Petit; 2012) such that approximately 85% of the compounds in the data set satisfy them completely and somewhat less than 10% violate them completely. The former contribute nothing to the overall Risk, whereas the latter contribute the full amount (weight) specified for the corresponding rule. Predictions falling in the gray area in between contribute fractional amounts to the Risk Score. The concept is illustrated on the left.

Highly correlated criteria were combined into single rules using Boolean operators. The rules for identifying overly large structures, for example, is:

size (Sz): MWt > [450,550] OR N_Atoms > [32,38] OR MolVol > [475,550] OR N_Bonds > [35,41]

where the values within the brackets indicate the boundaries of threshold regions. The Sz rule includes four individual criteria, all of which use the “>” relational operator. Predictions falling below the minimum threshold values contribute nothing to the Risk, whereas predictions above the maximum contribute 1 violation “point”. Intermediate values represent intermediate risks: a compound of molecular weight 500 violates the first criterion and so would represent an incremental Risk of 0.5 points for that criterion. Logical operators such as ORs and ANDs can also be included in the rules. The combined points from the criteria making up a rule then yield an overall value between 0 and 1, which is multiplied by the weight assigned to the rule as a whole.

The overall ADMET_Risk is the sum of three risks:

  • Absn_Risk – risk of low fraction absorbed (PCB Module models)
  • CYP_Risk – risk of high CYP metabolism (MET Module models)
  • TOX_Risk – toxicity related risks (TOX Module models)

Two additional pharmacokinetic risks (high plasma protein binding and high steady-state volume of distribution) are also included in the ADMET_Risk score.

References

Lipinski CA, Lombardo F, Dominy BW, Feeney PJ. “Experimental and Computational Approaches to Estimate Solubility and Permeability in Drug Discovery and Development Settings.” Adv Drug Delivery Rev. 1997; 23:3-25.

Petit J, Meurice N, Kaiser C, Maggiora G. “Softening the Rule of Five – where to draw the line?” Bioorg Med Chem. 2012; 20:5343-5351.

How do I move forward from here?

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