As a discovery chemist or chemical risk assessment scientist, you are required to design and/or evaluate high-quality compounds that are safe, effective, novel, and easy to make.
ADMET Predictor® is the flagship AI/ML platform for ADMET modeling with extended capabilities for data analysis, extensive metabolism predictions, virtual screening, AI-driven drug design, integrated HT-PBPK simulations, and rapid mechanistic liver safety risk assessments.
ADMET Predictor is an AI/ML software platform that quickly and accurately predicts over 175 properties, including aqueous and biorelevant solubility vs. pH profiles, logD vs. pH curves, pKa, CYP and UGT metabolism outcomes, key toxicity endpoints including Ames mutagenicity and drug-induced liver injury (DILI) mechanisms, and major systemic PK endpoints using integrated high-throughput GastroPlus® PBPK simulations.
It integrates market-leading ADMET modeling capabilities with 3D ligand-based virtual screening, AI-driven drug design, data analysis, SAR, and cheminformatics capabilities to support scientists across computational chemistry, medicinal chemistry, DMPK, safety assessment, and other disciplines.
The ADMET (absorption, distribution, metabolism, excretion, and toxicity) and pharmacokinetic properties of your molecules are of vital importance. The ability to quickly and accurately predict these properties for thousands of molecules in seconds is beneficial in making decisions that can determine the success of your project. ADMET Predictor is a state-of-the-art platform that can support your entire early-stage discovery program through a M.A.P. workflow – molecular discovery, ADMET prediction and PK prediction. By integrating ADMET and PK awareness into the earliest design iterations, discovery chemists can identify and prioritize compounds that are both active and viable, streamlining the design-make-test-analyze (DMTA) cycle and reducing late-stage attrition.
ADMET Predictor provides powerful cheminformatics solutions that support your design and lead optimization efforts, including the ability to conduct scaffold clustering, R group analysis, and MCDA-based prioritization. It can also generate 3D conformations to conduct ligand-based virtual screening against internal or commercial libraries. In addition to advanced volumetric shape and pharmacophore similarity, you can triage and rank hit selections based on predicted ADMET and PK properties. Furthermore, you can use our generative chemistry and multi-parameter optimization engine, the AI-Driven Drug Design (AIDD) module, to generate completely novel molecules that can be simultaneously optimized for activity, 3D similarity, and any other ADMET property models, as well as in vivo PK properties.
With the ability to predict 175+ ADMET and PK properties, you can triage compounds to select for synthesis and screening. ADMET Predictor has a choice of modules to support your requirements, such as the Physicochemical and Biopharmaceuticals (PCB) module, that includes our flagship pKa model amongst others, or our Metabolism & Transporters module, which allows you to predict metabolites across most CYP enzymes, or consider liver microsome and hepatocyte clearance for five species (human, monkey, dog, rate and mouse). You can also actively predict for Toxicity such as for hERG binding and AMES mutagenesis.
While we’ve built global models using both public and partner data and innovative molecular and atomic descriptors, we recognize you have your own proprietary datasets that support your specific programs. You can use your data to retrain our models for your needs, as well as use ADMET Modeler to construct your own structure-property and structure-activity models using our advanced descriptors.
The ability to conduct High-Throughput Pharmacokinetic (HTPK) Simulations, underpinned by our GastroPlus® ACAT™ model, allows designs to be evaluated against key PK parameters such as % fraction absorbed, volume of distribution and systemic clearance. The power of having such information at the earliest opportunity in a discovery process streamlines the DMTA cycle by focusing on the right compounds.
ADMET Predictor’s enterprise-ready automation—whether through our REST API functionality, Python wrappers, KNIME components or other data connectors—provides capabilities for integrating over 175 ADMET Predictor property calculations into 3rd party informatics platforms such as Certara D360, Schrödinger LiveDesign®, or BIOVIA Pipeline Pilot to support your DMTA cycle.
Each of the 9 modules in ADMET Predictor can be licensed individually.
ADMET Predictor is our AI/ML software platform that quickly and accurately predicts over 175 properties, including aqueous and biorelevant solubility vs. pH profiles, logD vs. pH curves, pKa, CYP and UGT metabolism outcomes, key toxicity endpoints including Ames mutagenicity and drug-induced liver injury (DILI) mechanisms, and major systemic PK endpoints using integrated high-throughput GastroPlus® PBPK simulations. Our models are trained on premium datasets spanning public and private partner sources, and several have ranked #1 in independent peer-reviewed comparisons.
ADMET Predictor provides you with:
Biotechnology, pharmaceutical, and chemical companies license ADMET Predictor for diverse number of applications including:
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:
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.
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.
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.
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:
Two additional pharmacokinetic risks (high plasma protein binding and high steady-state volume of distribution) are also included in the ADMET_Risk score.
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.
Key enhancements include…
