Accurate metabolism insights, tailored for DMPK scientists

With Semeta, you can:

• Predict in vivo metabolite profiles confidently, with higher precision than other available software. Use this information to interpret metabolite-ID experiments.
• Complement in vitro and in vivo DMPK studies with comprehensive in silico modelling, to identify which enzymes and isoforms will metabolise your compounds, sites of metabolism and likely metabolites.
• Identify compounds with multiple routes of clearance, to reduce the risk of drug-drug interactions or issues from genetic polymorphisms.
• Choose the most appropriate preclinical species for testing by comparing cytochrome P450 regioselectivity between human and animal species.
• Guide compound design by identifying sites vulnerable to metabolism to optimise and balancing properties using a Probabilistic Scoring approach.

HIGHLY SENSITIVE, HIGHLY PRECISE

Metabolism Modelling

Our quantum mechanics and machine learning models predict metabolic routes, sites and products with greater precision and sensitivity than all other market methods.

Based off a fundamental mechanistic understanding of each enzyme family, our models allow you to identify sites of metabolism for key enzymes involved across human Phase I and II metabolism, including:

• Cytochrome P450s (CYPs)
• Flavin-containing monooxygenases (FMOs)
• Aldehyde oxidases (AOXs)
• Uridine diphosphate glucuronosyltransferases (UGTs)
• Sulfotransferases (SULTs)

Models for rat, dog and mouse CYPs, enable comparison of the sites of metabolism for common preclinical species with human predictions, so you can choose the most appropriate species for experimental studies.

Select peer-reviewed publications:

• Predicting routes of Phase I and II metabolism based on quantum mechanics and machine learning (Xenobiotica, 2023)
• Predicting regioselectivity of AO, CYP, FMO and UGT metabolism using quantum mechanical simulations and machine learning (J. Med. Chem., 2022)

More metabolism resources