In Silico ADMET Prediction Service
Absorption, distribution, metabolism, excretion, and toxicity (ADMET) describe the pharmacokinetic and pharmacodynamic properties of drug molecules and are key parameters for the discovery and optimization of new drugs. In silico ADMET predictions provide a powerful approach to assess these properties early in drug development, thereby reducing the time, cost, and risk of experimental screening. CD ComputaBio's team of experts combines expertise in medicinal chemistry, pharmacology, and computational science to develop and deliver best-in-class in silico ADMET prediction solutions.
Introduction to ADMET Prediction
Drug research and development (R&D) is a "knowledge-intensive" process with high risk, high investment, and a long cycle. From the initial discovery to the final market launch of a new drug, it usually takes billions of dollars and more than ten years. Early ADMET property evaluation studies can significantly improve the success rate of drugs, reduce drug development costs, minimize the occurrence of side effects and toxicity, and provide a direct therapeutic principle for the use of drugs. However, traditional experimental evaluation of ADMET-related properties can be a time-consuming and costly process, requiring a large number of animal experiments in the early stages of drug development. In silico ADMET prediction has attracted widespread attention from pharmaceutical scientists as a rational drug design tool. By constructing a prediction system based on computer models, the ADMET properties of compounds are predicted using existing experimental data and advanced algorithms. This approach can support high-throughput screening of large-scale compound libraries, provide in-depth insights into pharmacological effects, and help ensure the safety of products.
Fig. 1 In Silico ADMET prediction strategies. (Sharma B, et al.; 2023)
Our Services
CD ComputaBio has extensive experience in building quantitative structure-activity relationship (QSAR) and ADMET prediction models. Our scientists can use project data or published data to predict different ADMET properties of multiple compounds and support optimization in the drug design process. Through advanced computational tools and professional models, we help customers evaluate the safety and efficacy of candidate compounds, optimize their pharmacokinetic and toxicological properties, reduce R&D risks, and accelerate the development of new drugs.
ADME Prediction Service:
Absorption Prediction
- Oral bioavailability prediction.
- P-glycoprotein (P-gp) substrate/inhibitor prediction.
- Intestinal permeability prediction.
- Caco-2 cell model prediction.
Distribution Prediction
- Blood-brain barrier (BBB) permeability prediction.
- Plasma protein binding rate prediction.
- Tissue distribution prediction.
- Tissue distribution coefficient (Kp) prediction.
Metabolism Prediction
- Metabolism stability prediction.
- CYP450 enzyme system prediction.
- Metabolite prediction.
- Metabolism rate and body clearance rate prediction.
Excretion Prediction
- Renal excretion prediction.
- Bile excretion prediction.
- Half-life prediction.
- Excretion rate prediction.
More
Please note that our ADME prediction services are modular. You have the flexibility to use each service individually as standalone tools or combine them into a comprehensive solution, depending on your research needs.
- Acute toxicity (LD50) prediction.
- Chronic toxicity prediction.
- Mutagenicity prediction.
- Carcinogenicity prediction.
- Cardiac toxicity prediction.
- Renal toxicity prediction.
- Reproductive toxicity prediction.
- Hepatotoxicity prediction.
- More
The single or integrated ADMET prediction services provided by CD ComputaBio can meet the R&D needs of different drug molecules of different customers. Utilizing advanced computational methods and deep domain knowledge, our team of experts can provide customized ADMET prediction services based on the customer's target compound or molecule. Our expertise includes but is not limited to:
Methods For In Silico ADMET Prediction
QSAR Modeling
QSAR models explore the relationship between chemical structure and biological activity. By analyzing a large dataset of existing compounds, we can predict unknown properties based on structural features.
Machine Learning
Machine learning and artificial intelligence (AI) methods excel in processing complex, nonlinear, and high-dimensional data. We use these algorithms to build highly accurate ADMET prediction models.
Molecular Modeling
Using molecular docking and molecular dynamics simulations, we study the interactions between compounds and biomacromolecules such as enzymes, receptors, and transporters to predict their metabolic pathways, absorption characteristics, and toxicity risks.
Service Highlights
Large-Scale Compound Library Screening
We can use ADMET prediction models to efficiently screen a large number of compounds and support the structural modification and optimization of customers' lead compounds.
High-Performance Computing Resources
We have cutting-edge computing technologies such as QSAR models, machine learning, and artificial intelligence algorithms to help customers gain a deep understanding of the properties of compounds.
Customized Solutions
We offer bespoke ADMET prediction services by developing dedicated models tailored to our client's specific targets or compound series. This personalized approach ensures that the predictions are highly relevant and actionable.
In silico ADMET predictions are an essential tool in modern drug design. CD ComputaBio is committed to providing high-quality services to help you make informed decisions and accelerate safe and effective drug development. Please don't hesitate to contact us, if you are interested in our services.
References:
- Wu F, et al. Computational Approaches in Preclinical Studies on Drug Discovery and Development. Front Chem. 2020;8:726.
- Kar S, et al. Leszczynski J. In Silico Tools and Software to Predict ADMET of New Drug Candidates. Methods Mol Biol. 2022;2425:85-115.
* For Research Use Only.
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