CD ComputaBio is dedicated to effectively extracting structural features, including processing small molecule and protein structure, through deep neural network algorithms. It virtually predicts and evaluates absorption, distribution, metabolism, excretion, toxicity (ADMET), and other properties of small molecule structures on cell, protein, and disease levels. Function modules in the platform enable our clients to conveniently perform several types of drug-likeness analysis, ADMET endpoints prediction, systematic evaluation, and database/similarity searching.
ADMET prediction refers to the assessment of a compound's pharmacokinetic properties alongside its potential toxicity. Understanding these characteristics is vital in the early stages of drug discovery, as they can significantly influence a compound's viability as a therapeutic agent. The ability to predict ADMET properties helps in drug candidate optimization, ultimately saving time and resources in the drug development process.
CD ComputaBio is committed to providing services through advanced deep neural network algorithms, including processing small molecules and protein structures, and ADMET property prediction.
Absorption Prediction
We assess the likelihood of a drug being effectively absorbed through the gastrointestinal tract, incorporating factors like solubility and permeability.
Distribution Prediction
Our models predict the degree to which a drug distributes throughout bodily tissues and fluids. We analyze how well a drug binds to plasma proteins, impacting its therapeutic effect and safety profile.
Metabolism Prediction
We predict the potential pathways of metabolism engaging various cytochrome P450 isoforms, influencing drug clearance rates. Our metabolite identification service includes identifying and characterizing significant metabolites.
Excretion Prediction
We help predict drug elimination via renal mechanisms for safety assessments and also consider biliary elimination for overall drug clearance understanding.
Toxicity Prediction
We use predictive models to assess compound toxicity, mitigating drug development risks. Leveraging databases and algorithms, we evaluate new candidates for carcinogenic and mutagenic properties.
QSAR Models
We utilize state-of-the-art QSAR models to relate molecular structure with biological activity. By analyzing large datasets, our models efficiently predict ADMET properties based on chemical descriptors derived from molecular structures.
Machine Learning Approaches
Harnessing machine learning algorithms allows us to build predictive models that learn from past data. We utilize supervised and unsupervised learning techniques to enhance the accuracy of our ADMET predictions.
Docking Studies
For metabolism predictions, we conduct molecular docking studies to evaluate interactions between drug candidates and key metabolic enzymes. This enables precise predictions of metabolites and their possible pathways.
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At CD ComputaBio, we offer comprehensive ADMET prediction services that leverage advanced AI methods to provide insightful analyses, assisting companies in making informed decisions in their drug discovery processes. If you are interested in our services or have any questions, please feel free to contact us.
Services