Good ADME properties library
Accelerate your drug development journey with a chemical library designed to highlight compounds with promising ADME (Absorption, Distribution, Metabolism, and Elimination) properties. By leveraging advanced AI-driven predictions and carefully selected criteria, this library offers an opportunity to identify molecules with strong potential early in the development process, helping to streamline research and reduce risks.
Chemical library concept
- Thoughtfully Selected Compounds.
The compounds included in the library are chosen based on well-established ADME benchmarks, aiming to support favorable oral bioavailability, therapeutic efficacy, and safety profiles.
- AI-Enhanced Predictions.
Utilizing advanced machine learning models built with the Therapeutics Data Commons (TDC) framework, the library provides reliable predictions for key ADME parameters, offering valuable guidance for early-stage decision-making.
- Support for Efficient Development.
By focusing on compounds with desirable pharmacokinetics and reduced risk of drug-drug interactions, the library may help optimize workflows and prioritize candidates with higher potential for success.
Key Features of the Compounds
- Absorption: Compounds exhibit high intestinal permeability (logPapp > 6.5), which can support good oral bioavailability.
- Distribution: Low plasma protein binding (< 90%) helps ensure sufficient free drug concentration to enhance therapeutic potential.
- Metabolism: Designed to minimize CYP3A4-related challenges:
- Low inhibition potential (< 0.5) may reduce the risk of drug-drug interactions.
- A low likelihood of being a CYP3A4 substrate (probability < 0.5) could contribute to more predictable metabolism.
- Elimination: Low clearance rates (< 30 μL/min/million cells for hepatocytes; < 50 μL/min/mg protein for microsomes) suggest slower metabolism, potentially leading to longer halflife and reduced dosing frequency.
Chemical library development
- HighQuality Data Sources: The library is based on curated datasets containing compounds with known ADME parameters, ensuring broad coverage of druglike chemical space.
- Comprehensive Preprocessing: Rigorous data cleaning and standardization processes were applied to improve reliability and accuracy in predictions.
- Advanced AI Models: Machine learning techniques were used to deliver precise ADME predictions for novel molecules, providing valuable insights for compound selection.
This virtual chemical library is designed as a resource to support researchers in identifying high-potential drug candidates efficiently. By focusing on compounds with favorable ADME profiles, it may help reduce late-stage failures, optimize development costs, and improve the overall success rate of drug discovery efforts.