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ADMET Prediction Service - Neorabio

Neorabio provides ADMET prediction services to help research teams evaluate drug-like behavior before committing to costly experimental studies. Computational assessment of absorption, distribution, metabolism, excretion, and toxicity has become central to reducing project risk, a need underscored in early pharmacokinetic modeling work such as that discussed by van de Waterbeemd and Gifford (2003). Neorabio's workflows combine mechanistic modeling with machine-learning approaches, enabling early identification of liabilities and guiding molecular refinement with data-driven confidence.

About Service

Our ADMET prediction platform integrates QSAR modeling, PBPK simulation, curated toxicity analysis, and multi-algorithm machine-learning systems. These methods, supported by validated descriptor sets and large annotated datasets—similar to those examined by Cheng et al. (2012)—allow robust prediction across chemical classes. Neorabio enhances these methodologies with standardized data curation, internal benchmarking, and reproducible computational pipelines, yielding interpretable results suitable for medicinal chemistry and pharmacokinetics teams.

Key Advantages

● Multi-parameter prediction: Assessment of absorption, distribution, metabolism, excretion, and toxicity within one integrated workflow.
● Hybrid mechanistic + AI modeling: Use of PBPK logic, QSAR descriptors, and deep learning to improve prediction breadth and generality.
● Descriptor and feature transparency: Physicochemical drivers, structural alerts, and metabolic risk indicators are clearly annotated.
● Flexible workflows: Adaptable for hit triage, lead optimization, or toxicity-focused evaluation.
● Rigorous validation practices: Cross-validation, external test sets, and bias assessment ensure stable model performance.

Applications

● Early candidate prioritization: Filtering chemical libraries based on ADMET feasibility.
● Lead optimization guidance: Identifying structural liabilities and suggesting modification directions.
● Safety and toxicity assessment: Predicting off-target toxicities, metabolic risks, and reactive features.
● Drug-likeness and PK profiling: Evaluating solubility, permeability, clearance tendencies, and tissue-distribution potential.
● Support for integrated design workflows: Providing ADMET insights for docking, MD simulations, or analog-series design.

Workflow

Structure Submission → Preprocessing → Parameter Setup → ADMET Analysis → Report Delivery

References

1.van de Waterbeemd H., Gifford E. ADMET in silico modelling: towards prediction paradise? Nature Reviews Drug Discovery. 2003;2(3):192–204. doi:10.1038/nrd1032
2.Cheng F., Li W., Liu G., Tang Y. In silico ADMET prediction: recent advances, current challenges and future trends. Current Topics in Medicinal Chemistry. 2013;13(11):1273–1289.
3.Raies A.B., Bajic V.B. In silico toxicology: computational methods for the prediction of chemical toxicity. Wiley Interdisciplinary Reviews: Computational Molecular Science. 2016;6(2):147–172. doi:10.1002/wcms.1240
4.Kirchmair J., et al. Predicting drug metabolism: experiment and/or computation? Nature Reviews Drug Discovery. 2015;14(6):387–404. doi:10.1038/nrd4581

Inquiry Center

Neorabio's cheminformatics and computational pharmacokinetics specialists design prediction workflows tailored to each project's chemical space and development stage. Before model execution, molecular structures undergo consistency checks, descriptor validation, and risk-feature screening. Deliverables include ranked compound lists, ADMET scorecards, toxicity alerts, mechanistic rationales, and recommendations for synthetic or computational follow-up. This integrated approach ensures research teams gain actionable insight into molecule behavior long before in-vitro or in-vivo testing begins.

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