Transforming Data Quality for Next-Generation AI Models
Public datasets have fundamental limitations that reduce model accuracy
Literature-derived datasets lack standardized verification, with missing experimental details and quality controls. AI models built on this unverified data may perform well in cross-validation but fail dramatically when applied to novel compounds in real experimental settings.
Public databases overwhelmingly contain active compounds while unsuccessful experiments remain unpublished. This imbalance leads AI models to overpredict activity and produce high false-positive rates, as they've rarely been trained on truly inactive compounds.
Bioactivity and ADMET data comes from experiments with varying protocols, reagents, and conditions. What appears as a single property (like "solubility" or "IC50") can represent fundamentally different measurements depending on experimental setup, causing models to learn artifacts rather than intrinsic molecular properties.
Public datasets have significant gaps, focusing heavily on popular scaffolds and "drug-like" compounds. Most molecules are tested against only a fraction of possible targets, creating an incomplete activity matrix that limits model performance when encountering novel chemical structures or target families.
Our proprietary dataset delivers superior model performance
All compounds and activity data undergo rigorous verification through standardized protocols and quality control measures.
Comprehensive inclusion of both active and inactive compounds to prevent model bias and reduce false-positive predictions.
All experimental data normalized to consistent conditions with complete metadata for true comparability across measurements.
Extensive absorption, distribution, metabolism, excretion, and toxicity data for more accurate predictive models.
Deliberately curated to include novel scaffolds and underrepresented chemical classes to improve model generalization.
Pre-processed and formatted for immediate integration with popular machine learning frameworks and pipelines.
Accuracy: 0.35
Cohen Kappa Score: 0.044
Accuracy: 0.8
Cohen Kappa Score: 0.565
Schedule a consultation with our data science team and discover how ChemDiv can enhance your AI/ML models and accelerate your drug discovery pipeline.
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