The Engine

The QSAR Modeling suite provides advanced modeling and visualization tools for chemical space exploration and predictive compound analysis. It maps molecular diversity, uncovers structure activity patterns, and forecasts properties for hit selection and lead optimization.

Users upload SMILES based datasets and optional activity or ADMET fields, then run configurable analyses that produce interactive 2D and 3D projections, clustering, feature importance, and motif insights, all exportable and tracked in the Command Center.

The Algorithm

The suite unifies complementary methods that progress from exploration to prediction:

• Chemical Space

  • Input handling for SMILES lists or tables with properties.

  • Featurization with fingerprints or learned embeddings, followed by dimensionality reduction using UMAP and t SNE for 3D and 2D projections.

  • Clustering via KMeans, hierarchical, spectral, or Tanimoto distance grouping.

  • Property computation and summaries for MolWT, MolLogP, HBA, HBD, and user columns.

• QSAR Modeling

  • Featurization options include Morgan fingerprints, MACCS keys, GraphConv embeddings, and ChemBERTa encodings.

  • Model families for regression and classification tuned to IC50, Ki, Kd, EC50, and ADMET endpoints.

  • Cluster level statistics, partial dependence and permutation importance, and calibration plots for interpretability.

  • Substructure and motif analysis using BRICS and MMP fragmentation, SMARTS queries, and enrichment scoring across clusters.

Algorithm Validation

Models are evaluated with stratified cross validation, held out test sets, and by randomization checks for leakage detection. Applicability domain is estimated with k nearest neighbor or distance based thresholds so predictions are flagged when outside learned chemical space. Baseline comparisons against simple fingerprint similarities are included, and enrichment of activities in the top ranked decile is reported for screen-like scenarios. Chemical Space outputs provide cluster stability metrics and silhouette scores, while QSAR reports include error bars, ROC AUC or PR AUC for classification, and RMSE or R2 for regression.

Scientific Impact

The suite enables teams to:

• Visualize compound libraries to identify clusters, outliers, and gaps in chemical space.

• Link molecular features to bioactivity and ADMET outcomes through interpretable QSAR models.

• Detect enriched motifs and pharmacophores that correlate with potency or liability.

• Compare series side by side with cluster statistics to guide scaffold selection and replacement.

• Generate ranked predictions that integrate with downstream docking, activity analysis, and medicinal chemistry design.

By combining unbiased exploration with validated prediction, the suite turns raw SMILES tables into testable hypotheses and clear next steps.

Business Impact

The suite improves decision quality and reduces cycle time by:

• Prioritizing diverse, high value candidates using cluster aware hit selection.

• Lowering experimental costs through accurate, applicability aware QSAR forecasts.

• Standardizing analytics with reproducible pipelines, parameter capture, and Command Center tracking.

• Accelerating cross functional reviews using exportable visuals, tables, and motif summaries.

The result is faster movement from large libraries to focused, well justified hit and lead sets, with analytics that scale from rapid triage to in depth portfolio analysis.