# Toxicity Modeling Approach

## Overview
The composite modeling approach integrates multiple methodologies to assess compound toxicity. It combines heuristic methods, a global model, and individual nuclear receptor (NR) models, providing a comprehensive and flexible framework for toxicity prediction and confidence scoring.


## Components

### 1. Heuristic
- **Description:** Uses known toxicity data for compounds.
- **Purpose:** Bootstrap UI/tags and acts as a known baseline leveraging knowledge from existing toxicity databases.


### 2. Global Model
- **Description:** A unified model encompassing all nuclear receptor reaction sites.
- **Purpose:** Offers a broad toxicity prediction across diverse receptor interactions.


### 3. Individual NR Models
- **Description:** A set of `N` specialized models, each targeting a specific nuclear receptor (NR).
- **Purpose:** Delivers fine-grained predictions for receptor-specific toxicity.
- **Strengths:**
  - Pinpoints receptor-specific interactions.
  - Enhances interpretability of toxicity mechanisms.

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## Confidence Metric
- **Approach:**
  - Combine outputs from heuristic, global, and NR models.
  - Assess consistency and agreement across models.
- **Purpose:** Quantifies the reliability of toxicity predictions.
- **Advantages:**
  - Increases trust in predictions.
  - Highlights regions of feature space with conflicting or sparse data.


## Applications
- Toxicity screening for drug discovery and chemical safety assessments.
- Identification of high-risk compounds and prioritization for experimental validation.
- Confidence-based decision-making for regulatory and research contexts.

## Future Directions
- Integrate ensemble techniques to further refine model combinations.
- Enhance confidence metrics using additional layers of evidence (e.g., structural similarity, external validation).
- Expand NR model coverage for broader receptor families.