| | --- |
| | license: mit |
| | tags: |
| | - biology |
| | - chemistry |
| | - molecular-property-prediction |
| | - gnn |
| | - drug-discovery |
| | --- |
| | |
| | # molecular_bioactivity_predictor_gnn |
| | |
| | ## Overview |
| | This model utilizes a Graph Isomorphism Network (GIN) to predict the bioactivity and binding affinity ($K_i$) of small molecules against specific protein targets. By representing molecules as graphs where atoms are nodes and bonds are edges, the model captures complex spatial relationships crucial for pharmacological efficacy. |
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| | ## Model Architecture |
| | The model implements a **Message Passing Neural Network (MPNN)** using the GIN convolution operator. |
| | - **Node Features**: Includes atomic number, chirality, hybridization, and formal charge. |
| | - **Edge Features**: Includes bond type (single, double, triple, aromatic) and stereochemistry. |
| | - **Readout Layer**: Global Mean Pooling followed by a 3-layer MLP. |
| | - **Aggregation**: The update rule for node $i$ at layer $k$ is defined as: |
| | $$h_i^{(k)} = \text{MLP}^{(k)} \left( (1 + \epsilon^{(k)}) \cdot h_i^{(k-1)} + \sum_{j \in \mathcal{N}(i)} h_j^{(k-1)} \right)$$ |
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| | ## Intended Use |
| | - **Virtual Screening**: Ranking massive libraries of compounds to identify potential lead candidates for synthesis. |
| | - **ADMET Prediction**: Estimating the solubility and lipophilicity of new chemical entities. |
| | - **Target Profiling**: Predicting potential off-target interactions to minimize clinical side effects. |
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| | ## Limitations |
| | - **Stereoisomers**: The model may struggle to differentiate between complex enantiomers that have identical connectivity but different biological activity. |
| | - **Large Molecules**: It is primarily validated on small molecules (MW < 800 Da) and may not generalize to biologics or large macrocycles. |
| | - **Dataset Bias**: Prediction accuracy is highly dependent on the chemical diversity of the training set (e.g., ChEMBL or PDBBind). |
