Create README.md

README.md

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+---
+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.
+
+
+
+## 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:
+$$h_i^{(k)} = \text{MLP}^{(k)} \left( (1 + \epsilon^{(k)}) \cdot h_i^{(k-1)} + \sum_{j \in \mathcal{N}(i)} h_j^{(k-1)} \right)$$
+
+## 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.
+
+## 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).