AI for Drug Interactions

Drug Interaction Representation

Binary Interaction Matrix

Interaction Types

Polypharmacy Risk Models

class PolypharmacyRiskModel(nn.Module):
    def __init__(self, drug_dim=256, n_heads=8):
        super().__init__()
        self.drug_proj = nn.Linear(drug_dim, 128)
        encoder_layer = nn.TransformerEncoderLayer(128, n_heads, batch_first=True)
        self.transformer = nn.TransformerEncoder(encoder_layer, num_layers=3)
        self.risk_scorer = nn.Linear(128, 1)

    def forward(self, drug_embeddings, mask=None):
        x = self.drug_proj(drug_embeddings)
        h = self.transformer(x, src_key_padding_mask=mask)
        return torch.sigmoid(self.risk_scorer(h.mean(dim=1)))

Graph Neural Networks

Knowledge Graph Embedding

class DDIKnowledgeGraph(nn.Module):
    def __init__(self, n_entities, n_relations, embed_dim=128):
        super().__init__()
        self.entity_embed = nn.Embedding(n_entities, embed_dim)
        self.relation_embed = nn.Embedding(n_relations, embed_dim)

    def score(self, head, relation, tail):
        h = self.entity_embed(head)
        r = self.relation_embed(relation)
        t = self.entity_embed(tail)
        return -torch.norm(h + r - t, p=2, dim=-1)

CYP450 Enzyme Inhibition

Evaluation

• AUROC for interaction prediction
• Precision@K for screening
• Sensitivity for severe interactions