This model is a feedforward neural network trained to predict protein-ligand binding affinity values using embeddings of proteins and ligands. Protein embeddings are generated using ProtT5, and ligand embeddings using ChemBERTa. The model predicts continuous affinity metrics such as Kd, Ki, or IC50, enabling virtual screening and drug discovery tasks without requiring explicit 3D structural data.

Use this model to estimate protein-ligand binding affinity from precomputed embeddings for downstream drug discovery tasks such as virtual screening and prioritizing candidate molecules.

This model is not designed for predicting 3D molecular conformations or generating new molecules. It also may not generalize well outside the chemical space represented in the training data.

The model inherits biases present in the training data, including chemical diversity and assay variability. Predictions should be interpreted with caution and validated experimentally. The model does not account for explicit structural or dynamic effects of proteins or ligands.