Multimodal Embedding Model Development

Develop a multimodal embedding model using a framework like PyTorch or TensorFlow, conceptually integrating aspects of Gemini 2.5 Flash's ability to handle diverse inputs. The model should learn to project chemical structures, protein sequences, and potentially other contextual data into a unified, high-dimensional vector space. Train your model to ensure that semantically similar drug candidates and targets are close in this embedding space. Use Weights & Biases to track training progress, loss, and embedding quality metrics.