My research examines the impact of noise on small molecule activity prediction, identifying robust pairings of model and molecular embedding under increased artificial noise. By clustering molecules into chemical domains, I introduce domain-specific noise to mimic real-world variability. Within a federated learning framework, I address challenges from heterogeneous data distributions and client-specific noise variability by applying noise mitigation methods across both pre-processing and training steps, aiming to remove and smooth experimental noise.