Drug-Target Interaction (DTI) prediction remains a critical problem in drug discovery. Machine learning (ML) has shown great promise in feature-based DTI prediction. However, the vast number of ML architectures and biomolecular representations available makes selecting an appropriate model architecture a challenge. In this work, we propose a modular framework, MoDTI, that facilitates the exploration of three key inductive biases in DTI prediction: protein representation, multi-view learning, and modularity. We evaluate the impact of each of these inductive biases on DTI prediction performance and compare the performance of MoDTI against existing state-of-the-art models on multiple benchmarks. Our findings provide valuable insights into the importance of each component of the MoDTI model for improving DTI prediction, and we present general guidelines for the rapid development of more accurate DTI models. Through extensive empirical evaluation, we demonstrate the effectiveness of our proposed approach and its potential for further understanding key inductive biases for DTI prediction.