SPLIF-Enhanced Attention-Driven 3D CNNs for Precise and Reliable Protein–Ligand Interaction Modeling for METTL3

Structure-based virtual screening (SBVS) is a cornerstone of modern drug discovery pipelines. However, conventional scoring functions often fail to capture the complexities of protein-ligand binding interactions. To address this limitation, we developed DeepMETTL3, a novel scoring function that integrates 3D convolutional neural networks (CNNs) with multihead attention mechanisms and high-dimensional Structural Protein-Ligand Interaction Fingerprints (SPLIF). This approach enables the model to capture intricate 3D interaction patterns while refining and prioritizing features for precise classification of active and inactive compounds. We validated DeepMETTL3 using METTL3 as a therapeutic target, employing a scaffold-based data-splitting strategy and multiple test sets, including challenging sets with minimal chemical similarity to the training data. Our results demonstrate that DeepMETTL3 outperforms traditional scoring functions, achieving superior accuracy, robustness, and scalability. Key findings include the importance of an active-to-decoy ratio (1:50) in the training set for enhanced performance and the optimal placement of the attention mechanism after CNN1 for improved generalization. DeepMETTL3 represents a significant advancement in target-specific machine learning for SBVS, offering a framework that can be adapted to other biological targets. This work underscores the potential of deep learning in artificial intelligence-based drug design, balancing computational efficiency and predictive power in molecular docking and virtual screening. The scoring function is freely available at https://github.com/juniML/DeepMETTL3.