To enhance the denoising capabilities of the Multimodal Pretraining DEL-Fusion (MPDF) model, one promising approach is to integrate 3D structural information of compounds. This can be achieved by incorporating 3D molecular representations, such as molecular dynamics simulations or 3D pharmacophore models, which capture the spatial arrangement of atoms and their interactions in a more comprehensive manner than 2D representations alone.
3D Convolutional Neural Networks (3D CNNs): By employing 3D CNNs, the model can learn spatial hierarchies of features from 3D molecular structures. This would allow the MPDF model to capture intricate interactions that are not evident in 2D representations, thereby improving the accuracy of denoising.
Graph-based 3D Representations: Extending the existing Graph Convolutional Network (GCN) architecture to incorporate 3D coordinates can provide a richer feature set. This can be done by augmenting the node features in the graph to include 3D spatial information, allowing the model to learn from both the connectivity and the spatial arrangement of atoms.
Integration of 3D Descriptors: Utilizing 3D molecular descriptors, such as molecular volume, surface area, and shape, can provide additional context for the denoising process. These descriptors can be integrated into the existing framework as supplementary features, enhancing the model's ability to differentiate between active and inactive compounds.
Multi-Scale Fusion: The current DEL-fusion framework can be adapted to include 3D features by creating a multi-scale fusion approach that combines 2D and 3D representations. This would allow the model to leverage the strengths of both types of data, leading to improved feature extraction and denoising performance.
By incorporating these additional compound representations, the MPDF model can achieve a more nuanced understanding of molecular interactions, ultimately enhancing its denoising capabilities and improving the identification of high-affinity compounds in drug discovery.