Repurformer: Transformers for Repurposing-Aware Molecule Generation
This addresses the need for diverse molecule generation in drug discovery, though it appears incremental by building on existing Transformer methods.
The paper tackled the sample bias problem in molecule generation for drug discovery, where models produce structurally similar molecules targeting the same protein, and proposed Repurformer, which increased diversity by leveraging multi-hop relationships among proteins and compounds, as confirmed on the BindingDB dataset.
Generating as diverse molecules as possible with desired properties is crucial for drug discovery research, which invokes many approaches based on deep generative models today. Despite recent advancements in these models, particularly in variational autoencoders (VAEs), generative adversarial networks (GANs), Transformers, and diffusion models, a significant challenge known as \textit{the sample bias problem} remains. This problem occurs when generated molecules targeting the same protein tend to be structurally similar, reducing the diversity of generation. To address this, we propose leveraging multi-hop relationships among proteins and compounds. Our model, Repurformer, integrates bi-directional pretraining with Fast Fourier Transform (FFT) and low-pass filtering (LPF) to capture complex interactions and generate diverse molecules. A series of experiments on BindingDB dataset confirm that Repurformer successfully creates substitutes for anchor compounds that resemble positive compounds, increasing diversity between the anchor and generated compounds.