DiffDTM: A conditional structure-free framework for bioactive molecules
  generation targeted for dual proteins
Lei Huang Zheng Yuan Huihui Yan Rong Sheng Linjing Liu Fuzhou Wang Weidun Xie Nanjun Chen Fei Huang Songfang Huang Ka-Chun Wong Yaoyun Zhang
Advances in deep generative models shed light on de novo molecule generation
with desired properties. However, molecule generation targeted for dual protein
targets still faces formidable challenges including protein 3D structure data
requisition for model training, auto-regressive sampling, and model
generalization for unseen targets. Here, we proposed DiffDTM, a novel
conditional structure-free deep generative model based on a diffusion model for
dual targets based molecule generation to address the above issues.
Specifically, DiffDTM receives protein sequences and molecular graphs as inputs
instead of protein and molecular conformations and incorporates an information
fusion module to achieve conditional generation in a one-shot manner. We have
conducted comprehensive multi-view experiments to demonstrate that DiffDTM can
generate drug-like, synthesis-accessible, novel, and high-binding affinity
molecules targeting specific dual proteins, outperforming the state-of-the-art
(SOTA) models in terms of multiple evaluation metrics. Furthermore, we utilized
DiffDTM to generate molecules towards dopamine receptor D2 and
5-hydroxytryptamine receptor 1A as new antipsychotics. The experimental results
indicate that DiffDTM can be easily plugged into unseen dual targets to
generate bioactive molecules, addressing the issues of requiring insufficient
active molecule data for training as well as the need to retrain when
encountering new targets.