Enhancing Molecular Property Prediction via Mixture of Collaborative
  Experts
Xu Yao Shuang Liang Songqiao Han Hailiang Huang
Molecular Property Prediction (MPP) task involves predicting biochemical
properties based on molecular features, such as molecular graph structures,
contributing to the discovery of lead compounds in drug development. To address
data scarcity and imbalance in MPP, some studies have adopted Graph Neural
Networks (GNN) as an encoder to extract commonalities from molecular graphs.
However, these approaches often use a separate predictor for each task,
neglecting the shared characteristics among predictors corresponding to
different tasks. In response to this limitation, we introduce the GNN-MoCE
architecture. It employs the Mixture of Collaborative Experts (MoCE) as
predictors, exploiting task commonalities while confronting the homogeneity
issue in the expert pool and the decision dominance dilemma within the expert
group. To enhance expert diversity for collaboration among all experts, the
Expert-Specific Projection method is proposed to assign a unique projection
perspective to each expert. To balance decision-making influence for
collaboration within the expert group, the Expert-Specific Loss is presented to
integrate individual expert loss into the weighted decision loss of the group
for more equitable training. Benefiting from the enhancements of MoCE in expert
creation, dynamic expert group formation, and experts' collaboration, our model
demonstrates superior performance over traditional methods on 24 MPP datasets,
especially in tasks with limited data or high imbalance.