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Explainable Molecular Property Prediction: Aligning Chemical Concepts with Predictions via Language Models
Though transformer-based language models have shown great potential in accurate molecular property prediction, they neither provide chemically meaningful explanations nor faithfully reveal the molecular structure-property relationships.
Multi-View Subgraph Neural Networks: Self-Supervised Learning with Scarce Labeled Data
While graph neural networks (GNNs) have become the de-facto standard for graph-based node classification, they impose a strong assumption on the availability of sufficient labeled samples.
Manifold Interpolation for Large-Scale Multi-Objective Optimization via Generative Adversarial Networks
However, traditional evolutionary algorithms for solving LSMOPs have some deficiencies in dealing with this structural manifold, resulting in poor diversity, local optima, and inefficient searches.
Evolutionary Dynamic Multi-objective Optimization Via Regression Transfer Learning
In recent years, transfer learning has been proven to be a kind of effective approach in solving DMOPs.
