4 citations · 11 across the 13 of their papers we have counts for
7 papers · 1 filter
WMLLM: Self-Evolving Optimization Agents via Predict-Then-Act World Modeling
Zhongzheng Li, Qingsong Ran, Shikun Feng +5
Black-box optimization problems remain challenging because of large, weakly structured, and high-dimensional search spaces. Existing methods often suffer from poor sample efficienc…
MCCE: A Framework for Multi-LLM Collaborative Search in Discrete Spaces with Similarity-Filtered Preference Learning
Nian Ran, Zhongzheng Li, Yue Wang +5
Multi-objective discrete optimization problems, such as molecular design, pose significant challenges due to their vast and unstructured combinatorial spaces. Traditional evolution…
Straight-Line Diffusion Model for Efficient 3D Molecular Generation
Yuyan Ni, Shikun Feng, Haohan Chi +5
Diffusion-based models have shown great promise in molecular generation but often require a large number of sampling steps to generate valid samples. In this paper, we introduce a…
UniGEM: A Unified Approach to Generation and Property Prediction for Molecules
Shikun Feng, Yuyan Ni, Yan Lu +3
Molecular generation and molecular property prediction are both crucial for drug discovery, but they are often developed independently. Inspired by recent studies, which demonstrat…
Pre-training with Fractional Denoising to Enhance Molecular Property Prediction
Yuyan Ni, Shikun Feng, Xin Hong +5
Deep learning methods have been considered promising for accelerating molecular screening in drug discovery and material design. Due to the limited availability of labelled data, v…
Contextual Molecule Representation Learning from Chemical Reaction Knowledge
Han Tang, Shikun Feng, Bicheng Lin +4
In recent years, self-supervised learning has emerged as a powerful tool to harness abundant unlabelled data for representation learning and has been broadly adopted in diverse are…
UniMAP: Universal SMILES-Graph Representation Learning
Shikun Feng, Lixin Yang, Yanwen Huang +3
Molecular representation learning is fundamental for many drug related applications. Most existing molecular pre-training models are limited in using single molecular modality, eit…