Chemical representation learning has gained increasing interest due to the limited availability of supervised data in fields such as drug and materials design. This interest particularly extends to chemical language representation learning, which involves pre-training Transformers on SMILES sequences -- textual descriptors of molecules. Despite its success in molecular property prediction, current practices often lead to overfitting and limited scalability due to early convergence. In this paper, we introduce a novel chemical language representation learning framework, called MolTRES, to address these issues. MolTRES incorporates generator-discriminator training, allowing the model to learn from more challenging examples that require structural understanding. In addition, we enrich molecular representations by transferring knowledge from scientific literature by integrating external materials embedding. Experimental results show that our model outperforms existing state-of-the-art models on popular molecular property prediction tasks.
SMILES-Mamba: Chemical Mamba Foundation Models for Drug ADMET Prediction
Rensselaer Polytechnic Institute（伦斯勒理工学院）；Stanford University（斯坦福大学）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；University of Washington（华盛顿大学）；Virginia Tech（弗吉尼亚理工大学）
PolyFusionAgent: A Multimodal Foundation Model and Autonomous AI Assistant for Polymer Property Prediction and Inverse Design
Department of Applied Computer Science, The University of Winnipeg（应用计算机科学系，温尼伯大学）；Department of Mechanical Engineering, University of Manitoba（机械工程系，曼尼托巴大学）
QALPA: Property-guided diffusion modeling for efficient exploration of chemical spaces of flexible molecules
TUD Dresden University of Technology（德累斯顿工业大学）；Pfizer Worldwide R&D（辉瑞全球研发中心）；Center for Advanced Systems Understanding (CASUS)（高级系统理解中心（CASUS））；Helmholtz Zentrum Dresden-Rossendorf（亥姆霍兹德累斯顿罗森多夫研究中心）
Mind the Retrosynthesis Gap: Bridging the divide between Single-step and Multi-step Retrosynthesis Prediction
Leiden University（莱顿大学）；Helmholtz Zentrum München（慕尼黑亥姆霍兹中心）；AstraZeneca（阿斯利康）；Janssen Research & Development（杨森研发）；Janssen Pharmaceutica N.V（杨森制药有限公司）
