arXiv:2606. 30961v1 Announce Type: cross Abstract: Advances in deep learning architectures and representations have enabled ML-driven chemical property prediction, but state-of-the-art (SOTA) models have remained largely confined to independent codebases and lack support for diverse chemical species.
By Jacob W. Toney, Samir Darouich, Yiran Wang, Aaron G. Garrison, Johannes K\"astner, Heather J. Kulik
The paper introduces Align-React, a chemical reaction representation learning framework that incorporates atomic correspondence between reactants and products, an adapter for embedding reaction conditions, and a Reaction-Center-Aware attention mechanism. These components enable the model to capture precise molecular transformations and focus on critical functional groups, leading to improved performance across a variety of organic reaction tasks. The framework outperforms existing architectures on most benchmark datasets.
By Kaipeng Zeng, Xianbin Liu, Yu Zhang, Xiaokang Yang, Yaohui Jin, Yanyan Xu
SimpleDesign is a single-stage, end-to-end model for joint protein sequence and structure design that eliminates the need for multi-stage training. It combines discrete cross-entropy for sequences with a regression objective for structures, using a Mixture-of-Transformer architecture to handle modality-specific processing while maintaining global self-attention. Trained on over 2 million sequence-structure pairs, SimpleDesign achieves strong performance on co-design and unconditional generation benchmarks.
By Jiarui Lu, Yuyang Wang, Yizhe Zhang, Jiatao Gu, Navdeep Jaitly, Joshua M. Susskind, Miguel \'Angel Bautista
arXiv:2604. 11827v2 Announce Type: replace-cross Abstract: Machine learning is revolutionizing chemistry.
By Hannes Kneiding, Luc\'ia Mor\'an-Gonz\'alez, Nishamol Kuriakose, Ainara Nova, David Balcells
arXiv:2606. 11651v1 Announce Type: new Abstract: Synthetic random heteropolymers (RHPs), consisting of a predefined set of monomers, offer an approach toward the design of protein-like materials.
By Shuni Li, Zhiyuan Ruan, Andy Shen, Ivan Jayapurna, Ting Xu, Haiyan Huang
arXiv:2511. 19264v2 Announce Type: replace-cross Abstract: Generative Flow Networks (GFlowNets) construct molecules through sequential decisions, but their internal policies remain opaque, limiting adoption in drug discovery, where chemists need interpretable rationales for proposed structures.
By Amirtha Varshini A S, Duminda S. Ranasinghe, Hok Hei Tam