arXiv:2607. 00464v1 Announce Type: new Abstract: Current molecular generation benchmarks emphasize task complexity, molecule novelty, and property alignment; they largely overlook a critical concern: the potential safety risks of AI-generated molecules.
By Tong Xu, Xinzhe Cao, Zhihui Zhu, Keyan Ding, Huajun Chen
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:2607. 11978v1 Announce Type: cross Abstract: Precision molecular design aims to discover personalized drug candidates through joint control of multiple conditions, such as biological relevance and molecular design strategies.
By Hang Yuan, Chen Li, Wenjun Ma, Tadahiko Murata, Yuncheng Jiang
arXiv:2504. 12075v4 Announce Type: replace Abstract: In the present work, a generative deep learning framework combining a Co-optimized Variational Autoencoder (Co-VAE) with quantitative structure-property relationship (QSPR) techniques is developed to enable inverse molecular design of fuels.
By Kiran K. Yalamanchi, Pinaki Pal, Balaji Mohan, Abdullah S. AlRamadan, Jihad A. Badra, Yuanjiang Pei
arXiv:2606. 11256v1 Announce Type: cross Abstract: Designing molecules with target properties is most useful when candidate structures are accompanied by feasible synthetic routes.
By C\'esar Ojeda, Darius A. Faroughy, Maryam Karimi, Payam Zarrintaj, Mir Mehdi Seyedebrahimi, Mart\'in Carballo-Pacheco
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