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
arXiv:2607. 20551v1 Announce Type: cross Abstract: Effective molecular representation learning is crucial for accurate molecular property prediction.
By Tianming Han, Li Zhang, Qi Zhao
arXiv:2602. 22822v3 Announce Type: replace Abstract: Tandem mass spectrometry (MS/MS) is central to small molecule identification, but current deep learning systems for spectrum prediction still remain difficult to evaluate and deploy in practice.
By Yunhua Zhong, Yixuan Tang, Yifan Li, Pan Liu, Zhiwen Yang, Jie Yang, Jun Xia
arXiv:2606. 03232v1 Announce Type: cross Abstract: Graph Neural Networks (GNNs) have revolutionized Neural Force Fields for atomistic simulations, achieving near-quantum accuracy at reduced cost, yet adapting these models to new chemical systems requires expensive retraining of foundation models.
By Parth Verma, Parv P. Singh, Vipul Garg, Ishita Thakre, N. M. Anoop Krishnan, Sayan Ranu
arXiv:2605. 01625v3 Announce Type: replace Abstract: Proteins are inherently multiscale physical systems whose functional properties emerge from coordinated structural organization across multiple spatial resolutions, ranging from atomic interactions to global fold topology.
By Viet Thanh Duy Nguyen, John K. Johnstone, Truong-Son Hy
The paper introduces GRACE, a 3D collision cross section (CCS) predictor that incorporates geometric residual adduct conditioning via early fusion. GRACE adapts a pretrained molecular geometry encoder with an adduct token and low‑rank attention adapters, achieving the lowest mean percentage differences on random, scaffold, and adduct‑sensitive splits of a curated dataset of over 9,000 experimental CCS records. Diagnostic analyses attribute its performance to residual learning that removes the dominant mass‑CCS trend and to early fusion that enhances adduct‑sensitive prediction.
By Parthasarathy Suryanarayanan, Susanta Das, Shreyans Sethi, Kenneth M. Merz, Jr., Joseph A. Morrone