Oct 2025· Nature Computational Science· Vol 5, pp. 1017 - 1028· 4 citations· 58 references
Computer ScienceMedicine
TL;DR
This study presents ECloudGen, which uses latent diffusion to generate electron clouds from protein pockets and decodes them into molecules, and adopts two-stage training, which expands the chemical space accessible to generative drug design.
Mol-CADiff is introduced, a diffusion-based framework that uses causal attention mechanisms for text-conditional molecular generation and enhances dependency modeling both within and across modalities, enabling precise control over the generation process.
This tutorial provides a unified introduction to modern generative modeling approaches for molecular generation, covering their theoretical foundations, algorithmic design, and practical considerations for molecular representations such as 1D SMILES strings, 2D molecular graphs, and 3D structures.
Ke-Han Guo, Yili Shen, Jeehyun Hwang et al.· Proceedings of the 32nd ACM...· 0 citations
A clear pattern is revealed in LLM spatial capabilities: while they still lag behind state-of-the-art approaches, they are promising and can handle multiple spatial constraints simultaneously, enabling scaling to heterogeneous setups.
Thomas MacDougall, Maksim Kuznetsov, Roman Schutski et al.· arXiv.org· 1 citation
MARD-Mol is proposed, a hybrid AR-diffusion framework based on motif-inspired units that reformulate property optimization into an iterative “diagnose-and-repair” process, enabling targeted optimization of defective motifs while preserving the global scaffold.
Sizhe Zhang, G. Luo, Wei Fan et al.· Bioinform.· 0 citations
A high-accuracy surrogate predictor based on message-passing neural networks is decoupled from heterogeneous generators, and integrated into a closed-loop strategy of generate, score, select, and regenerate to progressively enrich high-performing candidates without modifying the underlying model architectures.
Zhao-Sheng Zhang, Yan-Bo Liu, Jia-Dong Liu et al.· Physical Chemistry, Chemical...· 0 citations
OmniScore is introduced, a universal structure-based framework that learns a shared geometry-aware representation of complexes once and then adapts it to downstream scoring through lightweight task-specific heads, suggesting that geometry-aware pretraining can provide a reusable scoring backbone for tasks that depend o...
Jennifer Neville did not want to go into computer science—but that’s exactly where she landed. Neville discusses the starts and stops that led to her professional sweet spot and her work identifying “surprising failures” making it hard for AI to handle complexity. The post What AI gets wrong and what failure teaches us appeared first on Microsoft Research.
Computer scientist, entrepreneur, and philanthropist will collaborate with the MIT Schwarzman College of Computing to advance AI and scientific discovery.