13 citations · 23 across the 12 of their papers we have counts for
12 papers
Scaffold-Conditioned Preference Triplets for Controllable Molecular Optimization with Large Language Models
Yi Xiong, Liang Xiong, Xiaohong Ji +4
Molecular property optimization is central to drug discovery, yet many deep learning methods rely on black-box scoring and offer limited control over scaffold preservation, often p…
NMRPeak: a ready-to-use intelligent system for molecular structure elucidation enabled by synergistic cross-modal learning
Fanjie Xu, Jinyuan Hu, Jingxiang Zou +8
One-dimensional nuclear magnetic resonance (NMR) spectroscopy is essential for molecular structure elucidation in organic synthesis, drug discovery, natural product characterizatio…
SpecXMaster Technical Report
Yutang Ge, Yaning Cui, Hanzheng Li +15
Intelligent spectroscopy serves as a pivotal element in AI-driven closed-loop scientific discovery, functioning as the critical bridge between matter structure and artificial intel…
Bohrium + SciMaster: Building the Infrastructure and Ecosystem for Agentic Science at Scale
Linfeng Zhang, Siheng Chen, Yuzhu Cai +46
AI agents are emerging as a practical way to run multi-step scientific workflows that interleave reasoning with tool use and verification, pointing to a shift from isolated AI-assi…
NMR-Solver: Automated Structure Elucidation via Large-Scale Spectral Matching and Physics-Guided Fragment Optimization
Yongqi Jin, Jun-Jie Wang, Fanjie Xu +6
Nuclear Magnetic Resonance (NMR) spectroscopy is one of the most powerful and widely used tools for molecular structure elucidation in organic chemistry. However, the interpretatio…
Uni-Mol3: A Multi-Molecular Foundation Model for Advancing Organic Reaction Modeling
Lirong Wu, Junjie Wang, Zhifeng Gao +6
Organic reaction, the foundation of modern chemical industry, is crucial for new material development and drug discovery. However, deciphering reaction mechanisms and modeling mult…
SynBridge: Bridging Reaction States via Discrete Flow for Bidirectional Reaction Prediction
Haitao Lin, Junjie Wang, Zhifeng Gao +5
The essence of a chemical reaction lies in the redistribution and reorganization of electrons, which is often manifested through electron transfer or the migration of electron pair…
pUniFind: a unified large pre-trained deep learning model pushing the limit of mass spectra interpretation
Jiale Zhao, Pengzhi Mao, Kaifei Wang +12
Deep learning has advanced mass spectrometry data interpretation, yet most models remain feature extractors rather than unified scoring frameworks. We present pUniFind, the first l…
Beyond Atoms: Enhancing Molecular Pretrained Representations with 3D Space Modeling
Shuqi Lu, Xiaohong Ji, Bohang Zhang +5
Molecular pretrained representations (MPR) has emerged as a powerful approach for addressing the challenge of limited supervised data in applications such as drug discovery and mat…
Unified Cross-Scale 3D Generation and Understanding via Autoregressive Modeling
Shuqi Lu, Haowei Lin, Lin Yao +6
3D structure modeling is essential across scales, enabling applications from fluid simulation and 3D reconstruction to protein folding and molecular docking. Yet, despite shared 3D…
Uni-Mol2: Exploring Molecular Pretraining Model at Scale
Xiaohong Ji, Zhen Wang, Zhifeng Gao +4
In recent years, pretraining models have made significant advancements in the fields of natural language processing (NLP), computer vision (CV), and life sciences. The significant…
Uni-QSAR: an Auto-ML Tool for Molecular Property Prediction
Zhifeng Gao, Xiaohong Ji, Guojiang Zhao +4
Recently deep learning based quantitative structure-activity relationship (QSAR) models has shown surpassing performance than traditional methods for property prediction tasks in d…