ChemVLM: Exploring the Power of Multimodal Large Language Models in Chemistry Area

no code implementations • 14 Aug 2024 • Junxian Li, Di Zhang, Xunzhi Wang, Zeying Hao, Jingdi Lei, Qian Tan, Cai Zhou, Wei Liu, Yaotian Yang, Xinrui Xiong, Weiyun Wang, Zhe Chen, Wenhai Wang, Wei Li, Shufei Zhang, Mao Su, Wanli Ouyang, Yuqiang Li, Dongzhan Zhou

We benchmark ChemVLM against a range of open-source and proprietary multimodal large language models on various tasks.

Language Modelling Large Language Model +4

Physical formula enhanced multi-task learning for pharmacokinetics prediction

no code implementations • 16 Apr 2024 • Ruifeng Li, Dongzhan Zhou, Ancheng Shen, Ao Zhang, Mao Su, Mingqian Li, Hongyang Chen, Gang Chen, Yin Zhang, Shufei Zhang, Yuqiang Li, Wanli Ouyang

Overall, our work illustrates the benefits and potential of using PEMAL in AIDD and other scenarios with data scarcity and noise.

Drug Discovery Multi-Task Learning

ChemLLM: A Chemical Large Language Model

1 code implementation • 10 Feb 2024 • Di Zhang, Wei Liu, Qian Tan, Jingdan Chen, Hang Yan, Yuliang Yan, Jiatong Li, Weiran Huang, Xiangyu Yue, Wanli Ouyang, Dongzhan Zhou, Shufei Zhang, Mao Su, Han-sen Zhong, Yuqiang Li

However, the community lacks an LLM specifically designed for chemistry.

Language Modelling Large Language Model +2

Transferable E(3) equivariant parameterization for Hamiltonian of molecules and solids

no code implementations • 28 Oct 2022 • Yang Zhong, Hongyu Yu, Mao Su, Xingao Gong, Hongjun Xiang

Using the message-passing mechanism in machine learning (ML) instead of self-consistent iterations to directly build the mapping from structures to electronic Hamiltonian matrices will greatly improve the efficiency of density functional theory (DFT) calculations.