no code implementations • 21 Oct 2023 • Lihang Liu, Donglong He, Xianbin Ye, Jingbo Zhou, Shanzhuo Zhang, Xiaonan Zhang, Jun Li, Hua Chai, Fan Wang, Jingzhou He, Liang Zheng, Yonghui Li, Xiaomin Fang
In this work, we show that by pre-training a geometry-aware SE(3)-Equivariant neural network on a large-scale docking conformation generated by traditional physics-based docking tools and then fine-tuning with a limited set of experimentally validated receptor-ligand complexes, we can achieve outstanding performance.
no code implementations • 12 Sep 2022 • Zhiyuan Yan, Peng Sun, Yubo Lang, Shuo Du, Shanzhuo Zhang, Wei Wang, Lei Liu
We evaluate the effectiveness of our method through extensive experiments on widely-used benchmarks and demonstrate that our method outperforms the state-of-the-art detectors in terms of generalization ability and robustness against unknown disturbances.
1 code implementation • 11 Aug 2022 • Lihang Liu, Donglong He, Xiaomin Fang, Shanzhuo Zhang, Fan Wang, Jingzhou He, Hua Wu
Full-range many-body interactions between electrons have been proven effective in obtaining an accurate solution of the Schr"odinger equation by classical computational chemistry methods, although modeling such interactions consumes an expensive computational cost.
no code implementations • 17 May 2022 • Shanzhuo Zhang, Zhiyuan Yan, Yueyang Huang, Lihang Liu, Donglong He, Wei Wang, Xiaomin Fang, Xiaonan Zhang, Fan Wang, Hua Wu, Haifeng Wang
Additionally, the pre-trained model provided by H-ADMET can be fine-tuned to generate new and customised ADMET endpoints, meeting various demands of drug research and development requirements.
1 code implementation • 28 Jun 2021 • Shanzhuo Zhang, Lihang Liu, Sheng Gao, Donglong He, Xiaomin Fang, Weibin Li, Zhengjie Huang, Weiyue Su, Wenjin Wang
In this report, we (SuperHelix team) present our solution to KDD Cup 2021-PCQM4M-LSC, a large-scale quantum chemistry dataset on predicting HOMO-LUMO gap of molecules.
no code implementations • 11 Jun 2021 • Xiaomin Fang, Lihang Liu, Jieqiong Lei, Donglong He, Shanzhuo Zhang, Jingbo Zhou, Fan Wang, Hua Wu, Haifeng Wang
Recent advances in graph neural networks (GNNs) have shown great promise in applying GNNs for molecular representation learning.
Ranked #2 on Molecular Property Prediction on ToxCast
1 code implementation • NA 2021 • Weibin Li, Shanzhuo Zhang, Lihang Liu, Zhengjie Huang, Jieqiong Lei, Xiaomin Fang, Shikun Feng, Fan Wang
As graph neural networks have achieved great success in many domains, some studies apply graph neural networks to molecular property prediction and regard each molecule as a graph.
Ranked #6 on Graph Property Prediction on ogbg-molhiv
no code implementations • IEEE Access 2019 • Chengqin Ye, Wei Wang, Shanzhuo Zhang, Kuanquan Wang
Obtaining precise whole-heart segmentation from computed tomography (CT) or other imaging techniques is prerequisite to clinically analyze the cardiac status, which plays an important role in the treatment of cardiovascular diseases.