Advances in elucidating the molecular mechanisms underlying AR conformational regulation are summarized and progress in the structure-based design and development of novel AR antagonists are highlighted, highlighting the power of computation-driven approaches in drug discovery.
Xin Chai, Tingjun Hou, Dan Li· Accounts of Chemical Researc...· 0 citations
Accurate prediction of metalloprotein-ligand interactions is critical for metalloprotein-targeted drug discovery. Conventional docking tools and existing deep learning (DL) models fail to reliably capture metal-ligand interactions, hampering the discovery of potent metalloprotein inhibitors. Here, we propose MetalloDoc...
Hui Zhang, Xujun Zhang, Qun Su et al.· Journal of the American Chem...· 6 citations
An explainable intelligence computational framework, Symbolic Trajectory-Embedded Dark Causal Interaction Inference (STE-DC2I), which combines symbolic trajectory embedding with historical prediction mechanisms to model nonmonotonic oscillatory dependencies between genes in CRC subtypes offers interpretable insights an...
Meng Huang, Huijin Hu, Ming Li et al.· Journal of Chemical Informat...· 0 citations
This work aims to discuss the features and the generative performance of different types of molecular generative models for the PROTAC design task and help researchers to better apply these models in practical cases.
Jieyu Jin, Tingjun Hou, Huanxiang Liu et al.· Journal of Chemical Informat...· 0 citations
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This Account describes a computational and AI-driven ecosystem for structure-based covalent drug discovery and dives into a suite of cutting-edge, AI-driven computational methods, exploring the potential of deep learning in tasks such as molecular docking, covalent binding site prediction, and lead optimization.
Shi Li, Hongyan Du, Xujun Zhang et al.· Accounts of Chemical Researc...· 4 citations
The DRHIN platform provides a code-free portal supporting three key predictive tasks: discovering drug-disease associations, repurposing existing drugs for new indications, and identifying potential therapies for specific diseases, making analyses accessible and reproducible.
Bowei Zhao, Dongxu Li, Yue Yang et al.· Journal of Chemical Informat...· 10 citations· ⚡1
LiTEN achieves state-of-the-art accuracy on standard benchmarks, consistently outperforming leading approaches in both precision and speed, and enables comprehensive modeling tasks, ranging from geometry optimization to free energy surface construction, with high computational efficiency for large biomolecules.
Qun Su, Kai Zhu, Qiaolin Gou et al.· Nature Communications· 2 citations
Token-Mol is presented, a token-only 3D drug design model that encodes both 2D and 3D structural information, along with molecular properties, into discrete tokens, which introduces a Gaussian cross-entropy loss function tailored for regression tasks, enabling superior performance across multiple downstream application...
Ji-Ke Wang, Rui Qin, Mingyang Wang et al.· Nature Communications· 30 citations· ⚡1
A deep learning framework that integrates knowledge graph embedding and pre-training strategies to overcome the intractable cold start issue, achieving superior performance and interpretability in drug repurposing is introduced.
Kun Li, Jiacai Yi, Qing Ye et al.· Communications Chemistry· 1 citation
A comprehensive overview of how enhanced sampling methods are reshaping the field, with a particular focus on the data-driven construction of collective variables, is provided.
Kai Zhu, Enrico Trizio, Jintu Zhang et al.· Chemical Reviews· 58 citations
HiCLR is the first foundation model that can be broadly applied to various synthesis-related tasks, and it achieves state-of-the-art performance in reaction classification, reaction condition recommendation, reaction yield prediction, synthesis planning, and even molecular property prediction.
Jialu Wu, Yiheng Zhu, Xiaorui Wang et al.· JACS Au· 0 citations
RSGPT, a generative model pre-trained on ten billion data points, achieving state-of-the-art performance for synthesis planning, and introduces reinforcement learning to capture the relationships among products, reactants, and templates more accurately.
Yafeng Deng, Xinda Zhao, Hanyu Sun et al.· Nature Communications· 18 citations· ⚡2
Exploring how generative AI could make machine vision more accessible to businesses. The post GenEye in a Box: Making Machine Vision Something You Can Just Ask For appeared first on GPT-Lab.
