分类： 药物科学 >> 药物设计 提交时间： 2024-05-13
Yulong Shi
Chongwu Li
Xinben Zhang
Cheng Peng
Peng Sun
Qian Zhang
Leilei Wu
Ying Ding
Dong Xie
Zhijian Xu
Weiliang Zhu
摘要： As key oncogenic drivers in non-small-cell lung cancer (NSCLC), various mutations in the epidermal growth factor receptor (EGFR) with variable drug sensitivities have been a major obstacle for precision medicine. To achieve clinical-level drug recommendations, a platform for clinical patient case retrieval and reliable drug sensitivity prediction is highly expected. Therefore, we built a database, D3EGFRdb, with the clinicopathologic characteristics and drug responses of 1,339 patients with EGFR mutations via literature mining. On the basis of D3EGFRdb, we developed a deep learning-based prediction model, D3EGFRAI, for drug sensitivity prediction of new EGFR mutation-driven NSCLC. Model validations of D3EGFRAI showed a prediction accuracy of 0.81 and 0.85 for patients from D3EGFRdb and our hospitals, respectively. Furthermore, mutation scanning of the crucial residues inside drug-binding pockets, which may occur in the future, was performed to explore their drug sensitivity changes. D3EGFR is the first platform to achieve clinical-level drug response prediction of all approved small molecule drugs for EGFR mutation-driven lung cancer and is freely accessible at https://www.d3pharma.com/D3EGFR/index.php.
分类： 药物科学 >> 药物设计 提交时间： 2024-04-10
摘要： Halogen bonds (XBs) are essential non-covalent interactions in molecular recognition and drug design. Current studies on XBs in drug design mainly focus on the interactions between halogenated ligands and target proteins, lacking a systematic study on naturally existing and artificially prepared halogenated residue XBs (hr_XBs) and their characteristics. Here, we conducted a computational study on the potential hr_XBs in proteins/peptides using database searching, quantum mechanics calculations, and molecular dynamics simulations. XBs at protein-peptide interaction interfaces are found to enhance their binding affinity. Additionally, the formation of intramolecular XBs (intra_XBs) within proteins may significantly contribute to the structural stability of structurally flexible proteins, while having a minor impact on proteins with inherently high structural rigidity. Impressively, introducing halogens without the formation of intra_XBs may lead to a decrease in protein structural stability. This study enriches our comprehension of the roles and effects of halogenated residue XBs in biological systems.
分类： 药物科学 >> 药物设计 提交时间： 2024-02-20
Jin Liu
Yike Gui
Jingxin Rao
Jingjing Sun
Gang Wang
Qun Ren
Ning Qu
Buying Niu
Zhiyi Chen
Xia Sheng
Yitian Wang
Mingyue Zheng
Xutong Li
摘要： Ensuring drug safety in the early stages of drug development is crucial to avoid costly failures in subsequent phases. However, the economic burden associated with detecting drug off-targets and potential side effects through in vitro safety screening and animal testing is substantial. Drug off-target interactions, along with the adverse drug reactions they induce, are significant factors affecting drug safety. To assess the liability of candidate drugs, we developed an artificial intelligence model for the precise prediction of compound off-target interactions, leveraging multi-task graph neural networks. The outcomes of off-target predictions can serve as representations for compounds, enabling the differentiation of drugs under various ATC codes and the classification of compound toxicity. Furthermore, the predicted off-target profiles are employed in ADR enrichment analysis, facilitating the inference of potential ADRs for a drug. Using the withdrawn drug Pergolide as an example, we elucidate the mechanisms underlying ADRs at the target level, contributing to the exploration of the potential clinical relevance of newly predicted off-target interactions. Overall, our work facilitates the early assessment of compound safety/toxicity based on off-target identification, deduces potential ADRs of drugs, and ultimately promotes the secure development of drugs.
分类： 药物科学 >> 药物设计 提交时间： 2024-02-06
Minfei Ma
Xinben Zhang
Liping Zhou
Zijian Han
Yulong Shi
Jintian Li
Leyun Wu
Zhijian Xu
Weiliang Zhu
摘要： Continuous exploration of the chemical space of molecules to find ligands with high affinity and specificity for specific targets is an important topic in drug discovery. A focus on cyclic compounds, particularly natural compounds with diverse scaffolds, provides important insights into novel molecular structures for drug design. However, the complexity of their ring structures has hindered the applicability of widely accepted methods and software for the systematic identification and classification of cyclic compounds. Herein, we successfully developed a new method, D3Rings, to identify acyclic, monocyclic, spiro ring, fused and bridged ring, and cage ring compounds as well as macrocyclic compounds. By using D3Rings, we completed the statistics of cyclic compounds in 3 different databases, e.g., ChEMBL, DrugBank, and COCONUT. The results demonstrated the richness of ring structures in natural products, especially spiro, macrocycles, fused and bridged rings. Based on this, three deep generative models, namely VAE, AAE, and CharRNN, were trained and used to construct two datasets similar to DrugBank and COCONUT but 10 times larger than them. The enlarged datasets were then used to explore the molecular chemical space, focusing on complex ring structures, for novel drug discovery and development. Docking experiments with the newly generated COCONUT-like dataset against three SARS-CoV-2 target proteins revealed that an expanded compound database improves molecular docking results. Cyclic structures were exhibited the best docking scores among the top-ranked docking molecules. These results suggest the importance of exploring the chemical space of structurally novel cyclic compounds and continuous expansion of the library of drug-like compounds to facilitate the discovery of potent ligands with high binding affinity to specific targets. D3Rings is now freely available at http://www.d3pharma.com/D3Rings/.
点击量
通过
点击量
下载量
评论
待评议
