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/.
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.
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