Ekambaram Srinivasan, Wang Jian, Dokholyan Nikolay V
Penn State College of Medicine.
Res Sq. 2024 Aug 9:rs.3.rs-4744915. doi: 10.21203/rs.3.rs-4744915/v1.
with a rich history of traditional medicinal use, has garnered significant attention in contemporary research for its potential therapeutic applications in various human diseases, including pain, inflammation, cancer, and osteoarthritis. However, the specific molecular targets and mechanisms underlying the synergistic effects of its diverse phytochemical constituents remain elusive. Understanding these mechanisms is crucial for developing targeted, effective cannabis-based therapies.
To investigate the molecular targets and pathways involved in the synergistic effects of cannabis compounds, we utilized DRIFT, a deep learning model that leverages attention-based neural networks to predict compound-target interactions. We considered both whole plant extracts and specific plant-based formulations. Predicted targets were then mapped to the Reactome pathway database to identify the biological processes affected. To facilitate the prediction of molecular targets and associated pathways for any user-specified cannabis formulation, we developed CANDI (Cannabis-derived compound Analysis and Network Discovery Interface), a web-based server. This platform offers a user-friendly interface for researchers and drug developers to explore the therapeutic potential of cannabis compounds.
Our analysis using DRIFT and CANDI successfully identified numerous molecular targets of cannabis compounds, many of which are involved in pathways relevant to pain, inflammation, cancer, and other diseases. The CANDI server enables researchers to predict the molecular targets and affected pathways for any specific cannabis formulation, providing valuable insights for developing targeted therapies.
By combining computational approaches with knowledge of traditional cannabis use, we have developed the CANDI server, a tool that allows us to harness the therapeutic potential of cannabis compounds for the effective treatment of various disorders. By bridging traditional pharmaceutical development with cannabis-based medicine, we propose a novel approach for botanical-based treatment modalities.
因其悠久的传统药用历史，在当代研究中因其在包括疼痛、炎症、癌症和骨关节炎等多种人类疾病中的潜在治疗应用而备受关注。然而，其多种植物化学成分协同作用的具体分子靶点和机制仍不清楚。了解这些机制对于开发有针对性的、有效的基于大麻的疗法至关重要。
为了研究大麻化合物协同作用所涉及的分子靶点和途径，我们利用了DRIFT，这是一种深度学习模型，利用基于注意力的神经网络来预测化合物-靶点相互作用。我们考虑了全植物提取物和特定的植物性制剂。然后将预测的靶点映射到Reactome通路数据库，以确定受影响的生物学过程。为了便于预测任何用户指定的大麻制剂的分子靶点和相关途径，我们开发了CANDI（大麻衍生化合物分析和网络发现界面），一个基于网络的服务器。该平台为研究人员和药物开发者提供了一个用户友好的界面，以探索大麻化合物的治疗潜力。
我们使用DRIFT和CANDI进行的分析成功地确定了大麻化合物的众多分子靶点，其中许多靶点参与了与疼痛、炎症、癌症和其他疾病相关的途径。CANDI服务器使研究人员能够预测任何特定大麻制剂的分子靶点和受影响的途径，为开发有针对性的疗法提供了有价值的见解。
通过将计算方法与传统大麻使用知识相结合，我们开发了CANDI服务器，这是一种工具，使我们能够利用大麻化合物的治疗潜力来有效治疗各种疾病。通过将传统药物开发与基于大麻的药物相结合，我们提出了一种基于植物的治疗方式的新方法。
