COVID 19 SARS COV2 targets and small molecule data including insilico analysis

Welcome to the repository for the COVID-19 research data. Corresponding Author: Girinath G. Pillai and few experts Co-authors: Team of experts, scholars and students To join dedicated Slack Discussion : https://join.slack.com/t/nyroindia/shared_invite/zt-ejes216c-QZzEK_G5tNKIjewbVj2IPA We commit to conduct research analysis and all the findings and data will be open and anyone can use or help us improve the data. The parameters for checkpoints are: A) Pharmacophore Modelling - i) generate pharmacophore reference maps from XRay crystal geometry, ii) Generate all possible conformers of the dataset molecules for screening. B) Virtual Screening - i) highest docking score within the dataset, ii) lowest clashes (interligand or intraligand), iii) interactions with key amino acid residues based on literature reports, PROSITE server and pocket finding algorithm like DoGSite or CASTp iv) optimal LE values and v) satisfactory interactions between small molecules and amino acids. C) Selection - i) binding affinity range predictions, lowest among the dataset, ii) free binding energy calculation considering desolvation terms, lowest among the dataset and iii) torsion analysis - coverage of bonds in CSD database. D) Optimization - i) pharmacokinetic properties to be generated from selected hits and an optimal balance of properties to be considered for candidate selection criteria. E) For novel lead molecules - i) chemical space exploration on building blocks could be carried out, ii) on-demand synthesis and procurement. If you prefer you could always cite https://github.com/giribio/COVID19 Feel free to create any issues in Github or feel free to contact me via Slack for any queries. Thanks and let us fight against COVID-19 in all possible ways.欢迎访问本COVID-19研究数据集仓库。通讯作者：Girinath G. Pillai及数位专家；联合作者：由专家、学者及学生组成的团队。如需加入专属Slack讨论群组，请访问：https://join.slack.com/t/nyroindia/shared_invite/zt-ejes216c-QZzEK_G5tNKIjewbVj2IPA。本项目致力于开展相关研究分析，所有研究结果与数据均开放共享，任何人均可使用或协助我们完善数据集。 本数据集的检查点参数如下： A) 药效团建模（Pharmacophore Modelling）： i) 基于X射线晶体几何结构生成药效团参考图谱； ii) 为数据集内的分子生成所有可用于筛选的构象。 B) 虚拟筛选（Virtual Screening）： i) 数据集内最高对接得分； ii) 最低冲突（配体间或配体内冲突）； iii) 基于文献报道、PROSITE数据库服务器以及DoGSite、CASTp等口袋识别算法得到的与关键氨基酸残基的相互作用； iv) 最优配体效率（LE）值； v) 小分子与氨基酸残基之间的合格相互作用。 C) 遴选环节： i) 结合亲和力范围预测，选取数据集内的最低值； ii) 考虑去溶剂化效应的结合自由能计算，选取数据集内的最低值； iii) 扭转角分析：覆盖剑桥晶体结构数据库（Cambridge Structural Database，CSD）中的键型。 D) 优化环节： i) 从筛选得到的命中化合物中生成药代动力学性质，并以各项性质的最优平衡作为候选化合物的遴选标准。 E) 新型先导化合物相关： i) 可开展基于构建模块的化学空间探索； ii) 按需合成与采购。 您可引用本项目：https://github.com/giribio/COVID19。欢迎在Github提交任何问题，或通过Slack联系我咨询相关事宜。感谢您的支持，让我们携手以各种方式抗击COVID-19。