ABSTRACT As the world struggles against current global pandemic of novel coronavirus disease (COVID-19), it is challenging to trigger drug discovery efforts to search broad-spectrum antiviral agents. Thus, there is a need of strong and sustainable global collaborative works especially in terms of new and existing data analysis and sharing which will join the dots of knowledge gap. Our present chemical-informatics based data analysis approach is an attempt of application of previous activity data of SARS-CoV main protease (Mpro) inhibitors to accelerate the search of present SARS-CoV-2 Mpro inhibitors. The study design was composed of three major aspects: (1) classification QSAR based data mining of diverse SARS-CoV Mpro inhibitors, (2) identification of favourable and/or unfavourable molecular features/fingerprints/substructures regulating the Mpro inhibitory properties, (3) data mining based prediction to validate recently reported virtual hits from natural origin against SARS-CoV-2 Mpro enzyme. Our Structural and physico-chemical interpretation (SPCI) analysis suggested that heterocyclic nucleus like diazole, furan and pyridine have clear positive contribution while, thiophen, thiazole and pyrimidine may exhibit negative contribution to the SARS-CoV Mpro inhibition. Several Monte Carlo optimization based QSAR models were developed and the best model was used for screening of some natural product hits from recent publications. The resulted active molecules were analysed further from the aspects of fragment analysis. This approach set a stage for fragment exploration and QSAR based screening of active molecules against putative SARS-CoV-2 Mpro enzyme. We believe the future in vitro and in vivo studies would provide more perspectives for anti-SARS-CoV-2 agents.

中文翻译：

---

COVID-19 药物发现的化学信息学方法：探索重要片段和基于数据挖掘的预测，预测天然来源中作为主要蛋白酶 (Mpro) 抑制剂的一些命中

摘要：随着世界与当前新型冠状病毒病（COVID-19）在全球大流行作斗争，启动药物发现工作以寻找广谱抗病毒药物具有挑战性。因此，需要强有力和可持续的全球合作，特别是在新的和现有的数据分析和共享方面，这将弥补知识差距。我们目前基于化学信息学的数据分析方法是应用 SARS-CoV 主要蛋白酶 (Mpro) 抑制剂先前活性数据来加速现有 SARS-CoV-2 Mpro 抑制剂的搜索的尝试。研究设计由三个主要方面组成：(1) 基于分类 QSAR 的多种 SARS-CoV Mpro 抑制剂的数据挖掘，(2) 识别有利和/或不利的分子特征/指纹/调节 Mpro 抑制特性的子结构，(3 ）基于数据挖掘的预测，以验证最近报告的针对 SARS-CoV-2 Mpro 酶的自然来源的虚拟命中。我们的结构和物理化学解释（SPCI）分析表明，二唑、呋喃和吡啶等杂环核对 SARS-CoV Mpro 抑制具有明显的积极贡献，而噻吩、噻唑和嘧啶可能表现出消极贡献。开发了几种基于蒙特卡罗优化的 QSAR 模型，并使用最佳模型从最近出版物中筛选一些天然产品。从片段分析方面对所得活性分子进行了进一步分析。这种方法为片段探索和基于 QSAR 的活性分子筛选奠定了基础，以对抗假定的 SARS-CoV-2 Mpro 酶。我们相信未来的体外和体内研究将为抗 SARS-CoV-2 药物提供更多视角。