Drugsniffer: An Open Source Workflow for Virtually Screening Billions of Molecules for Binding Affinity to Protein Targets.
Drugsniffer: An Open Source Workflow for Virtually Screening Billions of Molecules for Binding Affinity to Protein Targets.
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DOI：
10.3389/fphar.2022.874746
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发表时间：
2022
影响因子：
5.6
通讯作者：
Roy, Amitava
中科院分区：
文献类型：
--
作者：
Venkatraman, Vishwesh;Colligan, Thomas H.;Lesica, George T.;Olson, Daniel R.;Gaiser, Jeremiah;Copeland, Conner J.;Wheeler, Travis J.;Roy, Amitava
关键词：
SARS-CoV2大流行突出了鉴定治疗药物的高效和有效方法的重要性，特别是表明需要能够探索可合成小分子的全部多样性的方法。传统的高通量筛选方法可能考虑多达数百万个分子，而虚拟筛选方法有望对数十亿个候选分子进行评估，从而扩大搜索空间，同时降低成本并加速发现。在这里，我们描述了一种新的筛选管道，称为药物嗅探器，它能够从数十亿个分子库中快速探索候选药物，并且旨在支持集群和云资源上的分布式计算。作为性能的一个例子，我们的管道需要4万小时的总计算时间来筛选针对3种SARS-CoV2蛋白的潜在药物，这些药物来自37亿个候选分子库。
The SARS-CoV2 pandemic has highlighted the importance of efficient and effective methods for identification of therapeutic drugs, and in particular has laid bare the need for methods that allow exploration of the full diversity of synthesizable small molecules. While classical high-throughput screening methods may consider up to millions of molecules, virtual screening methods hold the promise of enabling appraisal of billions of candidate molecules, thus expanding the search space while concurrently reducing costs and speeding discovery. Here, we describe a new screening pipeline, called drugsniffer, that is capable of rapidly exploring drug candidates from a library of billions of molecules, and is designed to support distributed computation on cluster and cloud resources. As an example of performance, our pipeline required ∼40,000 total compute hours to screen for potential drugs targeting three SARS-CoV2 proteins among a library of ∼3.7 billion candidate molecules.
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影响因子：
5.6
作者：
Gorgulla C;Çınaroğlu SS;Fischer PD;Fackeldey K;Wagner G;Arthanari H
通讯作者：
Arthanari H
影响因子：
14.9
作者：
Douguet D
通讯作者：
Douguet D
DOI：
10.3390/molecules23020353
发表时间：
2018-02-07
期刊：
Molecules (Basel, Switzerland)
影响因子：
--
作者：
Álvarez-Carretero S;Pavlopoulou N;Adams J;Gilsenan J;Tabernero L
通讯作者：
Tabernero L
影响因子：
8.4
作者：
Gentile F;Fernandez M;Ban F;Ton AT;Mslati H;Perez CF;Leblanc E;Yaacoub JC;Gleave J;Stern A;Wong B;Jean F;Strynadka N;Cherkasov A
通讯作者：
Cherkasov A
影响因子：
5.6
作者：
Acharya A;Agarwal R;Baker MB;Baudry J;Bhowmik D;Boehm S;Byler KG;Chen SY;Coates L;Cooper CJ;Demerdash O;Daidone I;Eblen JD;Ellingson S;Forli S;Glaser J;Gumbart JC;Gunnels J;Hernandez O;Irle S;Kneller DW;Kovalevsky A;Larkin J;Lawrence TJ;LeGrand S;Liu SH;Mitchell JC;Park G;Parks JM;Pavlova A;Petridis L;Poole D;Pouchard L;Ramanathan A;Rogers DM;Santos-Martins D;Scheinberg A;Sedova A;Shen Y;Smith JC;Smith MD;Soto C;Tsaris A;Thavappiragasam M;Tillack AF;Vermaas JV;Vuong VQ;Yin J;Yoo S;Zahran M;Zanetti-Polzi L
通讯作者：
Zanetti-Polzi L