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      深度學習在藥物設計與發現中的應用
      來源: | 作者:黃牛、李偉 | 發布時間: 2019-03-20 | 3698 次瀏覽 | 分享到:
      閱讀原文,點擊 此處 

      藥學學報, 2019, 54(5): 761-767
      引用本文:
      李偉, 楊金才, 黃牛. 深度學習在藥物設計與發現中的應用[J]. 藥學學報, 2019, 54(5): 761-767.
      LI Wei, YANG Jin-cai, HUANG Niu. Deep learning in drug design and discovery[J]. Acta Pharmaceutica Sinica, 2019, 54(5): 761-767.


      深度學習在藥物設計與發現中的應用
      李偉2, 楊金才1, 黃牛1,3
      1. 北京生命科學研究所, 北京 102206;
      2. 瑞璞鑫(蘇州)生物科技有限公司, 江蘇 蘇州 215123;
      3. 清華大學生物醫學交叉研究院, 北京 102206
      摘要: 
      在新藥創制的藥物設計與發現所采用的多種技術中,深度學習仍處于初級階段,但近年來以其獨有的特點,開始應用于虛擬化合物庫的生成,化合物活性、代謝和毒性的預測,以及有機合成反應預測等多個方面。與傳統的機器學習方法相比,深度學習的預測能力無明顯優勢,但其無需人工歸納總結數據特征,而是具有學習能力,自動提取特征。與基于第一性原理的計算化學相比,深度學習雖然因為對標注明晰的大數據集的依賴,存在泛化能力的不足,但其以原子為中心進行卷積的表征開始助力計算化學。深度學習作為新興技術發展迅速,不依賴于大量標注數據的非監督學習等方法在逐漸完善,有望能更好地助力新藥研發。
      關鍵詞:    新藥研發      深度學習      機器學習      計算化學      全新藥物設計      


      Deep learning in drug design and discovery

      LI Wei2, YANG Jin-cai1, HUANG Niu1,3

      1. National Institute of Biological Sciences, Beijing 102206, China;
      2. RPXDs(Suzhou) Biotechnology Co., Ltd., Suzhou 215123, China;
      3. Tsinghua Institute of Multidisciplinary Biomedical Research, Tsinghua University, Beijing 102206, China
      Abstract: 
      Among various technologies used in drug design and discovery, deep learning is still in its infancy. Recently, deep learning approaches have been rapidly developed and applied to address various problems in drug discovery, including generation of virtual compound library, prediction of compound activity, metabolism and toxicity, and prediction of organic synthesis routes. Compared with the traditional machine learning methods, the prediction power of deep learning did not show significant improvement. However, proactively learning and automatically feature extraction bring advantages for deep learning approaches. Compared to first principle-based computational chemistry methods, deep learning can not be generalized because it depends on large-scale and highquality annotated data sets. But its molecular representation with single-atom atomic environment vectors could be useful for computational chemists. As an emerging technology, deep learning, especially the unsupervised learning method that does not rely on large datasets with labels, is gradually improving. It is expected that someday deep learning method will become practical for drug discovery.
      Key words:    drug discovery    deep learning    machine learning    computational chemistry    de-novo design    

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