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Article(id=1198656217829700487, tenantId=1146029695717560320, journalId=1189982191388893191, issueId=1198656209390764948, articleNumber=null, orderNo=null, doi=10.16438/j.0513-4870.2023-0702, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1685894400000, receivedDateStr=2023-06-05, revisedDate=1689955200000, revisedDateStr=2023-07-22, acceptedDate=null, acceptedDateStr=null, onlineDate=1763711512286, onlineDateStr=2025-11-21, pubDate=1697040000000, pubDateStr=2023-10-12, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1763711512286, onlineIssueDateStr=2025-11-21, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1763711512286, creator=13701087609, updateTime=1763711512286, updator=13701087609, issue=Issue{id=1198656209390764948, tenantId=1146029695717560320, journalId=1189982191388893191, year='2023', volume='58', issue='10', pageStart='2835', pageEnd='3150', issueExtLink='null', onlineDate='null', pubDate='1697040000000', pubDateStr='2023-10-12', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1763711510274, creator='13701087609', updateTime=1763711659007, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1198656833280897539, tenantId=1146029695717560320, journalId=1189982191388893191, issueId=1198656209390764948, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1198656833280897540, tenantId=1146029695717560320, journalId=1189982191388893191, issueId=1198656209390764948, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=2931, endPage=2941, ext={EN=ArticleExt(id=1198656218588869524, articleId=1198656217829700487, tenantId=1146029695717560320, journalId=1189982191388893191, language=EN, title=Perspective of CADD and AIDD in medicinal chemistry, columnId=null, journalTitle=Acta Pharmaceutica Sinica, columnName=null, runingTitle=null, highlight=null, articleAbstract=

Artificial intelligence-aided drug discovery (AIDD) is a new version of computer-aided drug discovery (CADD). AIDD is featured in significantly promoting the performance of conventional CADD. AI markedly enhances the learning ability of CADD. In the 1960s, CADD was established from conventional QSAR approaches, which mainly used regression approaches to derive substructure-activity relationship for compounds with a common scaffold, and guide drug molecular design, figure out the binding features of drugs, and identify potential drug targets. Since the 1990s, structural biology has provided three-dimensional structures of drug targets, enabling drug discovery based on target structure (SBDD), fragment-based drug discovery (FBDD), and structure-based virtual screening (SBVS) with CADD approaches. In the past 30 years, many first in class (FIC) and best in class (BIC) drugs were discovered with CADD. Now, AIDD will further revolutionize CADD by reducing human interventions and mining big chemical and biological data. It is expected that AIDD will significantly enhance the abilities of CADD, virtual screening and drug target identification. This article tries to provide perspectives of CADD and AIDD in medicinal chemistry with case studies.

, authors=null, authorsList=Zong-ru GUO, authorCompany=null, correspAuthors=Zong-ru GUO, authorNote=null, correspAuthorsNote=null, copyrightStatement=Copyright ©2023 Acta Pharmaceutica Sinica. All rights reserved., copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1198656230492304002, articleId=1198656217829700487, tenantId=1146029695717560320, journalId=1189982191388893191, language=CN, title=CADD和AIDD的药物化学刍议, columnId=1190335349206389552, journalTitle=药学学报, columnName=专家论坛, runingTitle=null, highlight=null, articleAbstract=

人工智能辅助药物发现(AIDD) 是计算机辅助药物发现(CADD) 的新版本, 现代AI的学习能力极大地提升了CADD的能力。20世纪中叶, QSAR开拓了计算机辅助药物研究。在药物靶标结构信息未知的情况下, 用回归的方法揭示具有相似骨架结构的化合物中子结构与活性之间的定量关系, 以指导药物分子的设计。经过几十年的发展, CADD技术在基于靶标结构的药物发现(SBDD)、基于片段的药物发现(FBDD)、基于靶标结构的虚拟筛选(SBVS) 等方面获得了广泛的应用。而AIDD的重大变革, 将显著提升计算机辅助药物设计、虚拟筛选和药物靶标发现的能力。本文从药物化学实践的角度, 以实例解读CADD和AIDD的关系。

, authors=null, authorsList=郭宗儒, authorCompany=null, correspAuthors=郭宗儒, authorNote=null, correspAuthorsNote=
*郭宗儒, E-mail:
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Drug Name Target From lead (hit) to NME Indication Year Ref.
5 Dorzolamide Carbonic
anhydrase		 Glaucoma 1995 [7, 8]
6 Saquinavir HIV protease AIDS 1997 [9]
7 Zanamivir Sialidase Influenza 1999 [10, 11]
8 Oseltamivir Sialidase Influenza 1999 [12-14]
9 Vemurafenib B-raf kinase Melanoma 2012 [15, 16]
10 Venetoclax Bcl-xL Chronic
lymphocytic
leukemia		 2016 [17-19]
11 Lorlatinib Anaplastic
lymphoma
kinase		 NSCLC 2018 [20, 21]
12 Alpelisib PI3K Advanced
breast
cancer		 2019 [22]
13 Belzutifan HIF-2α VHL
syndrome
CCRCC		 2021 [23, 24]
14 Sotorasib KRAS NSCLC 2021 [25-27]
15 Adagrasib KRAS NSCLC 2022 [28, 29]
), ArticleFig(id=1198960264889594725, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1198656217829700487, language=CN, label=Table 1, caption=

Some typical new molecular entities based on CADD

, figureFileSmall=null, figureFileBig=null, tableContent=
Drug Name Target From lead (hit) to NME Indication Year Ref.
5 Dorzolamide Carbonic
anhydrase		 Glaucoma 1995 [7, 8]
6 Saquinavir HIV protease AIDS 1997 [9]
7 Zanamivir Sialidase Influenza 1999 [10, 11]
8 Oseltamivir Sialidase Influenza 1999 [12-14]
9 Vemurafenib B-raf kinase Melanoma 2012 [15, 16]
10 Venetoclax Bcl-xL Chronic
lymphocytic
leukemia		 2016 [17-19]
11 Lorlatinib Anaplastic
lymphoma
kinase		 NSCLC 2018 [20, 21]
12 Alpelisib PI3K Advanced
breast
cancer		 2019 [22]
13 Belzutifan HIF-2α VHL
syndrome
CCRCC		 2021 [23, 24]
14 Sotorasib KRAS NSCLC 2021 [25-27]
15 Adagrasib KRAS NSCLC 2022 [28, 29]
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CADD和AIDD的药物化学刍议
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药学学报 | 专家论坛 2023,58(10): 2931-2941
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CADD和AIDD的药物化学刍议
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郭宗儒*
作者信息
  • 中国医学科学院、北京协和医学院药物研究所, 北京 100050
通讯作者:
*郭宗儒, E-mail:
Perspective of CADD and AIDD in medicinal chemistry
Zong-ru GUO*
Affiliations
  • Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, China
出版时间: 2023-10-12 doi: 10.16438/j.0513-4870.2023-0702
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摘要
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人工智能辅助药物发现(AIDD) 是计算机辅助药物发现(CADD) 的新版本, 现代AI的学习能力极大地提升了CADD的能力。20世纪中叶, QSAR开拓了计算机辅助药物研究。在药物靶标结构信息未知的情况下, 用回归的方法揭示具有相似骨架结构的化合物中子结构与活性之间的定量关系, 以指导药物分子的设计。经过几十年的发展, CADD技术在基于靶标结构的药物发现(SBDD)、基于片段的药物发现(FBDD)、基于靶标结构的虚拟筛选(SBVS) 等方面获得了广泛的应用。而AIDD的重大变革, 将显著提升计算机辅助药物设计、虚拟筛选和药物靶标发现的能力。本文从药物化学实践的角度, 以实例解读CADD和AIDD的关系。

计算机辅助药物发现  /  人工智能辅助药物发现  /  诺氟沙星  /  维奈托克  /  halicin

Artificial intelligence-aided drug discovery (AIDD) is a new version of computer-aided drug discovery (CADD). AIDD is featured in significantly promoting the performance of conventional CADD. AI markedly enhances the learning ability of CADD. In the 1960s, CADD was established from conventional QSAR approaches, which mainly used regression approaches to derive substructure-activity relationship for compounds with a common scaffold, and guide drug molecular design, figure out the binding features of drugs, and identify potential drug targets. Since the 1990s, structural biology has provided three-dimensional structures of drug targets, enabling drug discovery based on target structure (SBDD), fragment-based drug discovery (FBDD), and structure-based virtual screening (SBVS) with CADD approaches. In the past 30 years, many first in class (FIC) and best in class (BIC) drugs were discovered with CADD. Now, AIDD will further revolutionize CADD by reducing human interventions and mining big chemical and biological data. It is expected that AIDD will significantly enhance the abilities of CADD, virtual screening and drug target identification. This article tries to provide perspectives of CADD and AIDD in medicinal chemistry with case studies.

computer-aided drug discovery  /  artificial intelligence-aided drug discovery  /  norfloxacin  /  venetoclax  /  halicin
郭宗儒. CADD和AIDD的药物化学刍议. 药学学报, 2023 , 58 (10) : 2931 -2941 . DOI: 10.16438/j.0513-4870.2023-0702
Zong-ru GUO. Perspective of CADD and AIDD in medicinal chemistry[J]. Acta Pharmaceutica Sinica, 2023 , 58 (10) : 2931 -2941 . DOI: 10.16438/j.0513-4870.2023-0702
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计算机的应用和互联网普及深刻地影响着人类社会, 促进了计算机化学的发展, 也促进了新药的创制。从20世纪60年代计算机的应用, 人们经历了数据输入输出的穿孔纸带和穿孔卡片。个人计算机兴起, 从8位、16位、32位计算机, 发展到今天的64位多核个人计算机, 数据存储能力从最初的以千字节(KB) 到今天常见的万亿字节(TB)。日新月异的科学技术极大地改变了药物发现和分子设计的范式。如今, 人工智能辅助药物发现(AIDD) 为计算机辅助药物发现(CADD) 注入了新的活力。本文通过解读若干用CADD方法发现的药物(或进入临床研究的化合物), 从药物化学的角度分析CADD和AIDD的关系。
1 二维和三维定量构效关系
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20世纪60年代计算机用于药物研究, Hansch-藤田稔夫开创了药物(农药) 定量构效关系(QSAR) 的研究, 他们用原子或基团的热力学参数作为表征化合物结构描述符(descriptors), 以线性回归方法建立分子结构参数与活性(或其他性质) 的回归方程, 以表征结构与活性的定量关系[1]。虽然Hansch分析是性质(电性、疏水性和立体性) 与活性(药效或药代) 的相关性, 但这无疑是对既往(定性的) 构效关系的突破, 因为事件以数学形式表达是科学进步的标志。蓬勃发展的QSAR大都属于回顾性的描述和结构的优化, 完全依赖QSAR创制的新药很少(后面叙述)。与此同时, Free和Wilson提出的数学模型是对系列分子的周边基团与活性作回归处理, 可视作以结构作为描述符, 但缺乏物化/生物学意义[2]。QSAR的本质是提取分子的特征向量(即选出合适的描述符), 建立特征向量与生物活性的函数关系。
据笔者所知, 日本杏林公司研制的抗菌药, 拓扑异构酶抑制剂诺氟沙星(1, norfloxacin), 是完全基于Hansch-藤田模型的成功案例。在不断演化的QSAR方程指导下, 活性化合物与“未知靶标“的结合能力得到优化, 催生了诺氟沙星的诞生。
研制者古贺等[3]对喹诺酮酸骨架(2) 的不同位置作逐步变换, QSAR结果指导下一轮设计。例如, 首轮8个化合物的构效方程, 揭示了R1基团的最适STERIMOL (描述抑制或基团的三维尺寸) 轴向长度参数为4.2, 与乙基和环丙基相近。
$ \begin{aligned} \lg 1 / \mathrm{MIC}= & -0.492( \pm 0.18)\left[L_{(1)}\right]^2+ \\ & 4.102( \pm 1.59)\left[L_{(1)}\right]-1.999 \\ n=8, r= & 0.955, s=0.126, F_{2,5}=25.78, L_{(1) \mathrm{opt}}=4.2 \end{aligned} $
式中n是样本数, r是相关系数, s是标准偏差, F2, 5是显著性检验, L(1)opt是STERIMOL立体参数的1位取代基轴向最适距离。
经过对6、7和8位基团的3轮优化, 最终得到基于71个化合物数据的QSAR方程:
$ \begin{aligned} \log 1 / \mathrm{MIC} & =-0.362( \pm 0.25)\left[L_{(1)}\right]^2+ \\ & 3.036( \pm 2.21)\left[L_{(1)}\right]-2.499( \pm 0.55)\left[E_{s(6)}\right]^2- \\ & 3.345( \pm 0.73)\left[E_{s(6)}\right]+0.986( \pm 0.24) I_{(7)}- \\ & 0.734( \pm 0.27) I_{(7 \mathrm{~N}-\mathrm{CO})}-1.023( \pm 0.23)\left[B_{4(8)}\right]^2- \\ & 3.724( \pm 0.92)\left[B_{4(8)}\right]-0.205( \pm 0.05)\left[\Sigma \pi_{(6,7,8)}\right]^2- \\ & 0.485( \pm 0.10) \Sigma \pi_{(6,7,8)}- \\ & 0.681( \pm 0.39) \Sigma F_{(6,7,8)}-5.571 \\ n=71, r= & 0.964, s=0.274, F_{11,59}=70.22 \end{aligned} $
方程中的回归系数代表了不同位置基团的电性、疏水性或立体性对抗菌活性的影响权重, 研制出首创药物诺氟沙星, 1986年美国FDA批准上市。后来研制活性更强的沙星类药物也都依据了QSAR方程的设计原则。
Hansch-藤田模型和Free-Wilson模型基于分子的拓扑结构, 又称2D-QSAR。该模型未考虑分子的三维结构, 而且处理的样本为相同骨架的同系物分子, 骨架对活性的贡献隐含在回归方程的常数项中, 因而不能预测新的骨架, 只限于先导物的优化。后来出现的三维结构与活性的定量关系(3D-QSAR) 方法, 其中1988年Cramer III等[4]提出的“比较分子场方法” (CoMFA) 得到比较广泛的应用。
CoMFA方法假定, 具有相同骨架的系列化合物以相同的方式与靶标结合, 它们的活性是药物与受体相互作用的力场函数, 因而各化合物的生物活性取决于分子场的差异。分子场包括静电场、立体场和疏水场等, 分别反映药物分子和靶标之间的相互作用的特性。CoMFA分3个步骤: ①确定药物分子的活性构象, 将同系列分子按照共同骨架叠合; ②在叠合好的分子簇周围定义分子场的三维空间, 该空间按照一定的步长均匀划分成格点; 分别用表征分子场性质的探针(分别为带电荷的、体积的、疏水的原子、离子或基团) 在格点上移行, 计算探针与分子中诸原子的相互作用能; ③通过偏最小二乘(PLS) 方法建立化合物活性和分子场特征之间的关系, 一般用三维彩图表征这类化合物的电性、立体性和疏水性的三维构效关系[4]。由CoMFA又演化出比较分子相似性方法(CoMSIA)。CoMFA和CoMSIA在20世纪80/90年代受到学术界的重视, 主要原因是关联三维结构与活性的描述符是近于连续、而且是自生成的, 能够清晰地勾勒出高活性化合物的结构特征。然而应用CoMFA和CoMSIA方法主要是描述性的, 也不能产生新的骨架, 难以准确预测高活性分子, 因此, 尚未发现单纯由CoMFA设计的新药报道。
2D/3D-QSAR是在靶标结构未知情况下, 通过研究系列化合物的结构或物化性质与活性的定量依存关系, 揭示药物与靶标的作用特征, 借以研制高活性化合物, 所以可归纳为基于配体结构的药物设计。新世纪交替, 结构生物学的蓬勃发展, 解析了众多蛋白质和其配体复合物的三维结构。以计算机辅助基于靶标受体结构的药物发现(SBDD) 逐渐替换了2D/3D-QSAR模式。
日本卫材公司研制治疗轻度或中度阿尔茨海默病症状的胆碱酯酶(AChE) 抑制剂, 从普筛得到的苗头化合物(3) 出发, 经传统药物化学方法的结构变换与3D-QSAR (量子化学参数和分子形状比较) 的分析, 以及根据AChE与乙酰胆碱的复合物晶体结构, 将抑制剂作分子对接(docking), 研制出多奈哌齐(4, donepezil)。于1996年经FDA批准在美国上市[5]。多奈哌齐被认为是3D-QSAR辅助药物设计的另一个成功案例。
多奈哌齐是AChE可逆性抑制剂, 与底物乙酰胆碱的结合模式有相当大的区别。图 1a是多奈哌齐-AChE复合物晶体结构图, 图 1b是多奈哌齐与AChE的分子对接投影图, 二者对应非常相似。多奈哌齐与AChE由5个关键结合位点构成: ①茚酮的苯环与Trp279形成π-π堆积作用(KBI1); ②甲氧基与Arg289 (经结构水介导) 形成氢键结合(KBI2); ③茚酮的羰基氧原子经水分子介导与Phe288氢键结合(分子模拟的羰基与Tyr70或121有氢键结合) (KBI3); ④哌啶经质子化形成的铵离子没有像乙酰胆碱的季铵离子沉入底部与Trp84结合, 而是在“半途”与Phe330形成N+-π相互作用、与Asp72形成盐桥以及与Tyr121氢键结合(KBI4); ⑤苄基沉入到峽道底部, 与Trp84发生π-π堆积作用(KBI5)[6]。
2 基于受体结构的药物发现
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以互补性原理为基础的SBDD是继QSAR之后CADD的重要进展。用X-射线衍射测定受体蛋白或复合物的晶体三维结构, 或用二维核磁共振解析蛋白质在溶液中的构象, 以及冷冻电子显微镜技术使结构生物学不断成熟, 成为药物发现的常规技术, 它们与基于片段的药物发现(FBDD)、分子对接(docking) 和分子动力学模拟(MD simulation) 等技术结合, 增加CADD的能力, 促进了许多新药物分子实体(NME) 的发现。表 1[7-29]列出一些代表性的上市药物、先导物(或苗头分子) 和上市药物的化学结构(限于篇幅略去分子结构的演化过程)。
下面简要叙述维奈托克(10, venetoclax) 的结构演化过程。B细胞淋巴瘤(Bcl) 蛋白家族中包含有抗凋亡蛋白如Bcl-2和Bcl-xL, 与促凋亡蛋白如Bak、Bax和Bad相互作用和制约, 精确地调控细胞行为。一些肿瘤为了避免和逃逸凋亡, 高表达Bcl-2或Bcl-xL, 因而成为研制抗肿瘤药物的靶标。Bcl-xL和Bcl-2的疏水性沟槽是结合促凋亡蛋白Bak、Bax和Bad的结合部位。该蛋白-蛋白相互作用的面积广泛(750~1 500 Å2) 且表浅, 没有特征性结合位点。基于蛋白的三维结构, 采用基于片段设计方法(SAR by NMR), 以确定片段性质及其定位。筛选了近万个化合物得到了49个Kd值< 5 mmol·L-1苗头化合物为第一结合位点, 其中4′-氟-联苯-4-甲酸的二维核磁显示可引起Bcl-xL疏水沟槽内的Gly94和Gly138的15N化学位移变化, 计算得出Kd为300 μmol·L-1。第二结合位点筛选了3 000多个分子, 得到24个(Kd < 5 mmol·L-1) 化合物, 例如萘酚和联苯等。二者经不同连接基得到10a, Ki = 1.4 μmol·L-1, 但NMR显示与Bcl-xL结合并非最佳状态, 还发现有第三结合位点, 经设计合成得到10b, Ki = 36 nmol·L-1, 图 2为10b与Bcl-xL结合图。
化合物10b是基于Bcl-xL结构设计的, 没有考虑对Bcl-2蛋白的抑制, 由于对人体多种高表达Bcl-2肿瘤抑制作用很弱, 以致不能阻止Bcl-2蛋白的抗凋亡作用, 所以抑瘤谱较窄。研究者意识到当初对靶标可药性(druggability) 的认识有局限性。
10b高活性抑制Bcl-xL, 但介质中若含有1%人血清, 活性下降69倍, 含10%血清则完全失活。后来证明血清中白蛋白III (HAS-III) 可与10b的酸性基团-CO-NH-SO2- (拉电子的羰基与磺酰基使NH具有酸性) 结合, 降低了抑制Bcl-xL的活性。NMR研究发现, 10b的第一片段氟代联苯基与Bcl-xL的结合和与同HAS-III作用环境不同, Bcl-xL在氟端尚留有空间, 而且发生部分溶剂化, 而HAS-III结合的氟苯基被非极性残基完全包围, 没有空隙。提示该片段也可加入或变换为极性基团, 以阻止与HAS-III的结合[30]。
为此, 变换氟代联苯基部分, 引入含有极性基团的碳环或碳链, 发现10c对Bcl-xL具有高抑制活性而HAS-III不影响其作用。10c可促进放射治疗或紫杉醇对非小细胞肺癌(高表达抗凋亡蛋白Bcl-xL) 的抑制, 表明有促凋亡作用。小鼠移植对多种细胞毒药物无效的人肿瘤A549细胞, 10c与紫杉醇合用, 抑制率达60%~70%, 且未见增加毒性。
然而10c只是基于Bcl-xL结构设计的, 没有考虑对Bcl-2蛋白的抑制, 对人体多种高表达Bcl-2的肿瘤抑制作用很弱, 不能阻断Bcl-2蛋白的抗凋亡作用, 所以抑瘤谱窄。Bcl-xL与Bcl-2蛋白序列的同源性虽然只有49%, 但三维结构却很相似[31], 例如两个蛋白都有疏水型沟槽, 只是Bcl-2的沟槽较宽和深, 这个区别为继续修饰化合物结构提供了着力点。为了提高抗肿瘤活性, 设定的新目标是对Bcl-xL/Bcl-2双靶标作用。
化合物10d是变换片段1时所合成的化合物, NMR研究表明, 10d的苯乙基呈伸展型构象, 结合于Bcl-xL疏水沟槽; 而苯乙基与Bcl-2的结合则深入到疏水沟槽的深部, 埋入其中。这为设计双靶标抑制剂提供了修饰位置。图 3是10d经NMR研究确定的与Bcl-xL和Bcl-2的结合模式。然而10d对Bcl-xL/Bcl-2的活性都不够强。
以10c的4′, 4′-二甲基哌啶的二甲基作为“把手”进行基团变换或引长, 经多轮SAR优化, 得到化合物10e, 对高表达Bcl-xL和Bcl-2的细胞活性EC50分别为0.018和0.016 μmol·L-1, 而结合HAS-III的作用很弱。然而10e的溶解性和药代性质有缺陷, 仍需优化。
下一步是优化物化和药代性质, 几经变换不同位置的基团, 得到了10f, 对Bcl-xL细胞EC50 = 4.2 nmol·L-1, Bcl-2细胞EC50 = 5.9 nmol·L-1, 对HAS-III的作用很弱, AUC = 6.26 µmol·L-1·h。10f确定为候选化合物, 代号为ABT-263, 定名为navitoclax, 进入临床试验研究[32]。
Navitoclax (10f) 的Ⅱ期临床显示对患者有抗肿瘤作用, 但出现血液毒性, 与临床前实验发现与剂量依赖性地降低血小板相吻合, 是由于抑制了Bcl-xL蛋白的缘故。这个结果质疑了Bcl-xL可药性。下一步的结构变换是去除对Bcl-xL抑制作用, 只保留和提高抑制Bcl-2的活性。
10f与Bcl-xL和Bcl-2沟槽中主要结合位点都是P2和P4疏水腔, 二者之差异难以区分。因而从药物化学的构效关系作优化探索。通过系统地除去或变换重要的结合基团, 得到去除苯硫基的化合物10g, 对Bcl-2虽然失去了部分活性(Ki = 59 nmol·L-1), 但明显降低了抑制Bcl-xL作用(Ki = 5 540 nmol·L-1), 提示可区分两个靶标蛋白。
化合物10g与Bcl-2的晶体结构显示, 片段3占据的P4空间变小, 降低了与Bcl-xL作用。另一个特征是由于10g与Bcl-2二聚体结合, 引起第2个Bcl-2蛋白的色氨酸残基(Trp30) 嵌入到10g结合的P4腔内, 导致Trp30的吲哚环与硝基苯形成π-π叠合作用, 氮原子与Bcl-2的Asp103发生氢键结合。图 4是10g与Bcl-2二聚体的晶体图, 绿色的吲哚环与硝基苯发生π-π叠合, 氮原子与Asp103发生氢键结合。为了模拟这个结合特征将吲哚环经醚键连接在母核苯环上, 化合物10h结合Bcl-2有高度选择性(图 5), Ki < 0.1 nmol·L-1, 与Bcl-xL结合的Ki > 660 nmol·L-1, 活性相差千倍。
进而将前述药代优化的片段加以整合(干预蛋白-蛋白相互作用的分子结构作局部变换往往不影响整体的结合), 最终优化出化合物10, 对Bcl-2高表达的急性淋巴白血病细胞EC50 = 8 nmol·L-1, 而对Bcl-xL高表达的H146细胞EC50 > 4 000 nmol·L-1。10消除了抑制血小板的不良反应, 小鼠灌胃100 mg·kg-1, AUC = 2 261 µg·h·mL-1, 血小板计数未见变化。10定为候选化合物, 命名为维奈托克, 经临床试验, 证明对17号短臂染色体缺失的慢性淋巴白血病有效, 于2016年4月FDA批准上市[33]。
3 基于靶标结构的虚拟筛选和药物发现
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基于靶标结构的虚拟筛选(SBVS) 虽然也属分子对接范畴, 但与前述先导物优化的分子对接的目标和所处的阶段不同, SBVS旨在发现先导物, 是研究的初始阶段, 因而有更多不确定性因素。虚拟筛选基于受体与配体互补性结合, 其成功的标志是实验证实的有预期活性的结果, 筛选出的结构新颖, 不同于已有的分子骨架, 而且是值得进一步研究的类药性分子等。
虚拟筛选常用的分子对接软件有商用的GLIDE、DOCK系列、AutoDock系列和GOLD等。筛选的化合物库的规模为数百万到上亿个分子。分子对接找到化合物经打分函数表征和排序, 还要剔除假阳性化合物。Zhu等[34]最近总结分析了近15年来基于靶标结构的虚拟筛选的文献, 分析了不同对接软件的应用状况、靶标的分布, 以及在创制新药方面的作用。下面列举成功的或重要进展的实例简要解析虚拟筛选在新药创制特征。
3.1 抗COVID-19病毒药恩西曲韦
2019年全球暴发冠状病毒的流行性感染(COVID-19), 日本盐野义公司着手研制病毒增殖的重要蛋白酶3CLpro抑制剂, 以阻断多聚蛋白的成熟而终止病毒的复制和繁殖。设定的研制目标是可口服非肽类非共价结合的小分子药物。研制策略是基于冠状病毒蛋白酶3CLpro的结构, 经虚拟筛选以获得先导化合物。为进行分子对接的3CLpro模板是使用3CLpro与抑制剂ML-188 (11) 复合物的晶体结构(图 6), 图中的蓝圈表示3CLpro发生氢键的接受体位置, 红圈为疏水基团, 作为药效团过滤(pharmacophore filter) 的特征。对接的软件是GLIDE, 用公司的数十万个化合物作分子对接, 并用药效团滤器甄别, 将对接高打分的前300个苗头分子作抑酶实验, 同时经质谱分析以排除假阳性。最后确定了化合物12为先导物。
根据11与3CLpro复合物的晶体结构, 将二氟甲氧基的位置与苯环环化, 变换成苯并吡唑, 以与Tyr26发生π-π相互作用, 酰胺片段用三唑置换以与His163发生π-π相互作用(三唑或咪唑可视作酰胺的等排结构), 再对个别基团微调, 优化出抗COVID药物恩西曲韦(13, ensitrelvir), 于2022年2月在日本上市[35]。图 7是恩西曲韦与3CLpro蛋白酶复合物的晶体图。
3.2 乙酰辅酶A羧化酶抑制剂福尔索司他
乙酰辅酶A羧化酶(ACC) 是脂肪酸从头合成的限速酶, 已知有ACC1和ACC2两种亚型都与脂肪酸代谢密切相关。抑制ACC及其二聚化可阻止脂肪酸的体内合成, 促进脂肪酸氧化代谢, ACC是研制治疗肥胖症、糖尿病和非酒精性脂肪肝(NASH) 的靶标。
Harriman等[36]为研制ACC1和ACC2抑制剂, 采用基于结构的虚拟筛选寻找先导化合物。以索拉芬A (14, soraphen A) 与人ACC2的BC域(hACC2 BC) 复合物晶体结构为筛选模板。索拉芬A是从黏细菌Sorangium cellulosum中分离出的一种I型聚酮类抗生素, 不仅有抗真菌作用, 而且有强效抑制ACC酶活性。用分子对接软件GLIDE筛选了130万个化合物, 基于对参与结合的重要氨基酸能够发生氢键结合、静电和疏水相互作用以及可置换高能结构水分子的综合打分, 从中选出250个多样性结构的化合物。经体外实验测定对hACC1和hACC2的抑制活性, 发现数个结构不同的苗头分子, 经底物动力学分析, 证明不是结合于酶的催化中心。确定了化合物15 (ND-022) 为先导物, 其活性为hACC1 IC50 = 3.9 μmol·L-1; hACC2 IC50 = 6.6 μmol·L-1。图 8是hACC2 BC分别与ND-022 (15) 和与索拉芬A复合物晶体结构的叠合图, 显示有相同的结合位点和部分的结构重叠。
化合物15经结构优化(活性、选择性和成药性), 研制出化合物16 (代号ND-630), 对hACC1和hACC2的IC50分别为2.1和6.1 nmol·L-1, 图 9是16与hACC2 BC复合物晶体衍射图。16定名为福尔索司他(firsocostat), 现处于临床Ⅱ期, 治疗NASH引起的肝纤维化。
3.3 FTO抑制剂恩他卡朋类减肥药的研制
始自SBVS另一个进入临床研究的实例是研制以脂肪堆积和肥胖相关蛋白(FTO) 为靶标的药物, 治疗肥胖病和糖尿病。FTO是个酶蛋白, 是一种依赖二价铁和酮戊二酸的一种双加氧酶, 催化mRNA的N6-甲基腺苷(M6A) 去甲基化, 作为转录辅助因子参与到调控脂肪细胞的生长和发育, 在调节体重和脂肪含量方面具有重要作用[37]。
北京生命科学研究所黄牛团队研究FTO抑制剂, 以FTO-底物复合物结构为模板, 进行基于结构虚拟筛选, 软件为DOCK 3.5版本, 用FDA批准的1 323个药物作分子对接(药物再利用), 经实测19个打分高的药物对FTO的活性, 发现儿茶酚-O-甲基转移酶(COMT) 抑制剂、帕金森病治疗药恩他卡朋(17, entacapone) 抑制FTO去甲基活性IC50 = 3.5 μmol·L-1 (Ki = 234 nmol·L-1)。动物实验表明灌胃恩他卡朋显著降低小鼠体重和禁食小鼠的血糖水平。由于恩他卡朋是已批准的药物, 已在临床试验治疗肥胖病患者[38]。此外, 恩他卡朋类似物的SAR和结构优化, 例如用吗啉置换二乙氨基的化合物18活性提高到IC50 = 0.7 μmol·L-1。
结构生物学研究表明, 恩他卡朋与FTO和Zn2+的复合物单晶衍射提示(图 10), 硝基儿茶酚的间位羟基与FTO结合位点的Arg322和Tyr106形成氢键, 氰基与Zn2+发生螯合作用, 羰基与Asn205形成氢键, 柔性端基二乙基丙酰胺深埋于辅酶结合部位。恩他卡朋的分子对接与晶体结构的结合模式是完全一致的[39]。
4 人工智能辅助药物发现
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4.1 概说
AIDD是近年来迅速发展的平台技术, 本质上属于扩大和深化了的CADD。AI是训练机器使它像人类一样执行认知功能的能力, 在感知、学习、推理中解决问题。机器学习(machine learning, ML) 是人工智能的独特子集, 它用数据训练机器学习, 在数据中寻找模式并尝试得出结论, 它需要数据来测试新的想法或方法, 数据为人工智能提供支持。现代ML主要由多层人工神经网络实现, 其数据处理流程为: 信号→选择特征→映射。人工神经网络自己选择信号的特征以避免人工挑选可能产生的偏见, 其本质是对数据中隐含的内在规律进行多次拟合。大数据、算法、算力被视为AI技术的三要素。
4.2 进入临床研究的第一个AI候选药物DSP-1181以及DSP-0038
日本住友制药与英国AI公司Exscientia合作, 研制治疗强迫症(obsessive-compulsive disorder, OCD) 药物, 目标为5-羟色胺1A受体(5-HT1A) 和多巴胺D4受体的双靶标激动剂, 研制出代号为DSP-1181, 于2020年1月在日本开始了临床Ⅰ期研究, 成为全球第一个进入临床研究的AI药物。研制者未公开发现的过程, 也没有披露DSP-1181的化学结构, 但从公开的专利和实施例披露的化合物次数估计是化合物19~21[40], 分子骨架与1985年上市的抗精神病的氟哌啶醇(22, haloperidol) 相似, 在研究镇痛药哌替啶引长甲基成丁酰苯基得到的衍生物22, 发现其有很强的抗精神失常作用, 是通过阻断脑内多巴胺D2受体而发挥作用, 抑制多巴胺神经元的功能。AI从大数据中自学习, 在1年内设计出并进入临床研究, 虽与氟哌啶醇结构相似, 却翻转拮抗剂为激动剂是耐人寻味的。然而遗憾的是DSP-1181未达到临床目标而中止于Ⅰ期临床阶段。
住友与Exscientia还研制了DSP-0038拟作治疗阿尔茨海默病药物, 2021年在美国开始Ⅰ期临床研究。DSP-0038是5-HT1a受体激动剂和5-HT2a受体拮抗剂双靶向化合物, 虽然没有明确披露其化学结构, 但从194个实施例结构分析, 通式结构为23[41]。比对实施例与上市的治疗非典型抗精神病药的化学结构, 提示分子骨架非常相似。例如1993年上市的利司培酮(24, risperidone) 是当时新结构类型的抗精神病药物, 低剂量可阻断中枢的5-HT2受体, 高剂量还可阻断多巴胺D2受体[42]。2001上市的帕利哌酮(25, paliperidone) 是利司培酮的主要活性代谢产物[43]。齐拉西酮(26, ziprasidone) 上市于2006年[44], 是中枢多巴胺2 (D2) 受体和5-羟色胺2A (5HT2A) 受体拮抗剂, 它们都是非典型抗精神病治疗药。AI设计的药物没有超越药物化学家的思维框架, 或许是AI学习的训练集结构的多样性不够, 以及表征模型的描述符过于明确, 从而生成的分子显示出训练集中过多的结构痕迹。
4.3 抗菌药的研究—Haricin的发现
MIT的科学家用AI研究新的抗菌药, 实现了分子结构的突破。抗菌药长时间的应用或滥用, 使细菌产生耐药性, 药物失去效力。研究者训练了一个预测抗菌分子的深度神经网络, 从“药物再利用中心库” (drug repurposing hub) 中发现了与现有抗菌药的结构迥异的广谱抗菌化合物。简要的研制要点如下。
4.3.1 训练集
从FDA批准的药物库选出1 760个结构多样和活性不同的药物及800个天然化合物, 去掉重复分子共2 335个化合物。以一定浓度测定对大肠杆菌E. coli BW25113的抑制率, 以80%作为活性和无活性(hit/non-hit) 的截断值, 其中有120个对大肠杆菌有抑制作用。这120个分子的多样性结构应该有不同的作用机制和抑菌靶标。
4.3.2 建立二元分类模型
分子的化学结构用图论(graph theory) 表示, 原子为图中的节点(node), 节点的初始特征有明确的物理化学定义, 如原子类型、表面电荷、杂化态、手性、芳香性、原子连接氢原子等, 连接键为图的边(edge)。例如单/双/叁/共轭键、环状、顺反异构等。将这些连续向量初始特征值经消息传播的迭代, 即模型沿着编码了近邻的节点(原子) 和边(键型) 的连接信息作多次的消息传播, 从而找到节点最终稳定的特征向量表达方式, 再加和成表达分子的最终数字向量。
按初筛结果将2 335个分子经二进制编码分成hit和non-hit, 用这些数据训练二元分类模型, 该模型以后预测新化合物是否可能抑制大肠杆菌的生长。AI运行的切入点, 是选择模型的特征, 为化学分子找到一个可以量化的表现形式。既往使用描述符(descriptor) 或向量指纹(vector fingerprint) 的有无作为表征结构的量化特征, 因而生成的分子往往带有训练集中分子的结构印迹。MIT的数据转化采用了消息传递神经网络(message passing neural networks model, MPNN) 避免这种情况发生。
4.3.3 虚拟筛选生成潜在的活性化合物
将MPNN模型提取的分子特征向量, 经训练产生深度学习模型, 用此模型虚拟筛选药物再定位中心库(drug repurposing hub)[45]中6 111个已有临床试验的候选药, 得到99个对E. coli的抑制预测有高打分值的化合物。
4.3.4 实验测定与halicin发现
实验测定抑菌活性, 按照设定的截断值选出51个, 经对结构与训练集化合物的相似性、(现实或曾经的) 临床状态和安全性(用AI模型预测) 进行甄别, 得到了曾作为激酶抑制剂而研制的化合物27[46], 命名为halicin。27的MIC值为2 μg·mL-1, 与含硝基的抗菌化合物(如甲硝唑) 谷本相似度系数(Tanimoto similarity coefficient) 为0.21, 提示相似度较低。27具有广谱杀菌作用, 对结核分枝杆菌等36种碳青霉烯耐药菌株显示杀菌作用, MIC在μg·mL-1水平。深入研究杀菌机制是干扰细菌形成跨膜的电化学梯度。电化学梯度能协助细菌产生能量, 没有这种梯度, 细菌就会死亡。重塑电化学梯度不是简单的几个突变就能完成的, 因此27也最大程度上减少了耐药的产生[47]。
4.3.5 发现halicin的启示
广谱和抗耐药菌的halicin发现和因此发现新的作用机制, 证明AIDD确实可超越人们的思维范畴。首先, 用于训练模型的不拘一格, 样本是从宏观的抑制E. coli的表型作hit和non-hit的二元区分, hit的作用靶标可能包含了RNA、酶、离子通道或细胞膜等, 因而从更广泛空间发现新的作用环节和化合物, 避免了局限于给定靶标作分子生成。其次, 将化学结构(组成分子的原子性质和键合方式) 依据图论原理转换成AI可学习处理的数字向量, 通过深度神经网络的处理和自学, 形成的特定模型较少有研制者的人为干预, 避免了AI提取hit中的描述符或指纹向量, 构建出相似于hit结构的me-too分子, 也难以发现干扰细菌电化学(pH) 梯度的新抗菌环节。人们不知道AI模型在学习和运作中怎样产生的知识, 这正是研制者让AI替他们想和做出想象不到的事情。Halicin的发现标志着AI超越了人脑, 同时找到了崭新的作用靶标和强效分子。
5 小结
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AIDD是深化的CADD, 是用更多的数据和更复杂的算法在更强力的计算机进行药物发现。AIDD的学习能力是传统CADD所没有的。20世纪60年代QSAR开拓了计算机辅助药物研究, 是在不知靶标结构的情况下, 用构效关系方程揭示同类药物的结构变换引起活性变化的量变关系, 映射出药物与靶标的结合特征, 以指导新化合物的设计, 所以属于结构优化的范畴。20世纪90年代以来, 结构生物学提供靶标三维结构, 得以进行基于靶标结构的药物发现、基于片段的药物发现、基于靶标结构的虚拟筛选等, 在靶标-配体结合的原子和基团的微观水平进行分子设计, 基本过程是结合人类智慧与计算机协助以发现和优化先导化合物。近30年来诞生了许多同类第一(FIC) 和同类最佳(BIC) 的新药。AIDD则是新的变革和飞跃, 例如发现halicin较少有人为的诱导因素, 自学训练的多样结构和模型的稳健性, 使得AI生成了不同于现有的抗菌药结构, 同时也揭示了新的作用环节, 这些都是研究者所料未及的。
致谢: 本文完稿后承蒙中山大学徐峻教授的宝贵意见, 谨致谢忱。
作者贡献: 郭宗儒负责文献整理、论文撰写与修改。
利益冲突: 作者声明不存在利益冲突。

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2023年第58卷第10期
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doi: 10.16438/j.0513-4870.2023-0702
  • 接收时间：2023-06-05
  • 首发时间：2025-11-21
  • 出版时间：2023-10-12
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  • 收稿日期：2023-06-05
  • 修回日期：2023-07-22
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    中国医学科学院、北京协和医学院药物研究所, 北京 100050

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*郭宗儒, E-mail:
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2种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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