更新于：2024-04-01

CDK7

细胞周期蛋白依赖性激酶7

更新于：2024-04-01

基本信息

别名

39 kDa protein kinase、CAK、CAK1

+ [13]

简介

Serine/threonine kinase involved in cell cycle control and in RNA polymerase II-mediated RNA transcription. Cyclin-dependent kinases (CDKs) are activated by the binding to a cyclin and mediate the progression through the cell cycle. Each different complex controls a specific transition between 2 subsequent phases in the cell cycle. Required for both activation and complex formation of CDK1/cyclin-B during G2-M transition, and for activation of CDK2/cyclins during G1-S transition (but not complex formation). CDK7 is the catalytic subunit of the CDK-activating kinase (CAK) complex. Phosphorylates SPT5/SUPT5H, SF1/NR5A1, POLR2A, p53/TP53, CDK1, CDK2, CDK4, CDK6 and CDK11B/CDK11. CAK activates the cyclin-associated kinases CDK1, CDK2, CDK4 and CDK6 by threonine phosphorylation, thus regulating cell cycle progression. CAK complexed to the core-TFIIH basal transcription factor activates RNA polymerase II by serine phosphorylation of the repetitive C-terminal domain (CTD) of its large subunit (POLR2A), allowing its escape from the promoter and elongation of the transcripts (PubMed:9852112). Phosphorylation of POLR2A in complex with DNA promotes transcription initiation by triggering dissociation from DNA. Its expression and activity are constant throughout the cell cycle. Upon DNA damage, triggers p53/TP53 activation by phosphorylation, but is inactivated in turn by p53/TP53; this feedback loop may lead to an arrest of the cell cycle and of the transcription, helping in cell recovery, or to apoptosis. Required for DNA-bound peptides-mediated transcription and cellular growth inhibition.

关联

项与 CDK7 相关的药物

Seliciclib

靶点

CDK2 x CDK7 x CDK9

作用机制

CDK2抑制剂 [+2]

在研机构

Centre National de la Recherche Scientifique [+3]

原研机构

Centre National de la Recherche Scientifique

在研适应症

垂体ACTH分泌过多 [+2]

非在研适应症

晚期恶性实体瘤 [+9]

最高研发阶段临床2期

首次获批国家/地区-

首次获批日期1800-01-20

Samuraciclib hydrochloride

靶点

CDK7

作用机制

CDK7抑制剂

在研机构

Carrick Therapeutics Ltd.初创企业

原研机构

Emory University

在研适应症

HR阳性/HER2阴性乳腺癌 [+4]

非在研适应症-

最高研发阶段临床2期

首次获批国家/地区-

首次获批日期1800-01-20

GTAEXS-617

靶点

CDK7

作用机制

CDK7抑制剂

在研机构

Exscientia AI Ltd. [+3]

原研机构

Exscientia AI Ltd.

在研适应症

晚期恶性实体瘤 [+2]

非在研适应症-

最高研发阶段临床1/2期

首次获批国家/地区-

首次获批日期1800-01-20

项与 CDK7 相关的临床试验

NCT06125522 / Recruiting临床1/2期

TACTIVE-U: AN INTERVENTIONAL SAFETY AND EFFICACY PHASE 1B/2, OPEN-LABEL UMBRELLA STUDY TO INVESTIGATE TOLERABILITY, PK, AND ANTITUMOR ACTIVITY OF VEPDEGESTRANT (ARV-471/PF-07850327), AN ORAL PROTEOLYSIS TARGETING CHIMERA, IN COMBINATION WITH OTHER ANTICANCER TREATMENTS IN PARTICIPANTS AGED 18 AND OLDER WITH ER+ ADVANCED OR METASTATIC BREAST CANCER, SUB-STUDY C (ARV-471 IN COMBINATION WITH SAMURACICLIB)

The purpose of this study is to learn about the safety and effects of the study medicine called vepdegestrant. The safety and effects of vepdegestrant will be see when given with other medicines. Vepdegestrant is studied to see if it can be a possible treatment for advanced metastatic breast cancer. This type of cancer would have spread from where it started (breast) to other parts of the body and would be tough to treat.
The study is seeking for participants who have breast cancer that:
is hard to treat (advanced) and may have spread to other organs (metastatic). is sensitive to hormonal therapy (it is called estrogen receptor positive).
is no longer responding to treatments taken before starting this study.
This study is divided into separate sub-studies.
For Sub-Study C:
All the participants will receive vepdegestrant and a medicine called samuraciclib.
Vepdegestrant and samuraciclib will be taken once in a day by mouth. The medicines will be taken at home. The experience of people receiving the study medicines will be studied. This will help see if the study medicine is safe and effective.
Participant will continue to take vepdegestrant and samuraciclib until:
their cancer is no longer responding, or
side effects become too severe.
They will have visits at the study clinic about every 4 weeks.

开始日期2024-01-10

申办/合作机构

Pfizer Inc.

[+2]

NCT05963984 / Recruiting临床2期

An Open-label, Interventional, Multicenter, Randomized, Phase 2 Study of Fulvestrant With or Without Samuraciclib in Participants With Metastatic or Locally Advanced Hormone Receptor (HR) Positive and Human Epidermal Growth Factor Receptor (HER)2-Negative Breast Cancer (BC)

The purpose of this study is to evaluate the safety and efficacy of samuraciclib in combination with fulvestrant versus fulvestrant alone in adult participants with metastatic or locally advanced Hormone Receptor (HR) positive and Human Epidermal Growth Factor Receptor (HER)2-negative breast cancer.

开始日期2023-12-14

申办/合作机构

Carrick Therapeutics Ltd.初创企业

[+1]

NCT05963997 / Recruiting临床1/2期

A Phase 1b/2 Open-label Study of Samuraciclib in Combination With Elacestrant in Participants With Metastatic or Locally Advanced Hormone Receptor-positive and Human Epidermal Growth Factor Receptor 2-negative Breast Cancer

This is an international, multisite, open-label, Phase 1b/2 study, to confirm safety and efficacy of samuraciclib in combination with elacestrant in adult participants with metastatic or locally advanced Hormone Receptor (HR) positive and Human Epidermal Growth Factor Receptor (HER)2-negative breast cancer.

开始日期2023-10-09

申办/合作机构

Carrick Therapeutics Ltd.初创企业

[+1]

100 项与 CDK7 相关的临床结果

登录后查看更多信息

100 项与 CDK7 相关的转化医学

登录后查看更多信息

0 项与 CDK7 相关的专利（医药）

登录后查看更多信息

852

项与 CDK7 相关的文献（医药）

2024-02-21·Clinical cancer research : an official journal of the American Association for Cancer Research

Selective CDK7 inhibition suppresses cell cycle progression and MYC signaling while enhancing apoptosis in therapy-resistant estrogen receptor positive breast cancer.

Article

作者: Cristina Guarducci ; Agostina Nardone ; Douglas Russo ; Zsuzsanna Nagy ; Capucine Heraud ; Albert Grinshpun ; Qi Zhang ; Allegra Freelander ; Matthew Joseph Leventhal ; Avery Feit ; Gabriella Cohen Feit ; Ariel Feiglin ; Weihan Liu ; Francisco Hermida-Prado ; Nikolas Kesten ; Wen Ma ; Carmine De Angelis ; Antonio Morlando ; Madison O'Donnell ; Sergey Naumenko ; Shixia Huang ; Quang-De Nguyen ; Ying Huang ; Luca Malorni ; Johann S Bergholz Villafane ; Jean J Zhao ; Ernest Fraenkel ; Elgene Lim ; Rachel Schiff ; Geoffrey I Shapiro ; Rinath Jeselsohn

PURPOSE:

Resistance to endocrine therapy (ET) and CDK4/6 inhibitors (CDK4/6i) is a clinical challenge in estrogen receptor (ER) positive (ER+) breast cancer (BC). Cyclin-dependent kinase 7 (CDK7) is a candidate target in endocrine resistant ER+ BC models and selective CDK7 inhibitors (CDK7i) are in clinical development for the treatment of ER+ BC. Nonetheless, the precise mechanisms responsible for the activity of CDK7i in ER+ BC remain elusive. Herein, we sought to unravel these mechanisms.

EXPERIMENTAL DESIGN:

We conducted multi-omic analyses in ER+ BC models in vitro and in vivo including models with different genetic backgrounds. We also performed genome wide CRISPR knock-out library screens to identify potential therapeutic vulnerabilities in CDK4/6i resistance models.

RESULTS:

We found that the on-target anti-tumor effects of CDK7 inhibition in ER+ BC are in part p53 dependent, involve cell-cycle inhibition and suppression of c-MYC. Moreover, CDK7 inhibition exhibited cytotoxic effects, distinctive from the cytostatic nature of ETs and CDK4/6i. CDK7 inhibition resulted in suppression of ER phosphorylation at S118, however, long-term CDK7 inhibition resulted in increased ER signaling, supporting the combination of ET with a CDK7i. Lastly, genome wide CRISPR/Cas9 screens identified CDK7 and MYC signaling as putative vulnerabilities in CDK4/6i resistance, and CDK7 inhibition effectively inhibited CDK4/6i resistant models.

CONCLUSIONS:

Taken together, these findings support the clinical investigation of selective CDK7 inhibition combined with ET to overcome treatment resistance in ER+ BC. In addition, our study highlights the potential of increased MYC activity and intact P53 as predictors for sensitivity to CDK7 inhibitor-based treatments.

2024-02-14·British journal of cancer

Cyclin-dependent kinase 7 (CDK7) inhibitors as a novel therapeutic strategy for different molecular types of breast cancer.

Review

作者: Xue Song ; Chen Fang ; Yan Dai ; Yang Sun ; Chang Qiu ; Xiaojie Lin ; Rui Xu

BACKGROUND:

Cyclin-dependent kinase (CDK) 7 is aberrantly overexpressed in many types of cancer and is an attractive target for cancer therapy due to its dual role in transcription and cell cycle progression. Moreover, CDK7 can directly modulate the activities of estrogen receptor (ER), which is a major driver in breast cancer. Breast cancer cells have exhibited high sensitivity to CDK7 inhibition in pre-clinical studies.

METHODS:

In this review, we provide a comprehensive summary of the latest insights into CDK7 biology and recent advancements in CDK7 inhibitor development for breast cancer treatment. We also discuss the current application of CDK7 inhibitors in different molecular types of breast cancer to provide potential strategies for the treatment of breast cancer.

RESULTS:

Significant progress has been made in the development of selective CDK7 inhibitors, which show efficacy in both triple-negative breast cancer (TNBC) and hormone receptor-positive breast cancer (HR+). Moreover, combined with other agents, CDK7 inhibitors may provide synergistic effects for endocrine therapy and chemotherapy. Thus, high-quality studies for developing potent CDK7 inhibitors and investigating their applications in breast cancer therapy are rapidly emerging.

CONCLUSION:

CDK7 inhibitors have emerged as a promising therapeutic strategy and have demonstrated significant anti-cancer activity in different subtypes of breast cancer, especially those that have been resistant to current therapies.

2024-02-06·Antimicrobial agents and chemotherapy

Small molecule inhibitors of transcriptional cyclin-dependent kinases impose HIV-1 latency, presenting "block and lock" treatment strategies.

Article

作者: Riley M Horvath ; Zabrina L Brumme ; Ivan Sadowski

Current antiretroviral therapy for HIV-1 infection does not represent a cure for infection as viral rebound inevitably occurs following discontinuation of treatment. The "block and lock" therapeutic strategy is intended to enforce proviral latency and durably suppress viremic reemergence in the absence of other intervention. The transcription-associated cyclin-dependent protein kinases (tCDKs) are required for expression from the 5´ HIV-1 long-terminal repeat, but the therapeutic potential of inhibiting these kinases for enforcing HIV-1 latency has not been characterized. Here, we expanded previous observations to directly compare the effect of highly selective small molecule inhibitors of CDK7 (YKL-5-124), CDK9 (LDC000067), and CDK8/19 (Senexin A), and found each of these prevented HIV-1 provirus expression at concentrations that did not cause cell toxicity. Inhibition of CDK7 caused cell cycle arrest, whereas CDK9 and CDK8/19 inhibitors did not, and could be continuously administered to establish proviral latency. Upon discontinuation of drug administration, HIV immediately rebounded in cells that had been treated with the CDK9 inhibitor, while proviral latency persisted for several days in cells that had been treated with CDK8/19 inhibitors. These results identify the mediator kinases CDK8/CDK19 as potential "block and lock" targets for therapeutic suppression of HIV-1 provirus expression.

171

项与 CDK7 相关的新闻（医药）

2024-03-25

·BioSpace

Edgewood Oncology Emerges From Stealth with $20 Million in Series A Financing to Advance BTX-A51 in Patients with Hematologic Malignancies and Genetically-Defined Solid Tumors

BTX-A51 is a Novel, Small Molecule, Multi-Kinase Inhibitor That Synergistically Co-Targets Master Regulators of Cancer to Promote Cancer Cell Death Enrollment is Underway in Phase 2a Combination Study in Acute Myeloid Leukemia; Enrollment in Phase 2a Targeted Breast Cancer Study to Start in Q2 2024 Financing Enables Company to Fund Multiple Studies to Clinical Proof-of-Concept into 2026 BROOKLINE, Mass.--(BUSINESS WIRE)-- Edgewood Oncology, a clinical-stage biotechnology company focused on delivering BTX-A51 to patients with hematologic malignancies and genetically-defined solid tumors, today emerged from stealth with $20 million in Series A financing backed by Alta Partners to advance the clinical development of BTX-A51 in acute myeloid leukemia (AML) and a precision medicine approach to breast cancer. BTX-A51 is a first-in-class, small molecule, multi-kinase inhibitor of casein kinase 1 alpha (CK1α), cyclin-dependent kinase 7 (CDK7) and CDK9 that synergistically co-targets master regulators of cancer to promote programmed cell death, or apoptosis. Edgewood acquired the rights to BTX-A51 from Yissum, the technology transfer company of The Hebrew University of Jerusalem, in 2023 and will use the Series A funding to advance efficacy studies in AML and breast cancer. “We formed Edgewood Oncology because of the synergistic mechanism of action and promising safety and anti-tumor data that was observed with BTX-A51 in Phase 1 in AML and solid tumor patients. We look forward to further developing this compound using a precision medicine approach in patient populations who lack effective treatment options,” said David N. Cook, Ph.D., chief executive officer, Edgewood Oncology. “Furthermore, we have taken a virtual approach to drug development, which allows us to remain highly-focused, agile and efficient as we advance this investigational compound in the clinic.” Testing the Use of Combination Therapy in AML There exists a significant unmet need for improved first-line regimens in AML as well as for patients with relapsed or refractory (R/R) disease. In a Phase 1 study in heavily pre-treated R/R AML patients, monotherapy BTX-A51 demonstrated a favorable safety pro encouraging antileukemic activity. As a next step, Edgewood initiated a study of BTX-A51 in combination with azacitidine in R/R AML patients in December 2023. The aim of this Phase 2, multicenter, open-label study is to evaluate the response rate (CR, CRh and CRi) as well as the safety, toxicity and pharmacokinetics of BTX-A51 in combination with azacitidine in patients with R/R AML. “We’re incredibly excited about the potential of BTX-A51in this patient population, and we also have good reason to believe that BTX-A51 could be complementary to standard of care in first-line unfit AML,” said Zung Thai, M.D., Ph.D., chief medical officer, Edgewood Oncology. “In Phase 1 monotherapy, we observed several complete responses and a wide therapeutic window and our preclinical data also suggest that BTX-A51 acts synergistically with both azacitidine and venetoclax in human leukemia cells and animal models.” Edgewood is expecting to initiate a Phase 2 study in breast cancer patients with a genetically-defined pro Q2 2024. Leadership Team Comprised of Seasoned Biotech Veterans “Edgewood’s leadership team not only has a strong track record in company formation and drug development, but Dr. Thai brings an in-depth understanding of BTX-A51, having led all clinical and regulatory efforts for this program during his tenure at BiotheryX,” said Dan Janney, managing partner, Alta Partners. “Moreover, we believe Edgewood’s virtual approach to drug development is the best strategy to get innovative new medicines to patients as quickly and efficiently as possible.” The executive team includes Dr. Cook as chief executive officer, and Dr. Thai as chief medical officer. BTX-A51’s scientific founder is Yinon Ben-Neriah, M.D., Ph.D., professor of Immunology and Cancer Research, The Hebrew University of Jerusalem. The company’s board of directors includes Dr. Cook, Dan Janney, and Isan Chen, M.D., a noted clinical oncologist and co-founder, president and chief executive officer, MBrace Therapeutics, Inc. About Edgewood Oncology Edgewood Oncology is a clinical-stage biotechnology company that was founded to deliver on the promise of BTX-A51 for patients with hematologic malignancies and genetically-defined solid tumors. BTX-A51 is a novel small molecule, multi-kinase inhibitor that synergistically co-targets master regulators of cancer to activate programmed cell death, or apoptosis. BTX-A51 holds promise in acute myeloid leukemia and genetically-defined solid tumors, which are the focus of ongoing clinical programs. For more information, please visit , and follow us on LinkedIn. About Alta Partners Founded in 1996, Alta Partners is one of the nation's premier healthcare venture capital firms. Alta has raised $2.6 billion in capital through 11 venture funds and invests in healthcare companies across the development continuum, from company formation to later-stage opportunities, and has funded more than 175 companies in the sector to date. In August 2022, Stat Reports recognized Alta as the country's top performing biotech venture fund. Alta has offices in the San Francisco Bay Area, Denver, and Jackson, WY. For more information, please visit . View source version on businesswire.com: Contacts Tracy Lessor TLOW Communications, LLC 617-519-9827 tracy@tlowcommunications.com Source: Edgewood Oncology View this news release online at:

临床1期临床2期

2024-03-24

·精准药物

Nature世界首例！药物靶点的高分辨率结构可以促进下一代疗法的合理设计！

与小分子抑制剂结合的药物靶点的高分辨率结构可以促进下一代疗法的合理设计。然而，基于结构的方法在难结晶大分子中的应用受到低温电子显微镜（cryo-EM）的分辨率和通量的限制。在这里，我们使用高分辨率冷冻电镜来确定CDK激活激酶的结构，CDK激活激酶是细胞生长和分裂的主要调节因子，处于游离和核苷酸结合状态，并与15种抑制剂复合物，分辨率高达1.8 Å。我们的结构提供了对细胞周期蛋白依赖性激酶 7 活性位点中抑制剂相互作用和水分子网络的详细见解，并提供了对抑制剂选择性机制的见解，从而为下一代疗法的合理设计奠定了基础。这些结果为在基于结构的药物设计中使用高分辨率冷冻电镜建立了方法学框架。01、CADD计算机辅助药物设计目标：掌握包括PDB数据库、靶点蛋白、蛋白质-配体、蛋白-配体小分子、蛋白-配体结构、notepad的介绍和使用、分子对接、蛋白-配体对接、虚拟筛选、蛋白-蛋白对接、蛋白-多糖分子对接、蛋白-水合对接、Linux安装、gromacs分子动力学全程实操、溶剂化分子动力学模拟02、AIDD人工智能药物发现与设计目标：本课程让学员了解药物发现的前沿背景，学习人工智能领域的各类常见算法，熟悉工具包的安装与使用，掌握一定的算法编程能力，能够运用计算机方法研究药物相关问题。通过大量的案例讲解和实践操作，具备一定的AIDD模型构建和数据分析能力03、蛋白晶体结构解析目标：让学员了解蛋白质晶体结构解析的原理、方法与技术，学习分子克隆、蛋白表达纯化、蛋白结晶方法、软件安装，蛋白结构数据处理，得到高分辨率的蛋白晶体结构。使学员通过本次课程的学习，很轻松地解析出蛋白晶体结构，并进行晶体结构的精修。04、机器学习代谢组学目标：熟悉代谢组学和机器学习相关硬件和软件；熟悉代谢组学从样本处理到数据分析的全流程；能复现至少1篇CNS或子刊级别的代谢组学文章图片。05、深度学习基因组学目标：深入学习与了解深度学习基本框架与逻辑，同时掌握基本的生物信息学软件（Linux、R、python等）的使用，让学员能更好的应对基因组数据，挖掘出超越已有知识的新知识。而构建好的深度学习模型去探求新的研究思路和寻找新的潜在生物学机制，更好的服务于自身的科学研究和探索的过程中。06、机器学习微生物组学目标：通过本次培训多个案例的系统讲解让参会学员学会机器学习在微生物组数据分析流程，能够快速运用到自己的科研项目和课题上。07、CRISPR-Cas9基因编辑技术目标：课程从全局出发，由浅入深，课程通过基础入门+应用案例实操演练的方式，从最初的原理讲解到最后的应用实战，学完本课程你将掌握基因编辑技术的相关原理及其应用，此外可以学到基因编辑系统的优化策略，可以学到如何操作常用的生物学软件。能够快速运用到自己的科研项目和课题上。08、单颗粒冷冻电镜结构解析目标：冷冻电镜结构解析课程致力于培养学员掌握冷冻电镜在结构生物学和基于结构的药物设计中的应用，掌握单颗粒冷冻电镜结构解析原理，实验方法和实际应用。通过深入的理论讲解和实际操作，学员将逐步建立对这一关键领域的深刻理解。# Shopping Guide以下为内容介绍WORK OVERVIEWNO.1CADD计算机辅助药物设计内容可向下滑动第一天导论与基础1. 蛋白质三维结构的预测对于药物发现的重要性1.1 同源建模1.2 从头建模2. 蛋白质（酶/靶点）活性位点在药物发现的重要性3. 药物发现中的关键结构特征（特别是小分子）4. 药物辅助发现常用的计算方法4.1 分子对接4.2 虚拟筛选4.3 分子动力学模拟4.4 其他PDB数据库的介绍1.1 检索蛋白1.2 页面功能及解读1.3 数据的下载1.4 PDB文件格式的解读2. PyMol2.1 软件介绍2.2 基本操作介绍2.3 蛋白及小分子表面图、静电势表示2.4 绘制相互作用图及制作简单动画第二天同源建模1. 同源建模原理介绍1.1 同源建模的功能及使用场景1.2 同源建模的方法2. Swiss-Model 同源建模；2.1 同源蛋白的搜索（blast等方法）2.2 蛋白序列比对2.3 蛋白模板选择2.4 蛋白模型搭建2.5 模型评价（蛋白拉曼图）2.6 蛋白模型优化实例讲解与练习：用2019-nCoV spike蛋白序列建模，根据相应参数和方法评价模型小分子构建1. ChemDraw软件介绍1.1 小分子结构构建1.2 小分子理化性质（如分子量、clogP等）计算1.3 分别构建大环、氨基酸、DNA、RNA等分子小分子化合物库2 小分子数据库2.1 DrugBank、ZINC、ChEMBL等数据库介绍及使用2.2 天然产物、中药成分数据库介绍及使用第三天分子对接基础1.1 分子对接原理1.2 分子对接分类1.3 分子对接打分函数2. 常规分子对接实践2.1 对接的执行2.1.1 药物分子配体的准备2.1.2 蛋白受体的准备2.1.3 受体格点计算2.1.3 执行半柔性对接对接结果评价1.2.1 晶体结构构象进行对比1.2.2 能量角度评价对接结果1.2.3 聚类分析评价对接结果1.2.4 最优结合构象的选择2 对接其他方式的实现第四天1 柔性对接1.1 小分子配体优化准备1.2 蛋白受体的准备1.3 柔性残基的定义1.4 蛋白受体格点计算1.5 柔性对接计算及结果评价1.6 半柔性对接与柔性对接比较与选择2 柔性对接其他方式的实现下午基于受体的药物发现1 虚拟筛选的准备1.1 小分子文件的不同格式1.2 openbabel最实用功能的介绍1.3 小分子不同格式的转化2. 基于对接的虚拟筛选2.1 虚拟筛选定义、流程构建及演示2.2 靶点蛋白选择、化合物库获取2.3 虚拟筛选2.4 结果分析（打分值、能量及相互作用分析）第五天上午一些特殊的分子对接1.小分子-小分子对接1.1小分子-小分子相互作用简介1.2小分子结构预处理1.3小分子-小分子对接（糖-小分子为例）1.4对接结果展示与分析2. 蛋白-核酸对接3. 蛋白-蛋白对接下午基于配体的药物发现1. 3D-QSAR模型构建（Sybyl软件）1.1 小分子构建1.2 创建小分子数据库1.3 小分子加电荷及能量优化1.4 分子活性构象确定及叠合1.5 创建3D-QSAR模型1.6 CoMFA和CoMSIA模型构建1.7 测试集验证模型1.8 模型参数分析1.9 模型等势图分析1.10 3D-QSAR模型指导药物设计第六天上午1. linux系统介绍2.常用命令介绍3. linux上程序的安装（gromacs）下午MD实践一：溶剂化下蛋白质分子动力学模拟全面熟悉分子动力学模拟的一般流程第七天上午MD实践二：溶剂化下蛋白质-配体的分子动力学模拟掌握处理非标准残基的力场拟合下午分子动力学模拟中的常用分析命令蛋白-配体结合自由能的结算部分模型案例图片NO.2 AIDD人工智能药物发现与设计内容可向下滑动第一天1 人工智能药物发现(AIDD)简介2 机器学习和深度学习在药物发现领域的应用2.1 分子属性预测与优化2.2 虚拟筛选2.3 药物副作用预测与安全性评估2.4 新药分子设计3 工具介绍与安装3.1 Anaconda3/Pycharm 安装3.2 Numpy 基础3.3 Pandas 基础3.4 Matplotlib 基础3.5 Scikit-learn 基础3.6 Pytorch 基础3.7 RDKit 基础第二天1 机器学习简介1.1 机器学习四要素1.2 数据模块1.3 核心和高级 API2 回归算法与应用2.1 线性回归2.2 Lasso 回归2.3 Ridge 回归2.4 ElasticNset 弹性网络3 分类算法与应用3.1 逻辑回归3.2 朴素贝叶斯3.3 KNN3.4 SVC3.5 决策树3.6 随机森林3.7 集成学习4 聚类算法4.1 KMeans4.2 密度聚类 DBSCAN5 降维5.1 奇异值分解 SVD5.2 主成分分析 PCA5.3 非负矩阵分解 NMF6 模型的评估方法和评价指标6.1 超参数优化6.2 交叉验证6.3 评价指标7 特征工程8 机器学习药物发现案例（一）——化合物生物活性分类模型9 机器学习药物发现案例（二）——化合物生物活性回归模型10 机器学习药物发现案例（三）—— 药物副作用预测模型图1. 副作用在药物-药物相似性网络中传播。第三天1 深度学习与药物发现（一）1.1 深度神经网络1.2 正向和反向传播1.3 优化方法1.3.1 梯度下降增加动力1.3.2 自适应学习1.3.3 Adam1.4 损失函数1.4.1 平均绝对误差1.4.2 均方误差损失函数1.4.3 交叉熵损失函数1.5 卷积神经网络1.5.1 卷积层1.5.2 填充和步幅1.5.3 池化层1.5.4 LeNet 网络1.5.5 AlexNet 网络2 深度学习药物发现案例（一）—— 药物-药物相互作用预测模型图 2. 利用药物关联网络特征和深度神经网络识别药物之间潜在相互作用事件。第四天1 深度学习与药物发现（二）1.1 循环神经网络1.2 消息传递神经网络1.3 图卷积神经网络1.4 图注意力神经网络1.5 图采样和聚合2 深度学习药物发现案例（二）—— 药物靶标相互作用预测模型3 深度学习药物发现案例（三）—— 药物重定位模型第五天1 深度学习与药物发现（三）1.1 注意力机制1.2 自注意力模型1.3 多头自注意力模型1.4 交叉注意力模型1.5 Transformer 模型2 深度学习药物发现案例（四）—— 药物-药物相互作用预测模型3 深度学习药物发现案例（五）—— 药物靶标结合亲和力预测模型图 4.将药物和蛋白质信息关联起来的带有注意力区块的AttentionDTA模型NO.3 蛋白晶体结构解析内容可向下滑动第一天蛋白质结晶前准备课程介绍和蛋白质结构功能基本介绍提纯蛋白质,确定浓度、pH值、缓冲液等条件,控制蛋白质稳定性等。1、目的蛋白质信息检索与调查- 利用生物信息学工具搜集目标蛋白质的基因序列、结构域、同源蛋白质的信息- 分析目标蛋白质的理化性质,如分子量、等电点、聚合程度、稳定性等2、质粒制备- 设计引物,克隆目标基因到表达载体- 转化表达宿主,提取重组质粒- 质粒测序等验证目标基因插入3、蛋白质纯化- 选择合适的诱导表达等条件,表达可溶性或不溶性重组蛋白- 裂解菌体,释放重组蛋白质- 蛋白质纯化:亲和层析、离子交换层析、凝胶过滤等层析技术的原理和实践等4、蛋白质不表达和包涵体问题- 分析不表达的原因,优化诱导条件- 改进溶解缓冲液条件,提高蛋白从包涵体中释放5、蛋白质活性鉴定- 进行Western Blot或酶活性实验验证蛋白质活性6、蛋白质结晶前分析- 测定蛋白质的纯度、聚合状态、稳定性等- 优化缓冲液条件,调整蛋白质到适宜的pH和离子浓度等第二天蛋白质结晶与衍射数据收集利用协同结晶筛选获得蛋白质结晶,在同步辐射光源下收集衍射数据。1、蛋白质结晶- 蛋白质结晶的基本原理- 蛋白质结晶的影响因素- 蛋白质结晶的基本方法- 结晶条件筛选策略- 结晶条件筛选策略- 没有晶体或者改善晶体质量的策略- 晶体后处理- 晶体冻存的基本原理和策略2、SSRF（同步辐射光源）的介绍- SSRF简介- SSRF的光源优势- SSRF的实验站介绍3、蛋白质晶体衍射数据收集- X射线结晶学基本原理- 晶体探针和晶体定位- 晶体测试和优化- 衍射数据收集参数设定和收集策略- 衍射数据处理和分析第三天蛋白质晶体结构解析软件安装安装相关计算机程序,如Phenix, XDS, Pymol等用于后续的数据处理与模型建立。1、下载和安装简要介绍2、蛋白质晶体结构解析软件安装- CCP4安装- Phenix安装- Coot安装- PyMol安装- 其他结构解析支持软件安装依次介绍CCP4、Phenix、Coot、PyMol等主要的结构解析软件的下载和安装方法。也可以介绍一些结构解析中需要的其他软件工具的安装。Index、integrate与scale & merge等软件使用和介绍利用软件index及integrate衍射点,scale& merge等处理衍射数据以校正强度。 1、晶体结构学基础知识- 晶体学中的衍射理论基础- 布拉格定律和倒易空间- 晶体的对称性2、蛋白质晶体结构解析流程- 蛋白质的表达与纯化- 蛋白质的结晶- X射线晶体学数据收集- 晶体结构解析流程概述3、Index和integrate- Indexing的目的和原理- Integration的目的和过程4、Scale & merge- Scale & merge的目的——校正数据- Scale& merge常用方法5、使用Scala/XSCALE/Aimless等进行Scale & merge- Scala/XSCALE/Aimless等软件介绍- Scala/XSCALE/Aimless进行数据scale& merge的步骤6、使用HKL2000进行index、integrate和scale & merge- HKL2000软件介绍- 使用HKL2000进行indexing- 使用HKL2000进行integration- 使用HKL2000进行scaling & merge第四天相位解析、电子密度重构、分子结构模型构建修正和优化与结构提交利用直接法/分子置换法/M(S)AD/M(S)IR 等等相位解析方法确定蛋白质框架,手动模型构建余下结构,进行修正和优化后达到标准后提交蛋白质坐标库。1、直接法/分子置换法/M(S)AD/M(S)IR 等方法解析相位（1）直接法/分子置换法/M(S)AD/M(S)IR等的基本原理(2) 直接法/分子置换法/M(S)AD/M(S)IR等的目的(3) 常用的软件介绍(4) 直接法/分子置换法/M(S)AD/M(S)IR等的具体操作步骤2．电子密度修饰：（1）电子密度修饰的基本原理：（2）电子密度修饰的目的（3）电子密度修饰的常用软件介绍（4）电子密度修饰的具体操作步骤3．电子密度重构（1）电子密度重构的目的和基本原理（2）电子密度重构的操作4、蛋白质晶体结构模型构建(1) 蛋白质序列比对确定构建起始模型(2) 主链构建方法(3) 侧链构建方法(4) 构建完成后的模型检查5、蛋白质晶体结构修正与优化(1) 能量最小化原理 (2) 模拟退火原理(3) 分子动力学模拟原理(4) 优化过程中的评估标准（5）结构修正常用软件介绍（6）结构修证的具体操作步骤6、蛋白质晶体结构验证（1）结构验证的目的和基本原理(2) Ramachandran图分析(3) 各类键长和键角分布(4) 密接点分析(5) B因子分布(6) 电子密度匹配度评价（7）各种指标与统计数据7、蛋白质晶体结构提交到PDB(1) PDB数据提交要求(2) 各项验证确认无误后压缩需提交文件(3) 在PDB网站提交表单,上传文件,等待审核结果，回复信息第五天蛋白质晶体结构展示与分析、结构与功能的关系5.1 利用Pymol等软件分析并展示蛋白质的二级结构、三级结构,活性口袋等结构信息。1、pdb格式文件简介- pdb文件概述:包含蛋白质晶体学数据的标准格式- 原子坐标:记录每个原子的xyz坐标- 温度因子:记录每个原子的热运动参数- 二级结构:记录α螺旋和β片层的位置- 结构注解:记录配体、酶活性中心等重要结构信息2、PyMOL制作蛋白质晶体结构图 - PyMOL简介:流行的分子可视化软件- 加载pdb文件- 显示蛋白质链、α螺旋和β片层- 调整视角、变色和放大关键结构- 导出高质量图像3、使用PyMOL制作蛋白质配体结合位点信息- 识别蛋白质与配体的相互作用- 突出显示配体结合位点残基- 在结合位点生成表面模型- 制作配体结合位点的特写图4、使用PyMOL调查蛋白质的温度因子B-factors- 显示温度因子putty图- 分析柔性域和稳定域- 与酶活性中心和功能位点的关系5、使用PyMOL重叠对比不同的蛋白质晶体结构- 载入不同状态的pdb文件- 重叠对齐蛋白质结构- 比较构象变化,如酶动力学过程中的不同中间状态6、使用PyMOL显示蛋白质晶体结构中配体的电子密度图- 加载包含配体密度的pdb文件- 显示2Fo-Fc 和 Fo-Fc电子密度图- 检查配体与电子密度的匹配程度- 评估配体定位和取向的准确性7、使用PyMOL结合Chimera实现同步显示非对称单元的蛋白质分子- 在PyMOL中显示蛋白质非对称单元- 在Chimera中同步显示非对称单元- 细节对比不同分子中的相同结构- 分析蛋白质多聚体形成的分子间相互作用5.2 生物大分子结构介绍5.3 结构与功能关系：（1）如何分析结构与功能关系：（2）分析结构的目的：（3）结构与功能关系的研究手段：（4）结构能带来什么？（5）测定结构之后的思路介绍NO.4机器学习代谢组学内容可向下滑动第一天A1 代谢物及代谢组学的发展与应用（1）代谢与生理过程；（2）代谢与疾病；（3）非靶向与靶向代谢组学；（4）空间代谢组学与质谱成像（MSI）；（5）代谢组学与药物和生物标志物；（6）代谢流与机制研究。A2 代谢通路及代谢数据库（1）几种经典代谢通路简介；（2）三大常见代谢物库：HMDB、METLIN 和 KEGG; （3）代谢组学原始数据库：Metabolomics Workbench 和 Metabolights.A3 参考资料推荐A4 代谢组学实验流程简介 A5 色谱、质谱硬件与原理解析（1）色谱分析原理与构造；（2）色谱仪和色谱柱的选择；（3）色谱的流动相：梯度洗脱法；（4）离子源、质量分析器与质量检测器解析；（5）质谱分析原理及动画演示；（6）色谱质谱联用技术（LC-MS）；第二天B1 代谢物样本处理与抽提（1）各种组织、血液和体液等样本的提取流程与注意事项；（2）代谢物抽提流程与注意事项；（3）样本及代谢物的运输与保存问题；B2 LC-MS 数据质控与搜库（1） LC-MS 实验过程中 QC 和 Blank 样本的设置方法；（2） LC-MS 上机过程的数据质控监测和分析；（3）代谢组学上游分析原理——基于 Compound Discoverer 与 Xcms 软件；（4） Xcms 软件数据转换、提峰、峰对齐与搜库；B3 R 软件基础（1） R 和 Rstudio 的安装；（2） Rstudio 的界面配置；（3） R 中的基础运算和统计计算；（4） R 中的包：包，函数与参数的使用；（5） R 语言语法，数据类型与数据结构；（6） R 基础画图；B4 ggplot2 （1） ggplot2 简介（2） ggplot2 的画图哲学；（3） ggplot2 的配色系统；（4） ggplot2 数据挖掘与作图实战；第三天机器学习C1 有监督式机器学习在代谢组学数据处理中的应用（1）人工智能、机器学习、深度学习的关系；（2）回归算法：从线性回归、Logistic 回归与 Cox 回归讲起；（3） PLS-DA 算法：PCA 降维后没有差异的数据还有救吗？（4） VIP score 的意义及选择；（5）分类算法：决策树，随机森林和贝叶斯网络模型；C2 一组代谢组学数据的分类算法实现的 R 演练(1) 数据解读；(2) 演练与操作；C3 无监督式机器学习在代谢组学数据处理中的应用（1）大数据处理中的降维；（2） PCA 分析作图；（3）三种常见的聚类分析：K-means、层次分析与 SOM（4）热图和 hcluster 图的 R 语言实现；C4 一组代谢组学数据的降维与聚类分析的 R 演练(1) 数据解析；(2) 演练与操作；第四天D1 在线代谢组分析网页 Metaboanalyst 操作（1）用 R 将数据清洗成网页需要的格式；（2）独立组、配对组和多组的数据格式问题；（3） Metaboanalyst 中的上游分析（原始数据峰提取、峰对齐与搜库）（4） Metaboanalyst 的 pipeline 以及参数设置和注意事项；（5） Metaboanalyst 的结果查看和导出；（6） Metaboanalyst 的数据编辑；（7）全流程演练与操作。D2 代谢组学数据清洗与 R 语言进阶（1）代谢组学中的 t、fold-change 和响应值；（2）数据清洗流程；（3） R 语言 tidyverse；（4）数据预处理：数据过滤与数据标准化（样本的 Normalization 和代谢物的 Scaling）；（5）代谢组学数据清洗演练；第五天E1 文献数据分析部分复现（1 篇）（1）文献深度解读；（2）实操：从原始数据下载到图片复现；（3）学员实操。E2 机器学习与代谢组学顶刊解读（3 篇）；（1） Signal Transduction and Targeted Therapy 一篇有关饥饿对不同脑区代谢组学影响变化的小鼠脑组织代谢图谱类的文献；(数据库型)（2） Cell 一篇代谢组学孕妇全程血液代谢组学分析得出对孕周和孕产期预测的代谢标志物的文献；(生物标志物型) （3） Nature 一篇对胰腺癌患者肠道菌群的代谢组学分析找到可以提高化疗效果的代谢物的文献。(机制研究型）部分模型案例图片NO.5深度学习基因组学内容可向下滑动第一天理论部分深度学习算法介绍1.有监督学习的神经网络算法1.1全连接深度神经网络DNN在基因组学中的应用举例1.2卷积神经网络CNN在基因组学中的应用举例1.3循环神经网络RNN在基因组学中的应用举例1.4图卷积神经网络GCN在基因组学中的应用举例2.无监督的神经网络算法2.1自动编码器AE在基因组学中的应用举例2.2生成对抗网络GAN在基因组学中的应用举例实操内容1.Linux操作系统1.1常用的Linux命令1.2 Vim编辑器1.3基因组数据文件管理, 修改文件权限1.4查看探索基因组区域2.Python语言基础2.1.Python包安装和环境搭建2.2.常见的数据结构和数据类型第二天基因组学基础1. 基因组数据库2. 表观基因组3. 转录基因组4.蛋白质组5. 功能基因组实操内容基因组常用深度学习框架1. 安装并介绍深度学习工具包tensorflow, keras，pytorch 2. 在工具包中识别深度学习模型要素2.1.数据表示2.2.张量运算2.3.神经网络中的“层”2.4.由层构成的模型2.5.损失函数与优化器2.6.数据集分割2.7.过拟合与欠拟合3.基因组数据处理3.1安装并使用keras_dna处理各种基因序列数据如BED、 GFF、GTF、BIGWIG、BEDGRAPH、WIG等3.2使用keras_dna设计深度学习模型3.3使用keras_dna分割训练集、测试集3.4使用keras_dna选取特定染色体的基因序列等第三天微生物组常用分析方法（实操）1. 微生物丰度分析2. 转录组丰度分析3. 进化树分析4. 降维分析第四天理论部分深度学习在识别拷贝数变异DeepCNV1. SNP微阵列中预测拷贝数变异CNV，DeepCNV2. RNA-Seq中预测premiRNA，dnnMiRPre实操内容1. 复现DeepCNV利用SNP微阵列联合图像分析识别拷贝数变异复现循环神经网络RNN工具 dnnMiRPre，从RNA-Seq中预测premiRNA第五天理论部分深度学习在识别及疾病表型及生物标志物上的应用1. 从基因表达数据中识别乳腺癌分型的深度学习工具DeepType实操内容1.复现DeepType，从METABRIC乳腺癌数据中区分乳腺癌亚型第六天理论部分深度学习在预测药物反应机制上的应用1. 联合肿瘤基因标记及药物分子结构预测药物反应机制的深度学习工具SWnet实操内容1. 预处理药物分子结构信息2. 计算药物相似性3. 在不同数据集上构建self-attention SWnet4. 评估self-attention SWnet5. 构建多任务的SWnet6. 构建单层SWnet7. 构建带权值层的SWnet部分模型案例图片NO.6机器学习微生物组学内容可向下滑动第一天1.微生物学基础知识回顾2.机器学习基本概念介绍 a.什么是机器学习 b.监督学习、无监督学习 c.常用机器学习模型介绍3.混淆矩阵4.ROC曲线第二天R语言简介与实操1. R语言概述2. R studio软件与R包安装3. R语言语法及数据类型4. 条件语句和循环Linux实操1. Linux操作系统2. Linux操作系统的安装与设置3. 网络配置与服务进程管理4. Linux的远程登录管理5. 常用的Linux命令6. 在Linux下获取基因数据7. Shell script与Vim编辑器下·第三天微生物组常用分析方法（实操）1. 微生物丰度分析2. 转录组丰度分析3. 进化树分析4. 降维分析第四天机器学习在微生物组学中的应用案例分享1. 疾病预测应用：利用机器学习基于微生物组学数据预测疾病状态2. 肠道菌群研究：机器学习研究饮食对肠道微生物的影响第五天机器学习模型训练和分析（实操）1. 加载数据及数据归一化2. 构建训练模型（GLM, RF, SVM）3. 模型参数优化4. 模型错误率曲线绘制5. 混淆矩阵计算6. 重要特征筛选7. 模型验证，ROC曲线绘制利用模型进行预测利用机器学习基于微生物组学数据预测宿主表型1. 加载数据2. 数据归一化3. OUT特征处理4. 机器学习模型构建（RF, KNN, SVM, Lasso等多种机器学习方法）5. 绘制ROC 曲线，比较不同机器学习模型模型性能评估利用机器学习基于临床特征和肠道菌群预测疾病风险1. 加载数据2. 机器学习模型构建（RF, gbm, SVM等等）3. 交叉验证4.模型性能评估部分模型案例图片NO.7CRISPR-Cas9基因编辑技术内容可向下滑动第一天基因编辑简介1.基因编辑基本概念介绍2.使用单基因遗传病数据库 (实操)3. 5524种单基因遗传病的发病率及对应基因4.使用网站查找突变位点周围碱基选择基因编辑工具(实操)5.基因编辑历史6.TALEN7.Zinc finger8.Base editor9.Prime editor第二天1.如何选择正确Cas9蛋白类型2.单碱基编辑(base editor) gRNA设计和软件(实操)3.sgRNA修饰4.手动设计PegRNA的八个要点(实操)5.七种PegRNA辅助设计软件6. 查找不同PAM的Cas9的碱基序列7. 设计Cas9 mRNA及其体外转录所需的DNA8. 如何设计深度测序(NSG)所需的引物9. 如何准备深度测序(NGS)所需样品10. Sleeping beauty11. PiggyBac12. 肽核酸Peptide nucleic acids13. 外显子跳读Exon skipping14. 介绍CRISPR knock out库 (GeCKO v2.0)15. 简单介绍Base editing在微生物中的应用第三天1.AAV递送(组织靶向)2.脂质体递送3.核糖核蛋白递送4.高分子递送5.Viral like particles递送6.外泌体递送7.无机纳米粒递送8. 电转9. 超声10. 显微注射11. PAM高通量筛选的6篇文章12. Cas9-DNA-gRNA晶体结构13.单基因遗传病细胞订制14.单基因遗传病动物模型定制第四天1.动物模型2.质粒3.分子克隆基础4.AAV设计(实操)5.Base editing文章分析6.Prime editing 相关的140篇文章概览7.如何提高Prime editing效率8.Prime editing 相关的87篇研究文章概览9.如何提高Prime editing效率10.NGS引物设计11.NGS测序结果分析第五天1.基因编辑已经批准的药物2. 临床试验3.主要公司、科学家和专利4.副作用和退市的产品5.FDA政策6.CRISPR在诊断中的应用7.CRISPR library8.CRISPR与单细胞测序9. CRISPR与表观遗传学10. CIRPSR在植物学中的应用11. CRISPR在微生物学中的应用12. NGS类型及原理13. 3个基因序列的数据库(涵植物基因)14. 2023年BE和PE领域热点15. 设计课题与评价(实操)NO.8 冷冻电镜结构解析内容可向下滑动第一天一、冷冻电镜的起源和发展1，冷冻电镜的起源2，冷冻电镜原理3，相机的发展4，冷冻制样方法的发展二、冷冻电镜的应用概述1，单颗粒冷冻电镜（single particle Cryo-EM analysis）2，原位冷冻电镜（cryo-ET）3，微晶电子衍射（Micro-ED）第二天三、冷冻电镜在结构解析中的应用1，结构解析方法概述2，单颗粒冷冻电镜方法的应用3，单颗粒冷冻电镜在结构解析和药物开发中的应用四、冷冻透射电镜的构造和成像原理1，基本概念2，冷冻透射电镜的构造3，成像原理简介第三天五、冷冻电镜的样品处理1，冷冻电镜样品概述2，冷冻电镜样品类型和处理方法3，样品制备方法及优缺点4，冷冻载网的选择（载网类型及特点）六、冷冻电镜高分辨数据收集1，数据收集软件2，数据收集流程和注意事项3，数据分析和评价第四天七、冷冻电镜数据处理1，数据处理基本原理软件介绍2，数据处理过程基本概念3，软件使用，数据处理流程及注意事项4，数据处理软件的注意事项和软件系统的相互转换第五天八、电镜map图的展示和处理1， Chimera，ChimeraX 软件下载安装2，软件基本使用规则3， Chimera在冷冻电镜数据处理中的应用九、模型搭建和结构修正1， Coot，Phenix 软件的下载安装2，模型搭建基本流程3，结构修正及评价讲师介绍01.「CADD计算机辅助药物设计」主讲老师来自国内高校、中科院等单位，老师主要擅长深度学习、机器学习、药物虚拟筛选、计算机辅助药物设计、人工智能药物发现、分子对接、分子动力学等方面的研究02.「AIDD人工智能药物发现与设计」授课老师余老师，有十余年的计算机算法研究和程序设计经验。研究方向涉及生物信息学，深度学习，药物靶标识别，药物不良反应等。参与了国自然基金2项，主持了省厅级科研项目3项。一作身份发表SCI论文数篇，包括BMC Bioinformatics, Journal of Biomedical Informatics, International Journal of Molecular Sciences等知名期刊。03.「蛋白晶体结构解析」范教授毕业于中国科学院生物物理研究所，师从著名结构生物学家王院士，具有美国耶鲁大学留学六年多的背景，为独立PI。研究方向是结构生物学和免疫学；除了深钻结构生物学和免疫学，还在积极参与和推动结构生物学的教育工作，应邀在多所高校或者科研研究所开展结构生物学课程讲授，在多种国际期刊上发表论文30余篇，均为SCI，作为第一作者或通讯作者4篇一区，包括国际顶尖杂志PNAS两篇，一区top2篇等。并承担国家自然科学基金面上项目等；作为国际著名学术杂志Nature Communication和Journal of Virology, Structure等的审稿人。04.「CRISPR-Cas9基因编辑技术」主讲老师来自加州大学生物医学工程专业博士，曾在麻省理工和哈佛大学从事基因编辑研究，在耶鲁大学从事基因递送工作。文章发表在Nature Biomedical Engineering, Nature Communications等杂志上。曾在天使投资机构工作，主要投资基因编辑、单细胞测序、AI制药等方向的创业公司。05.「机器学习代谢组学」主讲老师来自985高校神经科学博士，主要利用代谢组学、转录组学和分子生物学等技术研究神经内科慢性病的发病机制和生物标志物。擅长高效液相色谱-质谱联用（LC-MS）技术进行非靶向和靶向代谢组学从样本制备到数据分析的全流程研究，以及多组学大数据的生物信息学整合分析。5年内在J Clin Invest, EBioMedicine, Cell Death Dis, Cell Death Discov, Nanotoxicology等杂志发表SCI论文10篇。06.「机器学习微生物组学」主讲老师来自生物科技公司核心高管，曾在麻省理工学院、加州大学旧金山分校等机构工作，过去5年科研工作发表于Cell, PNAS等杂志07.「深度学习基因组学」主讲老师刘老师，生物信息学博士，有十余年的测序数据分析经验。研究领域涉及人工智能、自然语言处理、功能基因组学、转录组学、miRNA及靶基因网络分析，单细胞测序数据分析，基因调控网络时序分析，蛋白质互作网络分析，多组学联合分析等。主持省自然科学基金等项目4项，发表SCI论文23篇，论著一部。08.「冷冻电镜结构解析」来自中科院所毕业的结构生物学博士，主要利用单颗粒冷冻电镜手段研究作用于药物靶点的的分子机制和基于结构的药物设计。目前主要研究方向为膜蛋白及病毒相关蛋白的结构研究。5年内在国内外顶尖杂志包括Nature,NatureCommunications，Cell Research 等杂志发表文章数篇。授课时间及地点CADD计算机辅助药物设计2024.04.16-2024.04.19晚上授课（晚19：00—晚22：00）2024.04.20-2024.04.21全天授课（上午9：00-11：30下午13：30-17：00）2024.04.22-2024.04.25晚上授课（晚19：00—晚22：00）2024.04.27全天授课（上午9：00-11：30下午13：30-17：00）腾讯会议直播形式机器学习代谢组学2024.04.13-2024.04.14全天授课（上午9：00-11：30下午13：30-17：00）2024.04.16-2024.04.19晚上授课（晚19：00—晚22：00）2024.04.20全天授课（上午9：00-11：30下午13：30-17：00）腾讯会议直播形式深度学习基因组学2024.04.15-2024.04.18晚上授课（晚19：00—晚22：00）2024.04.20-2024.04.21全天授课（上午9：00-11：30下午13：30-17：00）2024.04.23-2024.04.26晚上授课（晚19：00—晚22：00）腾讯会议直播形式机器学习微生物2024.04.08-2024.04.11晚上授课（晚19：00—晚22：00）2024.04.13-2024.04.14全天授课（上午9：00-11：30下午13：30-17：00）2024.04.16-2024.04.17晚上授课（晚19：00—晚22：00）腾讯会议直播形式CRISPR-Cas9基因编辑技术2024.04.15-2024.04.18晚上授课（晚19：00—晚22：00）2024.04.20-2024.04.21全天授课（上午9：00-11：30下午13：30-17：00）2024.04.23-2024.04.24晚上授课（晚19：00—晚22：00）腾讯会议直播形式蛋白晶体结构解析2024.04.08-2024.04.11晚上授课（晚18：30—晚21：30）2024.04.13-2024.04.14全天授课（上午9：00-11：30下午14：00-17：30）2024.04.16-2024.04.17晚上授课（晚18：30—晚21：30）腾讯会议直播形式AIDD人工智能药物发现与设计2024.04.08-2024.04.11晚上授课（晚19：00—晚22：00）2024.04.13-2024.04.14全天授课（上午9：00-11：30下午13：30-17：00）2024.04.16-2024.04.17晚上授课（晚19：00—晚22：00）腾讯会议直播形式冷冻电镜结构解析2024.04.15-17-18-19晚上授课（晚19：00—晚22：00）2024.04.20-2024.04.21全天授课（上午9：00-11：30下午13：30-17：00）2024.04.22号、24号晚上授课（晚19：00—晚22：00）腾讯会议直播形式报名费用及福利CADD计算机辅助药物设计；AIDD人工智能药物发现；蛋白晶体结构解析；冷冻电镜结构解析；深度学习基因组学；每班公费价：5880每班自费价：5480机器学习代谢组学；机器学习微生物组学；CRISPR-Cas9基因编辑；（机器学习生物医学免费赠送）每班公费价：5380每班自费价：5080优惠一：两班同报9880元另外赠送一个学习名额（可任意选择一个课程赠送）优惠二：三班同报13880元另外赠送一个学习名额（可任意选择一个课程赠送）优惠三：四班同报17880元另外赠送两个学习名额（可任意选择两个课程赠送）优惠四：全部报名学习25880元（一年内可免费参加我司举办的任何课程，不限课程及次数）福利：报名缴费后即可获得往期全套视频回放及PPT资料提供预习结业证书：参加培训并通过考试的学员，可以获得工业和信息化部工业文化发展中心颁发的“工业强国建设素质素养提升尚工行动”岗位能力适应评测证书。该证书可在中心官网查询，可作为能力评价，考核和任职的重要依据。评测证书查询网址：www.miit-icdc.org学员反馈官方联系人/扫码咨询详情联系人：孙老师电话：18638251323（同V）声明：此内容由第三方提供、授权发布或者转载自第三方；本内容一切版权及连带责任由第三方负责，与本公众号无关。据此内容作出的任何判断，后果自负。

寡核苷酸

2024-03-21

·BioSpace

Exscientia Business and Financial Update for the Full Year 2023

OXFORD, U.K.--(BUSINESS WIRE)-- Exscientia plc (Nasdaq: EXAI) Recent advancements in the Company’s pipeline, collaborations and operations, as well as financial results for the fourth quarter and full year 2023, are summarised below. Exscientia will host a conference call Thursday, March 21 at 12:30 p.m. GMT / 8:30 a.m. EDT. “In 2023, we significantly expanded our technological capabilities with the opening of our automation facility, concentrated our internal research on high value oncology targets and steadily progressed multiple new and existing programmes with our large pharma partners,” said Dr. David Hallett, interim Chief Executive Officer and Chief Scientific Officer of Exscientia. “In 2024 we will continue to advance our pipeline programmes through clinical development. This includes GTAEXS617 (‘617), our CDK7 inhibitor currently in a Phase 1/2 clinical trial; LSD1, for which we anticipate initiating a Phase 1 patient study this year; and MALT1, which continues to progress through IND-enabling studies. In addition, we look forward to advancing our partnered programmes through potential milestones, while simultaneously inking new partnerships and expanding our technological capabilities to further cement our leadership in AI-powered drug design and development.” Key Business Highlights Pipeline Patient enrolment is continuing to progress for the dose escalation phase of ELUCIDATE, the adaptive Phase 1/2 trial studying ‘617, a potential best-in-class CDK7 inhibitor, for the treatment of advanced solid tumours. The Company expects to announce a transition from the dose escalation to dose expansion phase in the second half of 2024 EXS74539 (‘539), a highly differentiated LSD1 inhibitor, is continuing through IND/CTA enabling studies, and the Company anticipates initiating a Phase 1 clinical trial in cancer patients in 2024. ‘539 was precision designed by Exscientia to optimise the therapeutic window by combining reversible binding, brain penetration and a short half-life EXS4318 (‘4318), a PKC-theta inhibitor designed by Exscientia and in-licensed by Bristol Myers Squibb for immunology and inflammation indications, is continuing to advance in a Phase 1 study. Exscientia retains rights to receive milestone payments and potential royalties for ‘4318 EXS73565 (‘565), the Company’s differentiated MALT1 inhibitor, precision designed with selectivity over UGT1A1, is also advancing through IND/CTA-enabling studies. The Company expects to provide further updates in 1H 2024 Collaborations & Partnerships In December 2023, the Company announced the addition of an existing Exscientia programme to its Sanofi collaboration Preliminary data show these compounds have good potency and selectivity towards the target and differentiated molecular properties The addition of this discovery stage programme, identified and initially advanced by Exscientia, includes financial milestones over and above programmes initiated as part of the collaboration agreement Exscientia received a $4 million upfront payment in the first quarter of 2024 for this programme. In addition to the upfront payment, the Company is eligible to receive up to $41 million in preclinical milestone payments, as well as over $300 million in further milestone payments if the programme achieves all milestones under the agreement Exscientia is also eligible to receive tiered royalties on sales of products that may be developed from this programme In October 2023, Exscientia also announced the achievement of the first discovery stage milestone from a different programme within the Sanofi collaboration Exscientia’s collaboration with Merck KGaA, Darmstadt, Germany, initially announced in September 2023, is ongoing, leveraging Exscientia’s precision design capabilities to design and discover novel small molecule drug candidates across oncology and immunology In December 2023, Exscientia announced that it had received a $2.3 million grant from Open Philanthropy to drive research on the host-interferon response as a pathway to treating and preventing pandemic influenza Focus aligns with Exscientia’s ongoing commitment to pandemic preparedness, initiated with support from the Bill & Melinda Gates Foundation in 2021 DSP-2342, a dual 5-HT2A/5-HT7 antagonist designed by Exscientia for Sumitomo Pharma for the treatment of psychiatric diseases, entered Phase I studies in April 2023 DSP-2342 is the third compound designed by Exscientia that Sumitomo Pharma has advanced into the clinic Platform Exscientia opened its novel automated discovery laboratory in mid-2023. This facility has the capability to integrate AI design methods with chemical synthesis and biological testing. This integration has the potential to bring significant further efficiencies to the drug design process and enhance the rate of learning of Exscientia’s generative design platform Exscientia continues to drive the development of its functional precision medicine platform with new studies and collaborations In January 2024, Exscientia launched EXCYTE-2, a multi-centre observational study to evaluate the precision medicine platform in patients with acute myeloid leukaemia. This study has the potential to support the ongoing development of ‘539, Exscientia’s LSD1 inhibitor In July 2023, Exscientia launched EXCYTE-1, a first-of-its-kind prospective observational study in ovarian cancer. This ongoing trial is the first trial evaluating the potential of Exscientia’s functional precision medicine platform in a solid tumour indication The collaboration with Charité – Universitätsmedizin Berlin, announced in March 2023, evaluating Exscientia’s precision medicine platform as a tool to predict response in haematological cancers is also ongoing. This collaboration highlights the scalability of the primary patient tissue platform as Charité will be able to utilise cloud-based AI to analyse samples imaged onsite In the fourth quarter of 2023, Exscientia deployed large language models (LLMs) internally for the use of target hypothesis generation. These LLMs, based on both public and proprietary datasets, have been integrated with the Company’s existing target identification infrastructure with the aim to further enhance the productivity of Exscientia’s discovery platform Investor Call and Webcast Information Exscientia will host a conference call on Thursday, March 21 at 12:30 p.m. GMT / 8:30 a.m. EDT. A webcast of the live call can be accessed by visiting the “Investors and Media” section of the Company’s website at investors.exscientia.ai. Alternatively, the live conference call can be accessed by dialling +1 (888) 596 4144 (U.S.), +44 800 358 0970 (U.K.), +1 (646) 968 2525 (International) and entering the conference ID: 8333895. A replay will be available for 90 days under “Events and Presentations” in the “Investors and Media” section of the Exscientia website. Fourth Quarter and Full Year 2023 Financial Results For the convenience of the reader, the Company has translated pound sterling amounts to U.S. dollars at the rate of £1.000 to $1.2743, which was the noon buying rate of the Federal Reserve Bank of New York on December 29, 2023. Revenue: Revenue for the three and twelve months ended December 31, 2023 was $3.1 million and $25.6 million respectively, compared to $8.7 million and $34.7 million for the three and twelve months ended December 31, 2022. The decrease in revenue quarter on quarter and year on year is primarily the result of multiple new programmes starting with partners in the second half of 2023 with revenue recognition constrained by the early nature of these programmes, as well as pipeline prioritisation activities. Research and development expenses: R&D expenses for the three and twelve months ended December 31, 2023 were $37.5 million and $163.7 million respectively, as compared to $45.4 million and $164.2 million for the same period ended December 31, 2022. The current quarter over quarter decrease in research and development expenses was primarily due to pipeline prioritisation activities and cost efficiency savings, including achieving faster cycle times and lower outsourcing costs. General and administrative expenses: G&A expenses for the three and twelve months ended December 31, 2023, were $14.8 million, or 24.7% of total operating expenses, and $57.8 million, or 22.5% of total operating expenses respectively. For the three and twelve months ended December 31, 2022, G&A expenses increased by $4.3 million and $8.8 million compared to the three and twelve months ended December 31, 2022, primarily associated with increased personnel expenses. Cash inflows: For the full year 2023, Exscientia received $27.4 million in cash inflows from its collaborations as compared to $117.8 million during the full year 2022, when the upfront payment for the Sanofi collaboration was received. Net operating cash flow and cash balance: For the full year ending December 31, 2023, net operating cash outflows were $149.9 million, in comparison to $77.1 million for the full year 2022. Cash, cash equivalents and short term bank deposits as of December 31, 2023 were $462.6 million, as compared to $644.6 million as of December 31, 2022 using the December 29, 2023 constant currency rate. Based on our current operating plans, the Company believes that its existing cash, cash equivalents and short-term bank deposits and anticipated milestones will be sufficient to fund its operations and capital expenditure requirements well into 2026. SELECTED CONSOLIDATED STATEMENT OF OPERATIONS, CONSTANT CURRENCY CONVERSION (unaudited) ($ millions, except per share data, at the rate of £1.000 to $1.2743) Three months ended December 31, Twelve months ended December 31, 2023 2022 2023 2022 Revenue 3.1 8.7 25.6 34.7 Cost of sales (7.6) (12.2) (34.9) (42.4) Research and development expenses (37.5) (45.4) (163.7) (164.2) General and administrative expenses (14.8) (10.5) (57.8) (49.0) Operating expenses (59.9) (68.1) (256.4) (255.6) Foreign exchange gains/(losses) (4.0) (8.0) (2.0) 28.4 Other income 1.4 2.0 8.5 7.3 Operating loss (59.4) (65.4) (224.3) (185.2) Finance income/(expense) 5.2 3.7 19.9 6.9 Share of loss on joint ventures (0.6) (0.0) (2.1) (0.9) Loss before taxation (54.8) (61.7) (206.5) (179.2) Income tax benefit 2.4 11.9 20.5 27.9 Loss for the period (52.4) (49.8) (186.0) (151.3) Net loss per share (0.42) (0.41) (1.50) (1.24) Weighted average shares outstanding (basic and diluted) 125,243,445 122,912,943 124,197,000 122,119,635 SELECTED CONSOLIDATED BALANCE SHEET, CONSTANT CURRENCY CONVERSION (unaudited) ($ millions, except per share data, at the rate of £1.000 to $1.2743) December 31, 2023 December 31, 2022 Cash, cash equivalents & short-term bank deposits 462.6 644.6 Total assets 650.8 827.8 Total equity 453.3 610.2 Total liabilities 197.5 217.6 Total equity and liabilities 650.8 827.8 SELECTED CONSOLIDATED STATEMENT OF CASH FLOWS, CONSTANT CURRENCY CONVERSION (unaudited) ($ millions, except per share data, at the rate of £1.000 to $1.2743) Twelve months ended December 31, 2023 2022 Net cash outflows from operating activities (149.9) (77.1) Net cash flows used in investing activities (27.4) (156.3) Net cash used in financing activities (4.3) (5.1) Net decrease in cash and cash equivalents (181.6) (238.5) Exchange (loss)/gain on cash and cash equivalents (3.3) 37.7 Net decrease in cash, cash equivalents and short-term bank deposits* (181.9) (71.8) * Includes both increases in short term bank deposits and foreign exchange (losses)/gains on cash and cash equivalents About Exscientia Exscientia is an AI-driven precision medicine company committed to discovering, designing, and developing the best possible drugs in the fastest and most effective manner. Exscientia developed the first-ever functional precision oncology platform to successfully guide treatment selection and improve patient outcomes in a prospective interventional clinical study, as well as to progress AI-designed small molecules into the clinical setting. Our internal pipeline is focused on leveraging our precision medicine platform in oncology, while our partnered pipeline broadens our approach to other therapeutic areas. By pioneering a new approach to medicine creation, we believe the best ideas of science can rapidly become the best medicines for patients. For more information visit us on or follow us on LinkedIn @ex-scientia and X @exscientiaAI. Forward-Looking Statements This press release contains forward-looking statements as that term is defined in the Private Securities Litigation Reform Act of 1995. Words such as “anticipates,” "believes," “expects,” "intends," "projects," and "future" or similar expressions are intended to identify forward-looking statements. These forward-looking statements include statements with regard to Exscientia’s expectations regarding: the initiation, timing and progress of, and data collected during and reported from, preclinical and clinical trials of the Company’s and its partners’ product candidates and the Company’s precision medicine platform; the performance of Exscientia’s technology and its discovery platform; and the sufficiency of the Company’s existing cash, cash equivalents and short term bank deposits and anticipated milestones to fund its operations into 2026. Such statements are subject to a number of risks, uncertainties and assumptions, including those related to: the initiation, scope and progress of Exscientia’s and its partners’ planned and ongoing preclinical studies and clinical trials and ramifications for the cost thereof; clinical, scientific, regulatory and technical developments; the process of discovering, developing and commercialising product candidates that are safe and effective for use as human therapeutics and the endeavour of building a business around such product candidates; and the Company’s use of its existing cash, cash equivalents and short term bank deposits and achievement of milestones. In light of these risks and uncertainties, and other risks and uncertainties that are described in the Risk Factors section of Exscientia’s Annual Report on Form 20-F for the fiscal year ended December 31, 2023, and in other filings that Exscientia makes with the SEC from time to time (which are available at ), the events and circumstances discussed in such forward-looking statements may not occur, and Exscientia’s actual results could differ materially and adversely from those anticipated or implied thereby. Although Exscientia’s forward-looking statements reflect the good faith judgement of its management, these statements are based only on facts and factors currently known by the Company. As a result, you are cautioned not to rely on these forward-looking statements. Exscientia undertakes no obligation to publicly update or revise any forward-looking statements, whether as a result of new information, future events or otherwise, except as may be required by law.

临床1期引进/卖出财报