Centralized assay datasets for modelling support of small drug discovery organizations

用于小型药物发现组织建模支持的集中化分析数据集

• 批准号: 10474479
• 负责人: SEAN EKINS
• 金额: $ 85.47万
• 依托单位: COLLABORATIONS PHARMACEUTICALS, INC.
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-01-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10474479
• 关键词: Acetylcholinesterase; Acetylcholinesterase Inhibitors; Algorithms; Alzheimer' s Disease; Artificial Intelligence; Back; Bayesian Modeling; Biological; Biological Assay; Biological Testing; CCR5 gene; CXCR4 gene; Cells; Chemistry; Client; Collaborations; Collection; Complement; Complex; Computer software; Consult; Data; Data Discovery; Data Set; Data Sources; Databases; Descriptor; Development; Disease; Disease Pathway; Docking; Drug Design; Drug usage; Employment; Ensure; Event; Fee-for-Service Plans; Foundations; Future; Generations; Growth; HIV; HIV Envelope Protein gp120; Human; In Vitro; Industrialization; Industry Standard; Integrase; Lead; Legal patent; Literature; Machine Learning; Manuals; Marketing; Measurable; Modeling; Molecular; National Institute of Allergy and Infectious Disease; National Institute of General Medical Sciences; Organism; Outcome; Output; Paper; Pathway interactions; Peptide Hydrolases; Pharmaceutical Preparations; Pharmacologic Substance; Phase; Phenotype; Population; Privatization; Process; Production; Property; PubChem; Public Domains; Publications; Publishing; RNA-Directed DNA Polymerase; Rare Diseases; Research; Sales; Service delivery model; Structure; Structure-Activity Relationship; Technology; Testing; Toxic effect; Toxicology; Trademark; United States National Institutes of Health; Validation; Virus; Visualization software; Work; adverse outcome; analog; base; commercialization; consumer product; data curation; data visualization; design; diverse data; drug discovery; improved; in vivo; inhibitor; interest; machine learning algorithm; machine learning model; model building; neglect; novel; outcome prediction; pre-clinical; prospective; prospective test; prototype; public database; screening; software development; technology development; tool

Project Summary Collaborations Pharmaceuticals, Inc. was formed after identifying a need for software to assist academics and smaller companies in curating their data and discovery of new hits or lead optimisation. In the past two years the continued importance of artificial intelligence (AI) is apparent from the explosive growth in number of these companies and the increasing number of multi-million dollar deals with pharma using Machine Learning (ML) to assist in drug discovery. There is a heavy focus by these companies on the drug discovery modeling aspect but there is a continued unmet need and bottleneck in the curation of quality in vitro and in vivo data ADME/Tox data for ML as well as prospective testing to validate the technologies. In Phase I, we developed a prototype of Assay CentralÒ software and used this with a wide variety of structure activity data from sources both public and private, formatted and unformatted, with ~14 collaborators working on neglected, rare or common disease targets as well as used it for our internal drug discovery projects. In Phase I we also created error checking and correction software. We also built and validated Bayesian models with the datasets that were collected and cleaned. And, in addition, we developed new data visualization tools. The software can be used to create selections of these models for sharing with collaborators as needed and for scoring new molecules and visualizing the multiple outputs in various formats. In Phase II, we have developed Assay CentralÒ into a production tool which is easy to deploy, built on industry standard technologies, provided graphical display of models and information on model applicability. Importantly, we identified that customers wanted us to provide them with the results! We developed our fee-for-service consulting services model using Assay CentralÒ to solve their problems and this has expanded our revenues annually. In Phase II we evaluated additional ML algorithms and molecular descriptors with manually curated datasets as well as compared algorithms across over 5000 auto-curated datasets from ChEMBL. This illustrated the utility of access to multiple algorithms and how the Bayesian algorithm was generally comparable to these other ML algorithms. This also motivated us to develop new software to integrate these algorithms. We have also explored finding rare disease datasets and applying our data curation and ML approach to them. With these and additional collaborations, as well as internal projects on Alzheimer’s disease (through a NIH NIGMS supplement) we have been able to repurpose already approved drugs for several targets for this and other diseases. For multiple projects we have performed several rounds of model building and fed data back into the models to enable improved predictions. Finally, we have developed prototype tools to enable us to develop automated molecule designs, assess their synthesizability and perform retrosynthetic analysis. These combined efforts dramatically increased the number of projects we were able to work on (and ultimately publish to raise our visibility), created new spin off products as collections of models (MegaTransÒ, MegaToxÒ and MegaPredictÒ), molecule related IP, and generated employment. In Phase IIB we now propose a focus on steps to aid commercialization and further development of these technologies. We have identified that developing auto-curation software for dealing with complex biological data in unstructured databases will be a competitive advantage. We have also recognized that for many diseases we can have a complete or near complete collection of targets which may enable us to understand how a molecule may interfere with biological pathways from structure alone and this can be applied to complex diseases and “adverse outcome pathways” in toxicology. We also propose integrating state of the art multi-objective generative models for molecule design into our Assay Central computational software in order to complement our analog generation and retrosynthesis tools created in Phase II and aid in molecule optimization. We will validate this capability using some of the hit molecules identified in Phase II for different targets including human acetylcholinesterase. Assay Central would then have a full suite of integrated capabilities from data curation through to molecule design and retrosynthetic analysis and will enable us to attract larger deals with companies.

项目摘要 合作Pharmaceuticals，Inc。是在确定需要协助学者和的软件之后成立的 较小的公司策划数据并发现新的热门单曲或铅优化。在过去的两年中 从这些数量的爆炸性增长中，人工智能（AI）的持续重要性显而易见 公司以及使用机器学习（ML）与制药公司达成的数百万美元交易数量的越来越多 协助药物发现。这些公司对药物发现建模方面有很大的重点，但是 在体外质量和体内数据质量策划中，持续未满足的需求和瓶颈 用于ML以及前瞻性测试以验证技术。在第一阶段，我们开发了一个测定的原型 Centralò软件，并将其与来自公共和私有来源的各种结构活动数据一起使用， 格式化和未格式化，约有14位合作者将被忽视的，罕见或常见的疾病靶标作为 并将其用于我们的内部药物发现项目。在第一阶段，我们还创建了错误检查和校正 软件。我们还使用收集和清洁的数据集建立了和验证的贝叶斯模型。和， 此外，我们开发了新的数据可视化工具。该软件可用于创建这些选择 根据需要与合作者共享的模型，并为新分子评分和可视化多个分子 以各种格式输出。在第二阶段，我们将AssayCentralò开发为一种很容易的生产工具 部署以行业标准技术为基础的部署，提供了模型的图形显示和模型的信息 适用性。重要的是，我们确定客户希望我们为他们提供结果！我们开发了 我们使用AssayCentralò来解决问题的服务费用咨询服务模型 每年扩大我们的揭示。在第二阶段，我们评估了其他ML算法和分子描述符 通过手动策划的数据集以及从5000个自动策划数据集中的算法进行比较 chembl。这说明了访问多种算法以及贝叶斯算法的实用性 通常与这些其他ML算法相当。这也使我们融合了开发新软件以集成 这些算法。我们还探索了寻找稀有疾病数据集并应用我们的数据策划和ML 对他们的方法。这些以及其他合作以及有关阿尔茨海默氏病的内部项目 （通过NIH NIGMS补充）我们已经能够复制已经批准的几个目标的药物 对于多个项目，我们进行了几轮模型构建并喂养 数据回到模型中，以改进预测。最后，我们开发了原型工具来启用 我们要开发自动分子设计，评估其合成性并执行递归合成分析。 这些综合努力极大地增加了我们能够从事的项目数量（并最终 出版以提高我们的知名度），创建了新的旋转产品作为模型集合（Megatransò，Megatoxò 和Megapredictò），与分子相关的IP和产生的就业。在IIB期中，我们现在建议专注于 帮助商业化和进一步发展这些技术的步骤。我们已经确定 开发用于处理非结构化数据库中复杂生物学数据的自动策略软件将是一个 竞争优势。我们还认识到，对于许多疾病，我们可以拥有一个完整或附近 完全收集目标，这可能使我们能够了解分子如何干扰生物学 单独的结构途径，可以应用于复杂疾病和“不利结果途径” 毒理学。我们还提出了整合分子设计多目标通用模型的整合状态 进入我们的测定中央计算软件，以补充我们的模拟生成和逆合合成 在第二阶段创建的工具并有助于分子优化。我们将使用一些命中来验证此功能 分子在II期中鉴定出包括人乙酰胆碱酯酶在内的不同靶标。测定中央会 然后具有从数据策划到分子设计和逆转录机的完整集成功能 分析并将使我们能够与公司吸引更大的交易。

项目成果

Multiple Machine Learning Comparisons of HIV Cell-based and Reverse Transcriptase Data Sets.

• DOI: 10.1021/acs.molpharmaceut.8b01297
• 发表时间: 2019-04-01
• 期刊: Molecular pharmaceutics
• 影响因子: 4.9
• 作者: Zorn KM;Lane TR;Russo DP;Clark AM;Makarov V;Ekins S
• 通讯作者: Ekins S

Validation of Acetylcholinesterase Inhibition Machine Learning Models for Multiple Species.

• DOI: 10.1021/acs.chemrestox.2c00283
• 发表时间: 2023-02-20
• 期刊: CHEMICAL RESEARCH IN TOXICOLOGY
• 影响因子: 4.1
• 作者: Vignaux, Patricia A.;Lane, Thomas R.;Urbina, Fabio;Gerlach, Jacob;Puhl, Ana C.;Snyder, Scott H.;Ekins, Sean
• 通讯作者: Ekins, Sean

Comparing Multiple Machine Learning Algorithms and Metrics for Estrogen Receptor Binding Prediction.

• DOI: 10.1021/acs.molpharmaceut.8b00546
• 发表时间: 2018-10-01
• 期刊: Molecular pharmaceutics
• 影响因子: 4.9
• 作者: Russo DP;Zorn KM;Clark AM;Zhu H;Ekins S
• 通讯作者: Ekins S

Using Bibliometric Analysis and Machine Learning to Identify Compounds Binding to Sialidase-1.

• DOI: 10.1021/acsomega.0c05591
• 发表时间: 2021-02-02
• 期刊: ACS omega
• 影响因子: 4.1
• 作者: Klein JJ;Baker NC;Foil DH;Zorn KM;Urbina F;Puhl AC;Ekins S
• 通讯作者: Ekins S

Synthesis and Evaluation of 9-Aminoacridines with SARS-CoV-2 Antiviral Activity.

• DOI: 10.1021/acsomega.3c05900
• 发表时间: 2023-10-31
• 期刊: ACS OMEGA
• 影响因子: 4.1
• 作者: Jones, Thane;Monakhova, Natalia;Guivel-Benhassine, Florence;Lepioshkin, Alexander;Bruel, Timothee;Lane, Thomas R.;Schwartz, Olivier;Puhl, Ana C.;Makarov, Vadim;Ekins, Sean
• 通讯作者: Ekins, Sean