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.

项目概要 Collaborations Pharmaceuticals, Inc. 在发现需要软件来协助学术和研究后成立。 过去两年中，较小的公司在整理数据和发现新产品或先导优化方面。 人工智能 (AI) 的持续重要性从其数量的爆炸式增长中可见一斑 公司以及越来越多的使用机器学习 (ML) 与制药公司达成数百万美元的交易 这些公司非常关注药物发现建模方面，但 在高质量体外和体内数据 ADME/Tox 数据的管理方面仍然存在未满足的需求和瓶颈 用于 ML 以及验证技术的前瞻性测试 在第一阶段，我们开发了 Assay 原型。 CentralÒ 软件，并将其与来自公共和私人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 名合作者致力于被忽视、罕见或常见的疾病目标，例如 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纠正。 我们还使用收集和清理的数据集构建并验证了贝叶斯模型。 此外，我们开发了新的数据可视化工具，可用于创建这些选择。 用于根据需要与合作者共享以及对新分子进行评分并可视化多个分子的模型 在第二阶段，我们将 Assay CentralÒ 开发为一种简单的生产工具。 部署，基于行业标准技术，提供模型的图形显示和模型信息 重要的是，我们发现客户希望我们向他们提供我们开发的结果！ 我们的按服务付费咨询服务模式使用 Assay CentralÒ 来解决他们的问题，这已经 在第二阶段，我们评估了额外的机器学习算法和分子描述符。 手动整理的数据集以及来自 5000 多个自动整理的数据集的比较算法 ChEMBL。这说明了访问多种算法的实用性以及贝叶斯算法的原理。 通常与其他机器学习算法相当，这也促使我们开发新的软件来集成。 我们还探索了寻找罕见疾病数据集并应用我们的数据管理和机器学习。 通过这些和其他合作以及有关阿尔茨海默病的内部项目。 （通过 NIH NIGMS 补充）我们已经能够将已批准的药物重新用于多个目标 对于这种疾病和其他疾病，我们进行了多轮模型构建和喂养。 最后，我们开发了原型工具来实现。 我们开发自动化分子设计，评估其可合成性并进行逆合成分析。 这些结合起来极大地增加了我们能够开展的项目数量（最终 发布以提高我们的知名度），创建了新的衍生产品作为模型集合（MegaTransÒ、MegaToxÒ 和 MegaPredictÒ）、分子相关知识产权和创造就业 在第二阶段，我们现在建议重点关注。 我们已经确定了帮助这些技术商业化和进一步开发的步骤。 开发自动管理软件来处理非结构化数据库中的复杂生物数据将是一个 我们还认识到，对于许多疾病，我们可以拥有完整或接近的优势。 完整的靶标集合可以使我们了解分子如何干扰生物 仅从结构出发的途径，这可以应用于复杂的疾病和“不良结果途径” 我们还建议整合最先进的多目标生成模型进行分子设计。 进入我们的 Assay Central 计算软件，以补充我们的模拟生成和逆合成 我们将使用一些热门产品来验证这一功能。 在第二阶段中鉴定出的针对不同靶标的分子，包括人乙酰胆碱酯酶。 然后拥有从数据管理到分子设计和逆合成的全套集成功能 分析并使我们能够吸引与公司进行更大的交易。

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

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

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

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

Machine Learning Models Identify Inhibitors of SARS-CoV-2.

• DOI: 10.1021/acs.jcim.1c00683
• 发表时间: 2021-09-27
• 期刊: Journal of chemical information and modeling
• 影响因子: 5.6
• 作者: Gawriljuk VO;Zin PPK;Puhl AC;Zorn KM;Foil DH;Lane TR;Hurst B;Tavella TA;Costa FTM;Lakshmanane P;Bernatchez J;Godoy AS;Oliva G;Siqueira-Neto JL;Madrid PB;Ekins S
• 通讯作者: Ekins S