MegaPredict for predicting natural product uses and their drug interactions

MegaPredict 用于预测天然产物用途及其药物相互作用

• 批准号: 10055938
• 负责人: SEAN EKINS
• 金额: $ 15.57万
• 依托单位: COLLABORATIONS PHARMACEUTICALS, INC.
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-15 至 2021-08-14
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10055938
• 关键词: 3-Dimensional; Address; Algorithms; Bayesian Method; Bayesian learning; Biological Assay; Catalogs; Chemical Models; Chemistry; Collection; Complex; Computer software; Data; Data Set; Databases; Decision Trees; Descriptor; Disease; Drug Industry; Drug Interactions; Drug Targeting; Ensure; Fingerprint; G-Protein-Coupled Receptors; Goals; Healthcare; Human; In Vitro; Industrialization; Lead; Literature; Logistic Regressions; Machine Learning; Measurement; Methodology; Methods; Modeling; Molecular; Molecular Conformation; Natural Product Drug; Natural Products; Output; Pharmaceutical Preparations; Phase; Phenotype; Phosphotransferases; Process; Property; PubChem; Public Domains; Publishing; Rare Diseases; Running; Scientist; Shapes; Software Tools; Source; Structure; Testing; Therapeutic; Toxic effect; Validation; Viral; Visualization; Work; base; cheminformatics; commercial application; deep neural network; drug discovery; human disease; improved; in vivo; industry partner; innovation; interest; machine learning algorithm; machine learning method; model building; novel; open data; pharmacophore; predictive modeling; predictive tools; process repeatability; prospective; prototype; random forest; software development; statistics; support vector machine; symposium; synthetic drug; tool; user-friendly; web interface; web-based tool

Project Summary The objective of ‘MegaPredict’ is to enable scientists to generate predictions for a natural product (or any molecule) and identify targets for efficacy assessment as well as identify any potential liabilities. We are building on our previous work which has compiled a comprehensive collection of datasets for structure-activity data for a broad variety of disease targets and other properties, in a form ready for model building. All of these models utilize the many sources of curated open data, including ChEMBL, ToxCast etc. We have developed a prototype of MegaPredict that utilizes Bayesian algorithm and ECFP6 fingerprints to output a list of prioritized ‘targets’. We realize that neither the algorithm or the descriptors may be optimal therefore we propose to address this as we validate MegaPredict and develop a product over this proposal. Our team is suitably qualified to develop the software needed and we will leverage our large collaborator network to assist us in validating the activity of compounds. We will initially create a script to take a natural product and score it against many thousands of machine learning models then rank the outputs to propose efficacy targets. We will use over 12,000 ChEMBL derived target-assay / bioactivity groups extracted from the ChEMBL v24 database, as well as EPA Tox21 measurements and other public datasets, using methodology that we have already partially developed. We can repeat this process for over 200 published compounds and access the outputs versus what is known. We intend to compare how the approach performs with synthetic drugs or drug-like compounds as well as natural products. We will assess whether other machine learning algorithms and molecular descriptors can improve predictions. As we generate machine learning models such as Linear Logistic Regression, AdaBoost Decision Tree, Random Forest, Support Vector Machine and deep neural networks (DNN) of varying depth we will assess the predictions for natural products and compare with the Bayesian approach. We will compare ECFP6 with other 2D, 3D descriptors and physicochemical properties in order to identify the optimal combination for generating predictions for natural products and compare how this differs for synthetic compounds. We will validate our predictions for natural product efficacy assessment. We will work closely with multiple academic groups to generate predictions for at least 20 natural products of interest against over 20 different targets or diseases. Our goal will be to identify potential targets that were previously unknown and then generate in vitro data inhouse or with academic collaborators. Develop a prototype user interface for input of a structure, processing an input molecule and output of prioritized targets and liabilities. We have developed multiple software prototypes (e. Assay Central, MegaTox, etc.) previously and will ensure a user-friendly interface and develop new visualization methods and algorithms for prioritizing potential predicted targets based on the outputs of thousands of machine learning models. In Phase I, we will use the software internally with collaborators to rapidly prototype it. In Phase II we will develop a commercial product, and greatly expand our validation by building a larger network of academic and industry partners that would help to prioritize features of most relevance. Using the machine learning models which we have for natural products is limited because ECFP6 fingerprints cannot distinguish between these very different classes of molecules. But this provides us with an opportunity by going for a "pharmacophore" style approach (ideally without using 3D conformations directly). We will therefore focus on developing a ‘3D shape-based fingerprint’ or developing a novel ‘2D fingerprint’ that captures the ‘3D shape’ for natural- and druglike molecules. Currently, the public datasets in ChEMBL and PubChem etc. are made up of mostly druglike molecules, but if we have fingerprints that can compare drug-like and natural product-like molecules then we can likely reliably use our MegaPredict models for natural products as well. We can also attempt to rank natural products with our ChEMBL models or we can look through catalogs of druglike compounds using models derived from natural products. That would be an important innovation. Additionally, in Phase II it would be important to see if we could find uses for natural products with any of the 7000 rare diseases. Developing software that predicts potential natural product drug interactions with various targets could be useful to regulatory organizations as well as the pharmaceutical industry and may broaden utility of being able to more effectively mix natural product and druglike compounds in models will have a profound effect on the value of cheminformatics in this arena.

项目摘要 “大易感性”的目的是使科学家能够为天然产品（或任何任何人）产生预测 分子）并确定效率评估的目标，并确定任何潜在负债。我们正在建造 关于我们以前的工作，该工作已汇编了用于结构活动数据的全面数据集 各种各样的疾病目标和其他特性，以准备建造模型的形式。所有这些模型 利用许多精选的开放数据来源，包括Chembl，Toxcast等。我们已经开发了一个原型 使用贝叶斯算法和ECFP6指纹来输出优先级的“目标”列表。我们 意识到算法或描述符都不是最佳的，因此我们建议在我们的情况下解决这个问题 根据该提案验证Megapredict并开发产品。我们的团队有资格开发 需要软件，我们将利用我们的大型合作者网络来帮助我们验证 化合物。 我们最初将创建一个脚本以使用自然产品并与数千台机器进行评分 然后，学习模型将输出对提案效率目标进行排名。我们将使用12,000多个Chembl 从Chembl V24数据库中提取的派生目标测定 /生物活性组以及EPA TOX21 测量和其他公共数据集，使用我们已经部分开发的方法。我们可以 重复200多个已发表的化合物的过程，并访问输出与已知内容。我们打算 比较该方法与合成药物或类似药物的化合物以及天然产物的性能。 我们将评估其他机器学习算法和分子描述符是否可以改善 预测。当我们生成机器学习模型时，例如线性逻辑回归，adaboost决策 树，随机森林，支持向量机和深度深度的深度神经网络（DNN）我们将评估 天然产品的预测并与贝叶斯方法进行比较。我们将将ECFP6与 其他2D，3D描述符和物理属性，以确定最佳组合 生成对天然产品的预测，并比较合成化合物的这种差异。 我们将验证我们对自然产品效率评估的预测。我们将与多个 学术组为至少20种自然产品产生预测，以超过20多种不同 目标或疾病。我们的目标是确定以前未知然后生成的潜在目标 体外数据内部或与学术合作者一起。 开发一个原型用户界面，用于输入结构，处理输入分子和输出 优先考虑目标和负债。我们已经开发了多个软件原型（例如，Assay Central，Megatox， 等等）以前，将确保用户友好的界面并开发新的可视化方法和算法 为了根据数千个机器学习模型的输出来确定潜在的预测目标。 在第一阶段，我们将与合作者内部使用该软件来快速原型。在第二阶段，我们将发展 商业产品，并通过建立更大的学术和行业网络来大大扩展我们的验证 合作伙伴将有助于优先考虑最相关的功能。使用我们的机器学习模型 天然产品的限制是有限的，因为ECFP6指纹无法区分这些截然不同的 分子类。但这通过采用“药效”样式方法为我们提供了机会 （理想情况下，无需直接使用3D构象）。因此，我们将专注于开发基于'3D形状的 指纹”或开发一种新颖的“ 2D指纹”，该新颖的“ 3D形状”用于天然和吸毒分子。 目前，Chembl和PubChem等的公共数据集由主要类似药物的分子组成，但是如果 我们有指纹可以比较类似药物和天然产物的分子 还使用我们的天然产品模型。我们还可以尝试用我们的 Chembl模型，或者我们可以使用源自天然的模型来浏览类似药物的化合物的目录 产品。那将是一项重要的创新。此外，在第二阶段中，重要的是要查看我们是否可以 查找具有7000种稀有疾病中任何一种天然产品的用途。开发预测潜力的软件 天然产品药物与各种目标的相互作用可能对监管组织以及 制药行业，可以扩大能够更有效地将天然产品和吸毒的效用 模型中的化合物将对该领域的化学信息学的价值产生深远的影响。