MegaPredict for predicting natural product uses and their drug interactions

项目摘要 “MegaPredict”的目标是使科学家能够对一种天然产品（或任何 分子），并确定疗效评估的目标以及确定任何潜在的责任。我们正在建设 我们以前的工作，其中汇编了一个全面的数据集的结构活性数据， 广泛的疾病靶点和其他属性，以可用于模型构建的形式。所有这些模型 利用策展的开放数据的许多来源，包括ChEMBL，ToxCast等，我们已经开发了一个原型 MegaPredict使用贝叶斯算法和ECFP 6指纹来输出优先级“目标”列表。我们 意识到算法或描述符可能都不是最佳的，因此我们建议解决这个问题，因为我们 验证MegaPredict并在此提议上开发产品。我们的团队具备开发 我们将利用我们庞大的合作者网络来帮助我们验证 化合物. 我们将首先创建一个脚本，采取自然的产品，并对成千上万的机器评分 然后，学习模型对输出进行排序，以提出功效目标。我们将使用超过12，000个ChEMBL 从ChEMBL v24数据库中提取的衍生靶向测定/生物活性组，以及EPA Tox 21 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 对超过200种已发表的化合物重复这一过程，并对照已知的化合物访问输出。我们打算 以比较该方法与合成药物或类似药物的化合物以及天然产品的性能。 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，支持向量机和深度不同的神经网络（DNN），我们将评估 天然产物的预测，并与贝叶斯方法进行比较。我们将比较ECFP 6与 其他2D，3D描述符和物理化学性质，以确定最佳组合 生成天然产物的预测，并比较这与合成化合物的不同之处。 我们将验证我们对天然产品功效评估的预测。我们将与多个 学术团体对至少20种感兴趣的天然产品进行预测， 目标或疾病。我们的目标将是确定以前未知的潜在目标，然后生成 内部或与学术合作者的体外数据。 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 优先目标和责任。我们已经开发了多个软件原型（e。检测中心，MegaTox， 等等）。并将确保用户友好的界面，并开发新的可视化方法和算法 根据数千个机器学习模型的输出，对潜在的预测目标进行优先级排序。 在第一阶段，我们将在内部与合作者一起使用该软件，以快速制作原型。 一个商业产品，并通过建立一个更大的学术和工业网络， 这将有助于优先考虑最相关的特征。使用机器学习模型， 天然产品的ECFP指纹是有限的，因为ECFP 6指纹不能区分这些非常不同的产品。 分子的种类。但这为我们提供了一个“药效团”式方法的机会 （理想情况下不直接使用3D构象）。因此，我们将专注于开发一个“基于3D形状的 或者开发一种新的“二维指纹”，捕捉天然和药物样分子的“三维形状”。 目前，ChEMBL和PubChem等的公共数据集主要由药物样分子组成，但如果 我们有指纹可以比较药物和天然产物的分子， 我们的MegaPredict模型也适用于天然产品。我们还可以尝试用我们的 或者我们可以使用来自自然界的模型来浏览类药物化合物的目录， 产品.这将是一个重要的创新。此外，在第二阶段，重要的是看看我们是否可以 寻找天然产品对7000种罕见疾病中任何一种的用途。开发软件预测潜在的 天然产物药物与各种靶点的相互作用可能对监管组织以及 并且可以拓宽能够更有效地混合天然产物和类药物的效用 在这个竞技场中，模型中的化合物将对化学信息学的价值产生深远的影响。

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.