Novel deep learning strategy to better predict pharmacological properties of candidate drugs and focus discovery efforts

新颖的深度学习策略可以更好地预测候选药物的药理学特性并集中发现工作

基本信息

批准号：
10133177
负责人：
BARRY A BUNIN
金额：
$ 74.99万
依托单位：
COLLABORATIVE DRUG DISCOVERY, INC.
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-08-01 至 2022-03-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10133177
关键词：
Animals Area Benchmarking Biological Assay Chemical Structure Chemicals Classification Computer Models Computer software Consumption Data Descriptor Development Disease Drug Kinetics Failure Goals Image Intuition Laboratories Language Libraries Methodology Modeling Molecular Molecular Structure Output Pathway interactions Performance Permeability Pharmaceutical Chemistry Pharmaceutical Preparations Pharmacologic Substance Pharmacology Phase Play Process Property Quantitative Structure-Activity Relationship Research Research Personnel Role Running Scientist Series Solubility Structure Techniques Technology Therapeutic Time Training Translations Validation Variant absorption autoencoder base chemical property cheminformatics computational chemistry computerized tools deep learning deep neural network drug candidate drug discovery experience feeding improved innovation interest lead candidate lead series learning strategy melting model building neural network novel novel strategies novel therapeutics predictive modeling screening vector voice recognition

项目摘要

PROJECT SUMMARY Collaborative Drug Discovery, Inc. (CDD) proposes to continue development of a novel approach based on deep learning neural networks to encode molecules into chemically rich vectors. In Phase 1 we demonstrated that this representation enables computational models that more accurately predict the chemical properties of molecules than state-of-the-art models, yet are also far simpler to build because they do not require any expert decisions or optimization to achieve high performance. In Phase 2 we will exploit this unprecedented simplicity to develop an intuitive software package that will for the first time enable any chemist or biologist working in drug discovery to create and run their own predictive models – without relying on specialized cheminformatics expertise – yet still achieve or exceed the accuracy of the best currently available techniques. Scientists engaged in drug discovery research from academic laboratories to large pharmaceutical companies rely on computational QSAR models to predict pharmacologically relevant properties and obviate the need to perform expensive, time-consuming assays (many of which require animal studies) for every molecule of interest. Improved models will enable researchers to select lead candidate series more effectively, explore chemical space around leads to generate novel IP more efficiently, reduce failure rates for compounds advancing through the drug discovery pipeline, and accelerate the entire drug discovery process. These benefits will be realized broadly across most therapeutic areas. We also plan to take the technology one step further, leveraging our chemically rich vector representation to enable the software to creatively suggest novel compounds (which do not appear in the training libraries, screening libraries, or lead series) that outperform the lead candidates simultaneously on bioactivity, ADME/Tox and PK assays . Solving this inverse problem is the Holy Grail of computational medicinal chemistry and has the potential to revolutionize drug discovery. !

项目摘要协作药物发现公司（CDD）提议继续开发基于新颖的方法深度学习的神经网络将分子编码为化学丰富的载体。在第1阶段，我们证明了该表示形式实现了更准确地预测的化学特性的计算模型分子比最先进的模型，但也更容易构建，因为它们不需要任何专家决策或优化以实现高性能。在第2阶段，我们将利用这种前所未有的简单性开发一个直观的软件包，该软件包将首次启用任何从事的化学家或生物学家毒品发现创建和运行自己的预测模型 - 而无需依靠专门的化学信息学专业知识 - 但仍达到或超过当前最佳可用技术的准确性。科学家参与了从学术实验室到大型制药公司的药物发现研究依靠计算QSAR模型，以预测药理学相关的特性，并消除执行的需求对于每个感兴趣的分子，昂贵的，耗时的测定法（其中许多需要动物研究）。改进的模型将使研究人员能够更有效地选择铅候选系列，探索化学周围的空间导致更有效地生成新颖的IP，降低化合物的衰竭率通过药物发现管道，并加速整个药物发现过程。这些好处将是在大多数治疗区域中广泛实现。我们还计划将技术进一步迈出一步，利用我们的化学丰富的矢量代表来使软件能够创造性地提出新颖的化合物（这些化合物都不出现在培训库中，筛选库或铅序列），以优于候选人，简单地以生物活性为准。 ADME/TOX和PK分析。解决这个反问题是计算医学的圣杯化学，有可能改变药物发现。呢