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

• 批准号: 10004481
• 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
• 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/10004481
• 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

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. !

项目概要 Collaborative Drug Discovery, Inc. (CDD) 提议继续开发一种基于 深度学习神经网络将分子编码成化学丰富的向量。在第一阶段我们展示了 这种表示使得计算模型能够更准确地预测化学性质 分子比最先进的模型更简单，但构建起来也简单得多，因为它们不需要任何专家 决策或优化以实现高性能。在第二阶段，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，这将首次使任何化学家或生物学家能够在 药物发现创建并运行自己的预测模型 - 无需依赖专门的化学信息学 专业知识——但仍然达到或超过当前可用的最佳技术的准确性。科学家参与 从学术实验室到大型制药公司的药物发现研究都依赖 计算 QSAR 模型可预测药理学相关特性并消除执行的需要 对每个感兴趣的分子进行昂贵且耗时的测定（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主要候选系列，探索化学 引线周围的空间可以更有效地生成新颖的知识产权，降低化合物进展的失败率 打通药物发现管道，加速整个药物发现过程。这些好处将是 在大多数治疗领域广泛实现。 我们还计划进一步推进该技术，利用我们化学丰富的矢量表示来 使软件能够创造性地建议新的化合物（这些化合物没有出现在训练库中， 筛选文库或先导系列）在生物活性上同时优于先导候选物， ADME/Tox 和 PK 测定。解决这个逆问题是计算医学的圣杯 化学并有可能彻底改变药物发现。 ！