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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：
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
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/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

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. !

项目总结