A Next-Generation Platform for Novel Drug Discovery Using Deep Learning of ADMET Properties.

DeepADMET是一个下一代项目，通过Optionals现有的软件产品（StarDrop（tm））进行集成，将开发和应用新型深度学习方法，以扩展和改进重要的吸收，分布，代谢，排泄和毒性（ADMET）终点的预测模型，以指导更有效的设计和选择高质量的候选药物。不易获得的重要ADMET终点的数据将作为为这些终点构建模型的基础，这些终点目前不可用，并扩展和提高当前最先进模型的准确性。项目目标的成功实现将通过我们现有的销售团队或在适当的情况下与该领域或地区的专业组织合作，通过软件许可证的商业化，增强OptionalTM在计算药物发现市场的立足点。DeepADMET是Optimable增强化学（TM）战略的关键部分，超越了“工具”，以支持化学优化。英特尔是剑桥大学的一个分支，它开发了一种独特的人工智能（AI）方法，用于从不完整的数据集训练神经网络。MDC拥有广泛的合作伙伴，来自：英国生物技术，使其能够实现其核心战略目标，作为推动该行业创新的催化剂。具体而言，该项目利用了信息提取和策展策略和工具方面的关键和稀有专业知识，这里计划的进一步开发和验证将适用于未来的转化生命科学领域。

Integrated through Optibrium's existing software offering (StarDrop(tm)), DeepADMET is a next-generation project that will develop and apply novel deep learning methods to extend and improve predictive models of important Absorption, Distribution, Metabolism, Excretion and Toxicity (ADMET) endpoints to guide more efficient design and selection of high quality drug candidates. Data for important ADMET endpoints that are not readily accessible will be curated as the basis for building models for these endpoints, which are not currently available, and extend and improve the accuracy of the current state-of-the-art models. The successful delivery of project objectives will enhance Optibrium's foothold in the Computational Drug Discovery market through the commercialisation of the software licences through our existing sales force or, where appropriate, by partnership with organisations specialising in that sector or region. DeepADMET forms a key part of Optibrium's Augmented Chemistry(tm) strategy, moving beyond 'tools' to support chemistry optimisation. Intellegens is a spin-out from the University of Cambridge that has developed a unique Artificial Intelligence (AI) method for training neural networks from incomplete data sets. The MDC has a wide variety of partners drawn from: UK biotech and enables it to meet its core strategic objectives as a catalyst for enabling innovation within the sector. Specifically, the project utilizes key and rare expertise in information extraction and curation strategies and tools, the further development and validation planned here will be applicable in future translational life science sectors.