Machine Learning Phenotypic De Novo Drug Design

机器学习表型从头药物设计

• 批准号: 10762633
• 负责人: Daniela Brunner
• 金额: $ 49.99万
• 依托单位: PSYCHOGENICS, INC.
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-15 至 2025-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10762633
• 关键词: Address; Algorithms; Animal Model; Antipsychotic Agents; Anxiety; Behavioral; Biological; Biological Assay; Biological Models; Bipolar Disorder; Central Nervous System; Central Nervous System Agents; Central Nervous System Diseases; Chemicals; Chemistry; Clinical Trials; Cognition; Collaborations; Collection; Complex; Data; Data Set; Decision Trees; Descriptor; Development; Diffusion; Dose; Drug Design; Drug Screening; Ensure; Expert Systems; Failure; Fee-for-Service Plans; Fingerprint; Generations; In Vitro; Lead; Libraries; Ligands; Logistic Regressions; Machine Learning; Mental Depression; Mental disorders; Methods; Modeling; Mus; Neurodegenerative Disorders; Neurodevelopmental Disorder; Outcome; Output; Pharmaceutical Preparations; Pharmacology; Phase; Phenotype; Property; Psychoses; Schizophrenia; Series; Small Business Innovation Research Grant; Structure; System; Techniques; Technology; Testing; Therapeutic; Therapeutic Effect; Toxic effect; Training; Validation; Work; algorithm training; analog; deep neural network; design; drug development; drug discovery; drug-like compound; effective therapy; improved; in vivo; in vivo evaluation; innovation; machine learning method; machine learning model; novel; novel therapeutics; pharmacologic; phase III trial; pre-clinical; prediction algorithm; random forest; response; screening; side effect; small molecule; small molecule libraries; statistics; stereochemistry; success; support vector machine

PROJECT SUMMARY The high rate of failure in CNS drug discovery, in particular of the first-in-class therapeutics with new modes of action, highlights a clear unmet need to improve the success rate in drug discovery for psychiatric disorders. One well-known issue is the poor ability of current bioassays and animal models to predict the efficacy and side- effects of compounds. Another important issue is the lack of clear targets for CNS disorders, which are complex and require polypharmacology. Phenotypic screening platforms are well-suited for drug discovery of compounds in a target-agnostic manner, allowing for the discovery and development of poly pharmacological agents. Suitable proven in vivo phenotypic screens, however, are rare with the exception of PsychoGenics SmartCube® platform, which has been used to screened ~8000 compounds and reference drugs. Compound availability for phenotypic screening, however, restrict discovery to known chemical spaces. Novel machine learning methods are now available to design novel drugs that can be used to poke unexplored chemical spaces. The combination of a machine learning model capturing structure-to-phenotype relationships and a model that can generate novel drug-like compounds promises to deliver a truly novel platform. Our aims therefore are 1) to generate a structure- to-phenotype machine learning model (“PhenCheML”) using our collection of more than 8000 compounds and drugs screened in Psychogenics’ SmartCube® phenotypic in vivo platform, and 2) to combine such model with Collaboration Pharma de novo drug design generative machine learning model MegaSyn®, and generate novel CNS drug-like compounds for testing in vivo. The success of this Phase I SBIR project will result in PhenCheML, a novel phenotypic machine learning-based drug discovery platform that can generate novel chemotypes and predict their therapeutic value. If our Phase I project is successful, we will extend it in a Phase II application through the design and synthesis of novel molecules for test in SmartCube® and validation in second tier assays focusing on psychiatric disorders (depression, anxiety, psychosis, and bipolar disorder). We will also explore the use of the platform for generation of novel compounds with potential therapeutic effects in model systems of psychiatric, neurodevelopmental, and neurodegenerative disease (e.g., Rett, ASD, HD, PD, etc). If successful, this platform will be an innovative and unique drug design method, offered by as fee-for-service or used in drug development by PGI and its partners.

项目摘要 CNS药物发现的高失败率，特别是具有新的治疗模式的一流疗法， 行动，突出了一个明确的未满足的需要，以提高成功率的药物发现精神疾病。 一个众所周知的问题是目前的生物测定和动物模型预测疗效和副作用的能力差， 化合物的影响。另一个重要的问题是缺乏明确的中枢神经系统疾病的目标，这是复杂的 需要多种药理学表型筛选平台非常适合化合物的药物发现 以目标不可知的方式，允许发现和开发多药理学试剂。 然而，除了PsychoGenics SmartCube 该平台已用于筛选约8000种化合物和参比药物。化合物的可用性 然而，表型筛选将发现限制在已知的化学空间。新的机器学习方法 现在可以用来设计新的药物，可以用来戳未开发的化学空间。相结合 一个机器学习模型捕捉结构与表型的关系， 类药物化合物有望提供一个真正新颖的平台。因此，我们的目标是：（1）建立一个结构- 表型机器学习模型（“PhenCheML”）使用我们收集的超过8000种化合物， 在Psychogenics的SmartCube®表型体内平台中筛选的药物，以及2）将这种模型与以下联合收割机组合： 协作制药从头药物设计生成机器学习模型MegaSyn®，并生成新的 用于体内测试的CNS药物样化合物。第一阶段SBIR项目的成功将导致PhenCheML， 一种新型的基于表型机器学习的药物发现平台，可以产生新的化学型， 预测其治疗价值。如果我们的第一阶段项目成功，我们将在第二阶段应用中扩展它 通过设计和合成用于SmartCube®测试的新型分子，并在二级检测中进行验证 专注于精神疾病（抑郁症，焦虑症，精神病和双相情感障碍）。我们亦会探讨 该平台用于产生具有潜在治疗作用的新化合物的用途， 精神、神经发育和神经变性疾病（例如，Rett、ASD、HD、PD等）。如果成功， 该平台将是一种创新和独特的药物设计方法，由收费服务提供或用于药物 由PGI及其合作伙伴开发。