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.

项目概要 中枢神经系统药物发现的失败率很高，特别是采用新模式的一流疗法 行动，凸显了提高精神疾病药物发现成功率的明显未满足的需求。 一个众所周知的问题是当前生物测定和动物模型预测疗效和副作用的能力较差。 化合物的影响的另一个重要问题是中枢神经系统疾病缺乏明确的靶标，这些疾病很复杂。 并且需要多药理学的表型筛选平台非常适合化合物的药物发现。 以与目标无关的方式，允许发现和开发多种药物。 然而，除了 PsychoGenics SmartCube® 之外，合适的经过验证的体内表型筛选很少见 平台，已用于筛选约 8000 种化合物和参考药物的可用性。 然而，表型筛选将发现限制于已知的化学空间。 现在可用于设计可用于探索未探索的化学空间的新型药物。 捕获结构与表型关系的机器学习模型以及可以生成新颖的模型 因此，类药物化合物有望提供一个真正新颖的平台。 使用我们收集的 8000 多种化合物和表型机器学习模型 (“PhenCheML”) 在 Psychogenics 药物的 SmartCube® 表型体内平台中进行筛选，2) 将此类模型与 Collaboration Pharma de novo 药物设计生成机器学习模型 MegaSyn®，并生成新颖药物 用于体内测试的 CNS 类药物化合物。该 I 期 SBIR 项目的成功将产生 PhenCheML， 一个基于机器学习的新型表型药物发现平台，可以生成新的化学型和 预测它们的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。 通过设计和合成新分子，用于在 SmartCube® 中进行测试并在二级分析中进行验证 重点关注精神疾病（抑郁、焦虑、精神病和双相情感障碍）。 使用该平台在模型系统中生成具有潜在治疗效果的小说 精神、神经发育和神经退行性疾病（例如 Rett、ASD、HD、PD 等）。 该平台将是一种创新且独特的药物设计方法，以按服务收费的形式提供或用于药物 由 PGI 及其合作伙伴开发。