NOVEL PARADIGMS FOR DRUG DISCOVERY: COMPUTATIONAL MULTITARGET SCREENING

药物发现的新范式：计算多目标筛选

• 批准号: 8703178
• 负责人: RAM SAMUDRALA
• 金额: $ 10.78万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-30 至 2014-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8703178
• 关键词: Adverse effects; Award; Clinical; Communicable Diseases; Communities; Computing Methodologies; Dengue; Disease; Drug Delivery Systems; Effectiveness; Ensure; Excretory function; Family; Funding; Goals; Herpesviridae; Human; Immunocompromised Host; In Vitro; Infection; Inherited; Lead; Machine Learning; Malaria; Metabolism; Methods; Pathway interactions; Pharmaceutical Preparations; Pharmacologic Substance; Population; Proteins; Protocols documentation; Resistance; Risk; Source; Techniques; Technology; Therapeutic; Therapeutic Uses; Time; Variant; Virus; absorption; abstracting; base; cost; drug candidate; drug discovery; in vivo; inhibitor/antagonist; mortality; novel; pathogen; pre-clinical; prospective; screening; small molecule; success; three dimensional structure

DESCRIPTION Abstract: We will create a comprehensive computational drug discovery platform by enhancing a novel technique for a dynamic, fragment based, screening of small molecule compounds against the three dimensional structures of multiple protein targets from infectious disease causing pathogens, followed by prospective in vitro and in vivo experimental verification. We will further modify the most promising lead candidates computationally and screen them against all known human proteins and variants simultaneously to assess for side effects against essential proteins, and to ensure that they possess safe and effective absorption, distribution, metabolism, and excretion profiles against major proteins in known drug delivery pathways. The top ranking leads will again be experimentally verified, and the computational protocol will be iteratively refined using machine learning techniques. We will initially focus on discovering preclinical drug candidates against infections caused by all eight human herpes viruses (HHVs). This virus family infects billions of humans worldwide every year and is the source of significant mortality in immunocompromised patients. Broad spectrum therapeutics against these key pathogens will benefit the entire global community. In contrast to other computational efforts, my group has successfuly applied and experimentally verified their predictions of inhibitors to treat herpes, malaria, and dengue. This was accomplished at a fraction of the time, effort, and cost typically required by pharmaceutical companies. Our significant successes thus far attest to the efficacy of our drug discovery technologies. The Pioneer Award funds will therefore allow us to bridge the gap of discovering computationally predicted lead compounds and demonstrating their preclinical effectiveness for further clinical and therapeutic use. The ultimate goal is to create a comprehensive computational drug discovery pipeline, applicable to any disease, thereby increasing the success rate and reducing the risk, cost, and time associated with traditional drug discovery methods.

描述 摘要： 我们将通过增强一项新技术来创建一个全面的计算药物发现平台 对于动态的、基于片段的、针对三维的小分子化合物筛选 感染性疾病引起病原体的多个蛋白质靶点的结构 体内外实验验证。我们将进一步修改最有希望的领头羊候选人 通过计算并同时对照所有已知的人类蛋白质和变种进行筛选，以评估 针对必需蛋白质的副作用，并确保它们具有安全和有效的吸收， 已知药物传递途径中主要蛋白质的分布、代谢和排泄情况。 排名靠前的线索将再次经过实验验证，计算协议将迭代进行 使用机器学习技术进行精炼。 我们最初将专注于发现临床前候选药物来治疗所有这八种药物引起的感染 人类疱疹病毒(HHV)。这个病毒家族每年感染全球数十亿人，并且 这是免疫功能低下患者显著死亡的原因。广谱疗法治疗 这些关键病原体将使整个全球社会受益。 与其他计算工作相比，我的团队已经成功地应用并进行了实验验证 他们对治疗疱疹、疟疾和登革热的抑制剂的预测。这只用了一小部分就完成了 制药公司通常需要的时间、精力和成本。我们取得的重大成就如下 远远证明了我们的药物发现技术的有效性。 因此，先锋奖基金将使我们能够弥合通过计算预测的发现差距 先导化合物，并展示其临床前有效性，以供进一步的临床和治疗使用。 最终目标是创建一个全面的计算药物发现管道，适用于任何 疾病，从而提高了成功率，并降低了与传统 药物发现方法。