Novel Paradigms For Drug Discovery: Computational Multitarget Screening

描述 摘要： 我们将通过增强一种新技术来创建一个全面的计算药物发现平台 对于针对三维的小分子化合物的动态的、基于片段的筛选， 结构的多个蛋白质目标从传染病引起的病原体，其次是前瞻性的 体外和体内实验验证。我们将进一步修改最有希望的领先候选人 计算和筛选他们对所有已知的人类蛋白质和变体，同时评估 针对必需蛋白质的副作用，并确保它们具有安全有效的吸收， 在已知的药物递送途径中针对主要蛋白质的分布、代谢和排泄概况。 排名靠前的线索将再次被实验验证，并且计算协议将被迭代地验证。 使用机器学习技术进行改进。 我们最初将专注于发现临床前候选药物，以对抗所有八种引起的感染 人类疱疹病毒（HHV）。这种病毒家族每年感染全球数十亿人， 免疫功能低下患者的显著死亡率的来源。广谱疗法， 这些主要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实验验证了 他们预测抑制剂可以治疗疱疹、疟疾和登革热。这是在一小部分的 制药公司通常需要的时间、精力和成本。因此，我们的重大成功 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发现计算预测的差距， 先导化合物并证明其临床前有效性以用于进一步的临床和治疗用途。 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，从而提高成功率，减少风险，成本，和时间与传统的 药物发现方法

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.