NOVEL PARADIGMS FOR DRUG DISCOVERY: COMPUTATIONAL MULTITARGET SCREENING

描述 摘要： 我们将通过增强一种新的技术来创建一个全面的计算药物发现平台，该技术用于针对传染病引起的病原体的多个蛋白质靶标的三维结构动态地、基于片段地筛选小分子化合物，然后进行前瞻性的体外和体内实验验证。我们将进一步通过计算机对最有希望的候选先导进行修改，并同时针对所有已知的人类蛋白质和变体进行筛选，以评估对必需蛋白质的副作用，并确保它们在已知药物传递途径中具有针对主要蛋白质的安全有效的吸收、分布、代谢和排泄特征。排名靠前的领先地位将再次得到实验验证，计算协议将使用机器学习技术进行迭代改进。我们最初将专注于发现针对所有八种人类疱疹病毒(HHV)引起的感染的临床前候选药物。该病毒家族每年感染全世界数十亿人，是免疫受损患者大量死亡的来源。针对这些关键病原体的广谱疗法将使整个全球社会受益。与其他计算工作相反，我的团队已经成功地应用并实验验证了他们关于抑制剂治疗疱疹、疟疾和登革热的预测。这只是制药公司通常所需的时间、精力和成本的一小部分。到目前为止，我们取得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通过计算预测的先导化合物并展示其临床前有效性以供进一步临床和治疗使用的差距。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，从而增加成功

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 itera- tively 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 suc