Fragment-to-lead and target validation

片段到先导和目标验证

• 批准号: 10513873
• 负责人: John Damon Chodera
• 金额: $ 847.72万
• 依托单位: SLOAN-KETTERING INST CAN RESEARCH
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-05-16 至 2025-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10513873
• 关键词: Lead; Structure; Validation

The discovery of an antiviral therapeutic requires a target that is robust to mutations, and suitable chemical matter that modulates the target. The Fragment-to-Lead and Target Validation project sits at the crucial interface between target validation and chemical lead generation. We aim to validate biological hypotheses behind target selection and produce lead molecules for downstream therapeutic development, producing leads against 9 antiviral targets. By tightly integrating the unique capabilities of extremely high-throughput X-ray crystallography at Diamond Light Source, this project leverages recent advances in artificial intelligence and machine learning (Al/ML) and exascale computing free energy calculations to rapidly generate novel potent lead compounds able to overcome resistance from initial X-ray fragment screens. This project builds on the successful COVID Moonshot initiative, which executed a rapid fragment-to-lead campaign against SARS-CoV-2 main protease - starting from fragment screen, a lead compound with IC50 = 140 nM was discovered in <6 months and <400 compounds made. In the first stage of a hit-to-lead campaign, we will use machine learning to learn pharmacophore features from high throughput fragment screen readout, and use these patterns to search for potent hits from virtual, synthetically accessible chemical space. The goal is to arrive at chemical matter which engages the viral protein with antiviral activity, which in turn enables experiments that validate the target. Working with Project 1 (Antiviral Targeting to Suppress Resistance), potent hits will be used to validate biological hypotheses of target engagement, and Deep Mutational Scanning and serial passaging will be used to evaluate the barrier to resistance and the mutations that give rise to resistance. These insights will be used in the iterative medicinal chemistry design process, in selecting chemical series with less resistance potential and focusing on expanding into vectors that target mutationally robust residues. In the second stage of the hit-to-lead campaign, we will build on the wealth of structural and bioactivity data generated in the first stage and use machine learning, alchemical free energy calculations, and high throughput nanomole chemistry to rapidly evaluate and synthesize analogues which expand into promising vectors. The goal of this phase is to arrive at: (i) potent inhibitor in biochemical assays with IC50<500 nM; (ii) inhibition in cellular antiviral assays with EC50<3μM and cytotoxicity CC50>50 μM; and (iii) developable Tier 1 ADME and physicochemical properties: clog P<4, kinetic solubility > 50 μM, rat and human microsomal stability Clint<50, MDCK-LE permeability Papp>1x108cm/s. These leads are inputs to Lead Optimization (Project 5), which will focus on further improvements in potency, ADMET and in vivo pharmacokinetics.

抗病毒治疗剂的发现需要对突变具有鲁棒性的靶标，以及合适的化学物质。 调制目标的物质。片段到铅和目标验证项目位于关键 目标验证和化学铅生成之间的接口。我们的目标是验证生物学假设 为下游治疗开发提供先导分子， 针对9个抗病毒靶点。通过紧密集成极高通量X射线的独特功能， 钻石光源的晶体学，该项目利用了人工智能的最新进展， 机器学习（Al/ML）和exascale计算自由能计算，以快速生成新的有效的 能够克服来自初始X射线碎片筛选的阻力的先导化合物。该项目建立在 成功的COVID Moonshot计划，该计划执行了一项快速的碎片到铅运动， SARS-CoV-2主要蛋白酶-从片段筛选开始， 在不到6个月的时间里发现了不到400种化合物。在一个点击率领先运动的第一阶段，我们将使用 机器学习从高通量片段筛选读数中学习药效团特征，并使用 这些模式从虚拟的、可合成的化学空间中搜索有效的命中。目标是 得到一种化学物质，它使病毒蛋白具有抗病毒活性， 验证目标的实验。与项目1（抗病毒靶向抑制耐药性）合作， 有效的命中将用于验证目标接合的生物学假设，以及深度突变扫描 并将使用连续传代来评估耐药性屏障和引起 阻力这些见解将用于迭代药物化学设计过程， 具有较小抗性潜力的化学品系列，重点是扩展到突变靶向载体 鲁棒残留物。在“点击领先”运动的第二阶段，我们将建立在结构和 在第一阶段生成的生物活性数据，并使用机器学习，炼金术自由能计算， 高通量纳米分子化学，以快速评估和合成类似物， 有前途的载体。该阶段的目标是达到：（i）在生物化学测定中具有IC 50 <500的有效抑制剂 nM;（ii）在细胞抗病毒试验中的抑制作用，EC 50 <3μM，细胞毒性CC 50>50 μM;和（iii）可开发 第1层ADME和理化性质：阻塞P<4，动力学溶解度> 50 μM，大鼠和人微粒体 稳定性克林特<50，MDCK-LE渗透率Papp> 1 × 108 cm/s。这些潜在客户是潜在客户优化的输入 （项目5），将重点放在进一步改善效力，ADMET和体内药代动力学。