AI-Powered Biased Ligand Design

人工智能驱动的偏向配体设计

• 批准号: 10637910
• 负责人: Junmei Wang
• 金额: $ 31.8万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-05-01 至 2027-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10637910
• 关键词: Address; Adverse effects; Affinity; Agonist; Algorithms; Artificial Intelligence; Binding; Biological Assay; CNR1 gene; CNR2 gene; Calibration; Chemical Structure; Chemicals; Coin; Computational algorithm; Computer Assisted; Computers; Computing Methodologies; Databases; Docking; Drug Design; Drug Modelings; Drug Targeting; Evaluation; Future; Generations; Goals; Grant; Internet; Learning; Libraries; Ligands; Measures; Methods; Modeling; Modernization; Modification; Molecular; Outcome; Pathway interactions; Pattern; Pharmaceutical Preparations; Play; Protocols documentation; Public Domains; Reporting; Research; Resources; Role; Series; Signal Pathway; Signal Transduction; Specificity; System; Techniques; Training; Validation; antagonist; application programming interface; cannabinoid receptor; computational platform; computerized tools; design; drug candidate; drug development; drug discovery; drug-like compound; generative adversarial network; improved; machine learning algorithm; network models; novel; novel therapeutics; rational design; screening; side effect; statistics; success; tool; virtual screening; web app; web-based tool

AI-Powered Biased Ligand Design A biased ligand which elicits a certain cellular signal but does not affect other pathways is an attractive drug candidate as it can minimize unwanted or adverse effects. Unfortunately, very few current computer-aided drug design methods can enable biased ligand design. Moreover, there is an urgent need to expand the druggable chemical space for those very promising drug targets which have plenty of potent ligands developed, but unfortunately, no approved drugs. We plan to apply the artificial intelligence (AI) techniques to address the two challenges by developing interaction profile scoring function models to enable biased ligand design, and Drug-GAN models to achieve de novo chemical structure design. The central hypothesis of this application states that the function as well as the signaling pathways elicited by a ligand is encoded in the ligand- residue interaction profile (IP), and machine learning algorithms can learn the key attributes of the IP and generate scoring functions, coined IPSFs, to recognize similar ligands in a screening library. The second hypothesis of this application states that generative adversarial networks (GAN) can learn chemical patterns from input and de novo design novel chemical structures. Thus, the AI-powered algorithms and Drug-GAN models will be able to tackle the two challenges, and likely revolutionize future drug discovery. Cannabinoid receptors, CB1R and CB2R, are an ideal model target system for experimental evaluation. The proposal has four aims. In Aim 1, we will develop IPSFs to specifically design agonists or antagonists of CB1R or CB2R, and agonists which can activate a certain signaling pathway. Those target-specific, function-specific and signaling pathway-specific IPSFs will enable biased ligand design. In Aim 2, we will develop Drug- GAN models to rationally design novel chemical structures as potential agonists or antagonists of CB1R and CB2R. In Aim 3, we will acquire top hits of screening compounds and Drug-GAN designed compounds, and conduct binding and functional assays to validate the predictions. In Aim 4, we will develop an expandable computational platform called PBLD to integrate the developed IPSF models and Drug-GAN-generated druglike chemicals, and launch webtools and APIs to conduct biased ligand design using the developed IPSF models and de novo design using the developed Drug-GAN models. We estimate that IPSFs and Drug-GAN models can be generated for about 300 drug targets based on a recent statistics analysis on the ChEMBL database. PBLD has the potential to become a national resource for biased ligand design with more and more IPSF and Drug-GAN models implemented to PBLD.

人工智能支持的偏向配位体设计 产生某种细胞信号但不影响其他途径的有偏见的配体是 有吸引力的候选药物，因为它可以将不想要的或不良反应降到最低。不幸的是，很少有人 目前的计算机辅助药物设计方法可以实现偏向配基设计。此外，还有 迫切需要为那些非常有希望的药物靶点扩大可药物化学空间 它们已经开发了大量有效的配体，但不幸的是，没有批准的药物。我们计划 应用人工智能(AI)技术通过开发 支持偏向配基设计的相互作用简档评分函数模型，以及药物-GaN模型 实现从头开始的化学结构设计。本申请的中心假设是 由配体引发的功能和信号通路都编码在配体中- 残基相互作用简档(IP)和机器学习算法可以学习关键属性 IP并生成计分函数，称为IPSFs，以在筛选中识别相似的配体 图书馆。这一应用的第二个假设指出，生成性对抗网络 (GaN)可以从输入和从头设计新的化学结构中学习化学模式。 因此，人工智能支持的算法和药物-GAN模型将能够应对这两个挑战， 可能会给未来的药物发现带来革命性的变化。大麻素受体CB1R和CB2R是一种 理想的实验评估模型指标体系。该提案有四个目标。在目标1中，我们 将开发IPSFs来专门设计CB1R或CB2R的激动剂或拮抗剂，以及激动剂 它可以激活特定的信号通路。这些特定于目标、特定于功能和 信号通路特异的IPSFs将使有偏见的配体设计成为可能。在目标2中，我们将开发药物- GaN模型合理设计新的化学结构作为潜在的激动剂或拮抗剂 CB1R和CB2R。在目标3中，我们将获得筛选化合物和药物-GAN的热门 设计了化合物，并进行了结合和功能分析来验证预测。在……里面 目标4，我们将开发一个可扩展的计算平台PBLD来集成 开发IPSF模型和药物-GAN产生的类药物化学品，并推出网络工具和 使用开发的IPSF模型进行偏向配基设计的API和使用 已开发的药物-GaN模型。我们估计IPSFs和药物-GaN模型可以 根据CHEMBL的最新统计分析，为大约300个药物靶点生成 数据库。PBLD有可能成为全国有偏见的配基设计资源 越来越多的IPSF和药物-GaN模型被应用到PBLD中。