AI-Powered Biased Ligand Design

人工智能驱动的偏向配体设计

基本信息

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
项目类别：
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

项目摘要

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.

人工智能驱动的偏向配体设计引起某种细胞信号但不影响其他途径的偏向配体是有吸引力的候选药物，因为它可以最大限度地减少不需要或不利的影响。不幸的是，很少当前的计算机辅助药物设计方法可以实现有偏差的配体设计。而且，还有迫切需要扩大那些非常有前途的药物靶标的可成药化学空间已开发出大量有效的配体，但不幸的是，尚未批准药物。我们计划应用人工智能（AI）技术通过开发来解决这两个挑战相互作用概况评分函数模型，以实现有偏差的配体设计和药物-GAN 模型实现从头化学结构设计。该申请的中心假设指出配体引发的功能和信号传导途径是在配体中编码的残基相互作用谱（IP），机器学习算法可以学习残基的关键属性 IP 并生成评分函数（称为 IPSF），以在筛选中识别相似的配体图书馆。该应用程序的第二个假设指出生成对抗网络（GAN）可以从输入中学习化学模式并从头设计新颖的化学结构。因此，人工智能驱动的算法和 Drug-GAN 模型将能够应对两个挑战：并可能彻底改变未来的药物发现。大麻素受体 CB1R 和 CB2R 是一种用于实验评估的理想模型目标系统。该提案有四个目标。在目标 1 中，我们将开发IPSF来专门设计CB1R或CB2R的激动剂或拮抗剂，以及激动剂可以激活特定的信号通路。那些特定目标、特定功能和信号通路特异性 IPSF 将实现有偏差的配体设计。在目标 2 中，我们将开发药物- GAN 模型合理设计新颖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 Drug-GAN 的热门产品设计化合物，并进行结合和功能测定以验证预测。在目标 4，我们将开发一个名为 PBLD 的可扩展计算平台来集成开发了 IPSF 模型和 Drug-GAN 生成的类药化学品，并推出了网络工具和 API 使用开发的 IPSF 模型进行有偏配体设计，并使用开发的 Drug-GAN 模型。我们估计 IPSF 和 Drug-GAN 模型可以根据 ChEMBL 最近的统计分析生成约 300 个药物靶标数据库。 PBLD 有潜力成为偏向配体设计的国家资源越来越多的 IPSF 和 Drug-GAN 模型应用于 PBLD。