AI-Powered Biased Ligand Design

AI驱动的偏置配体设计 一个偏向性配体，它能激发某种细胞信号，但不影响其他途径， 有吸引力的候选药物，因为它可以最大限度地减少不必要的或不利的影响。不幸的是， 目前的计算机辅助药物设计方法能够进行偏向的配体设计。而且 迫切需要为那些非常有前途的药物靶点扩展可药用化学空间 已经开发出了很多有效的配体，但不幸的是，还没有批准的药物。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 相互作用谱评分函数模型，以实现有偏配体设计，以及Drug-GAN模型 实现从头化学结构设计。本申请的中心假设是 由配体引发的功能和信号传导途径都是在配体中编码的， 残基相互作用谱（IP），机器学习算法可以学习的关键属性 IP和生成的评分函数，创造IPSF，以识别筛选中的相似配体 图书馆本申请的第二个假设指出，生成对抗网络 (GAN)可以从输入中学习化学模式，并重新设计新的化学结构。 因此，人工智能算法和Drug-GAN模型将能够解决这两个挑战， 并可能彻底改变未来的药物发现。大麻素受体，CB 1 R和CB 2 R，是一种 理想模型指标体系。该提案有四个目标。目标1： 将开发IPSF以专门设计CB 1 R或CB 2 R的激动剂或拮抗剂， 可以激活某种信号通路这些具体目标、具体职能和 信号传导途径特异性IPSF将使偏向配体设计成为可能。在目标2中，我们将开发药物- GAN模型，以合理设计新的化学结构作为潜在的激动剂或拮抗剂， CB 1 R和CB 2 R。在目标3中，我们将获得筛选化合物和Drug-GAN的热门结果 设计的化合物，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， 开发了IPSF模型和Drug-GAN生成的类药物化学品，并推出网络工具， 使用开发的IPSF模型进行有偏配体设计的API和使用 开发的Drug-GAN模型我们估计IPSF和Drug-GAN模型可以 基于最近对ChEMBL的统计分析， 数据库PBLD有可能成为偏向配体设计的国家资源， 越来越多的IPSF和Drug-GAN模型被应用到PBLD。

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.