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Retro Drug Design: From Target Properties to Molecular Structures
Journal of Chemical Information and Modeling ( IF 5.6 ) Pub Date : 2022-06-02 , DOI: 10.1021/acs.jcim.2c00123
Yuhong Wang 1 , Sam Michael 1 , Shyh-Ming Yang 1 , Ruili Huang 1 , Kennie Cruz-Gutierrez 1 , Yaqing Zhang 1 , Jinghua Zhao 1 , Menghang Xia 1 , Paul Shinn 1 , Hongmao Sun 1
Affiliation  

To deliver more therapeutics to more patients more quickly and economically is the ultimate goal of pharmaceutical researchers. The advent and rapid development of artificial intelligence (AI), in combination with other powerful computational methods in drug discovery, makes this goal more practical than ever before. Here, we describe a new strategy, retro drug design, or RDD, to create novel small-molecule drugs from scratch to meet multiple predefined requirements, including biological activity against a drug target and optimal range of physicochemical and ADMET properties. The molecular structure was represented by an atom typing based molecular descriptor system, optATP, which was further transformed to the space of loading vectors from principal component analysis. Traditional predictive models were trained over experimental data for the target properties using optATP and shallow machine learning methods. The Monte Carlo sampling algorithm was then utilized to find the solutions in the space of loading vectors that have the target properties. Finally, a deep learning model was employed to decode molecular structures from the solutions. To test the feasibility of the algorithm, we challenged RDD to generate novel kinase inhibitors from random numbers with five different ADMET properties optimized at the same time. The best Tanimoto similarity score between the generated valid structures and the available 4,314 kinase inhibitors was < 0.50, indicating a high extent of novelty of the generated compounds. From the 3,040 structures that met all six target properties, 20 were selected for synthesis and experimental measurement of inhibition activity over 97 representative kinases and the ADMET properties. Fifteen and eight compounds were determined to be hits or strong hits, respectively. Five of the six strong kinase inhibitors have excellent experimental ADMET properties. The results presented in this paper illustrate that RDD has the potential to significantly improve the current drug discovery process.

中文翻译:

复古药物设计:从靶点特性到分子结构

更快、更经济地为更多患者提供更多治疗方法是药物研究人员的最终目标。人工智能 (AI) 的出现和快速发展,结合其他强大的药物发现计算方法,使这一目标比以往任何时候都更加实用。在这里,我们描述了一种新的策略,即逆向药物设计或 RDD,从头开始创建新的小分子药物,以满足多个预定义的要求,包括针对药物靶点的生物活性以及物理化学和 ADMET 特性的最佳范围。分子结构由基于原子分型的分子描述符系统 optATP 表示,该系统从主成分分析进一步转换为加载向量空间。传统的预测模型是使用 optATP 和浅层机器学习方法对目标属性的实验数据进行训练的。然后利用蒙特卡罗采样算法在具有目标属性的加载向量空间中找到解决方案。最后,采用深度学习模型从解决方案中解码分子结构。为了测试算法的可行性,我们挑战 RDD 从随机数生成新的激酶抑制剂,同时优化了五种不同的 ADMET 特性。生成的有效结构与可用的 4,314 激酶抑制剂之间的最佳 Tanimoto 相似性得分 < 0.50,表明生成的化合物具有高度的新颖性。从满足所有六个目标属性的 3,040 个结构中,选择 20 个用于合成和实验测量 97 种代表性激酶的抑制活性和 ADMET 特性。15 和 8 种化合物分别被确定为热门或强热门。六种强激酶抑制剂中有五种具有出色的实验 ADMET 特性。本文提出的结果表明,RDD 具有显着改善当前药物发现过程的潜力。
更新日期:2022-06-02
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