Retro Drug Design: From Target Properties to Molecular Structures.
Retro Drug Design: From Target Properties to Molecular Structures.
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复古药物设计：从目标特性到分子结构。
DOI：
10.1101/2021.05.11.442656
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Sun,Hongmao
中科院分区：
文献类型：
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作者：
Wang,Yuhong;Michael,Sam;Huang,Ruili;Zhao,Jinghua;Recabo,Katlin;Bougie,Danielle;Shu,Qiang;Shinn,Paul;Sun,Hongmao
更快、更经济地为更多的患者提供更多的治疗药物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，与药物发现中其他强大的计算方法相结合，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，复古药物设计，或RDD，从零开始创造新的小分子药物，以满足多种预定义的要求，包括针对药物靶标的生物活性和最佳的物理化学和ADMET性质范围。分子结构由基于原子分类的分子描述符系统optATP表示，并通过主成分分析将其转化为负载向量空间。使用optATP和浅层机器学习方法，根据实验数据对传统预测模型进行目标属性的训练。然后利用蒙特卡罗抽样算法在具有目标属性的加载向量空间中寻找解。最后，使用深度学习模型从溶液中解码分子结构。为了测试算法的可行性，我们挑战RDD从同时优化了五种不同admet特性的随机数中生成新的激酶抑制剂。生成的有效结构与可用的4,314个激酶抑制剂之间的最佳田本相似性分数为<；0.50，表明生成的化合物具有很高的新颖性。从满足所有6个目标性质的3,040个结构中，选择20个用于合成和实验测量97个代表性的激酶和admet性质的抑制活性。分别有15种化合物和8种化合物被确定为Hit或Strong Hits。六种强激酶抑制剂中有五种具有优异的实验ADMET性能。本文提出的结果表明，RDD具有显著改善当前药物发现过程的潜力。
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.