National Center for Advancing Translational Sciences (NCATS), 9800 Medical Center Drive, Rockville, Maryland 20850, United States.
J Chem Inf Model. 2022 Jun 13;62(11):2659-2669. doi: 10.1021/acs.jcim.2c00123. Epub 2022 Jun 2.
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.
将更多的疗法更快、更经济地提供给更多的患者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，结合药物发现中的其他强大计算方法，使得这一目标比以往任何时候都更加切实可行。在这里，我们描述了一种新的策略，即反向药物设计（retro drug design，RDD），用于从头开始创建新的小分子药物，以满足多个预定义的要求，包括对药物靶点的生物活性和最佳范围的理化和 ADMET 特性。分子结构由基于原子类型的分子描述符系统 optATP 表示，该系统进一步通过主成分分析转换到加载向量空间。使用 optATP 和浅层机器学习方法，基于实验数据针对目标特性对传统预测模型进行了训练。然后，利用蒙特卡罗抽样算法在加载向量空间中找到具有目标特性的解决方案。最后，使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，我们使用 RDD 从随机数生成具有同时优化的五种不同 ADMET 特性的新型激酶抑制剂。生成的有效结构与可用的 4314 种激酶抑制剂之间的最佳 Tanimoto 相似性得分<0.50，表明生成化合物的新颖程度很高。从满足所有六个目标特性的 3040 个结构中，选择了 20 个进行合成和实验测量，以评估对 97 种代表性激酶和 ADMET 特性的抑制活性。其中 15 个和 8 个化合物分别被确定为命中或强命中。五个强激酶抑制剂中的六个具有出色的实验 ADMET 特性。本文介绍的结果表明，RDD 有可能显著改善当前的药物发现过程。
