Molecular Modeling Laboratory, Institute for Drug Research, The Hebrew University of Jerusalem, Jerusalem, 91120, Israel.
Institute of Applied Research, Galilee Society, Shefa-Amr, 20200, Israel.
Sci Rep. 2019 Jan 31;9(1):1106. doi: 10.1038/s41598-019-38508-8.
PPAR-δ agonists are known to enhance fatty acid metabolism, preserving glucose and physical endurance and are suggested as candidates for treating metabolic diseases. None have reached the clinic yet. Our Machine Learning algorithm called "Iterative Stochastic Elimination" (ISE) was applied to construct a ligand-based multi-filter ranking model to distinguish between confirmed PPAR-δ agonists and random molecules. Virtual screening of 1.56 million molecules by this model picked ~2500 top ranking molecules. Subsequent docking to PPAR-δ structures was mainly evaluated by geometric analysis of the docking poses rather than by energy criteria, leading to a set of 306 molecules that were sent for testing in vitro. Out of those, 13 molecules were found as potential PPAR-δ agonist leads with EC between 4-19 nM and 14 others with EC below 10 µM. Most of the nanomolar agonists were found to be highly selective for PPAR-δ and are structurally different than agonists used for model building.
过氧化物酶体增殖物激活受体-δ（PPAR-δ）激动剂已知可增强脂肪酸代谢，维持血糖和体力耐力，被认为是治疗代谢疾病的候选药物。但目前尚无药物进入临床应用。我们的机器学习算法“迭代随机消除（ISE）”被用于构建基于配体的多滤器排序模型，以区分已确认的 PPAR-δ 激动剂和随机分子。通过该模型对 156 万个分子进行虚拟筛选，挑选出约 2500 个排名靠前的分子。随后，主要通过对接构象的几何分析而非能量标准对 PPAR-δ 结构进行对接评估，得到了 306 个分子，这些分子被送去进行体外测试。其中，有 13 个分子被发现具有潜在的 PPAR-δ 激动剂特性，EC50 值在 4-19 nM 之间，另有 14 个分子的 EC50 值低于 10 μM。大多数纳摩尔激动剂对 PPAR-δ 具有高度选择性，并且在结构上与用于构建模型的激动剂不同。
