Lu Wenchao, Zhang Rukang, Jiang Hao, Zhang Huimin, Luo Cheng
Drug Discovery and Design Center, CAS Key Laboratory of Receptor Research, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China.
Department of Pharmacy, University of Chinese Academy of Sciences, Beijing, China.
Front Chem. 2018 Mar 12;6:57. doi: 10.3389/fchem.2018.00057. eCollection 2018.
Epigenetic dysfunction has been widely implicated in several diseases especially cancers thus highlights the therapeutic potential for chemical interventions in this field. With rapid development of computational methodologies and high-performance computational resources, computer-aided drug design has emerged as a promising strategy to speed up epigenetic drug discovery. Herein, we make a brief overview of major computational methods reported in the literature including druggability prediction, virtual screening, homology modeling, scaffold hopping, pharmacophore modeling, molecular dynamics simulations, quantum chemistry calculation, and 3D quantitative structure activity relationship that have been successfully applied in the design and discovery of epi-drugs and epi-probes. Finally, we discuss about major limitations of current virtual drug design strategies in epigenetics drug discovery and future directions in this field.
表观遗传功能障碍已被广泛认为与多种疾病尤其是癌症有关，因此凸显了该领域化学干预的治疗潜力。随着计算方法和高性能计算资源的快速发展，计算机辅助药物设计已成为加速表观遗传药物发现的一种有前景的策略。在此，我们简要概述文献中报道的主要计算方法，包括成药性预测、虚拟筛选、同源建模、骨架跃迁、药效团建模、分子动力学模拟、量子化学计算以及三维定量构效关系，这些方法已成功应用于表观遗传药物和表观遗传探针的设计与发现。最后，我们讨论了当前虚拟药物设计策略在表观遗传药物发现中的主要局限性以及该领域的未来方向。
