Department of Chemistry and Applied Biosciences, RETHINK, ETH Zurich, Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland.
ChemMedChem. 2019 Jun 18;14(12):1129-1134. doi: 10.1002/cmdc.201900097. Epub 2019 May 16.
A virtual screening protocol based on machine learning models was used to identify mimetics of the natural product (-)-galantamine. This fully automated approach identified eight compounds with bioactivities on at least one of the macromolecular targets of (-)-galantamine, with different polypharmacological profiles. Two of the computer-generated hits possess an expanded spectrum of bioactivity on targets relevant to the treatment of Alzheimer's disease and are suitable for hit-to-lead expansion. These results advocate multitarget drug design by advanced virtual screening protocols based on chemically informed machine learning models.
采用基于机器学习模型的虚拟筛选方案来鉴定天然产物(-)-加兰他敏类似物。这种全自动方法鉴定出了 8 种化合物，它们在(-)-加兰他敏的至少一个大分子靶标上具有生物活性，具有不同的多药理学特性。计算机生成的两个命中化合物在与治疗阿尔茨海默病相关的靶标上具有更广泛的生物活性谱，适合进行从命中化合物到先导化合物的扩展。这些结果提倡通过基于化学信息的机器学习模型的先进虚拟筛选方案进行多靶标药物设计。
