Centre for Misfolding Diseases, Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, United Kingdom.
College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China.
J Chem Theory Comput. 2023 Jul 25;19(14):4701-4710. doi: 10.1021/acs.jctc.2c01303. Epub 2023 Mar 20.
The high attrition rate in drug discovery pipelines is an especially pressing issue for Parkinson's disease, for which no disease-modifying drugs have yet been approved. Numerous clinical trials targeting α-synuclein aggregation have failed, at least in part due to the challenges in identifying potent compounds in preclinical investigations. To address this problem, we present a machine learning approach that combines generative modeling and reinforcement learning to identify small molecules that perturb the kinetics of aggregation in a manner that reduces the production of oligomeric species. Training data were obtained by an assay reporting on the degree of inhibition of secondary nucleation, which is the most important mechanism of α-synuclein oligomer production. This approach resulted in the identification of small molecules with high potency against secondary nucleation.
药物发现管道中的高淘汰率是帕金森病面临的一个特别紧迫的问题，因为目前还没有批准任何能够改变疾病进程的药物。针对α-突触核蛋白聚集的许多临床试验都失败了，至少部分原因是在临床前研究中识别有效化合物存在挑战。为了解决这个问题，我们提出了一种机器学习方法，将生成建模和强化学习相结合，以识别能够改变聚集动力学从而减少寡聚体产生的小分子。训练数据是通过一种报告次级成核抑制程度的测定方法获得的，这是α-突触核蛋白寡聚体产生的最重要机制。这种方法确定了一些对次级成核具有高抑制活性的小分子。
