DataSheet1_PCW-A1001, AI-assisted de novo design approach to design a selective inhibitor for FLT-3(D835Y) in acute myeloid leukemia.docx

Treating acute myeloid leukemia (AML) by targeting FMS-like tyrosine kinase 3 (FLT-3) is considered an effective treatment strategy. By using AI-assisted hit optimization, we discovered a novel and highly selective compound with desired drug-like properties with which to target the FLT-3 (D835Y) mutant. In the current study, we applied an AI-assisted de novo design approach to identify a novel inhibitor of FLT-3 (D835Y). A recurrent neural network containing long short-term memory cells (LSTM) was implemented to generate potential candidates related to our in-house hit compound (PCW-1001). Approximately 10,416 hits were generated from 20 epochs, and the generated hits were further filtered using various toxicity and synthetic feasibility filters. Based on the docking and free energy ranking, the top compound was selected for synthesis and screening. Of these three compounds, PCW-A1001 proved to be highly selective for the FLT-3 (D835Y) mutant, with an IC50 of 764 nM, whereas the IC50 of FLT-3 WT was 2.54 μM.

针对急性髓系白血病（AML）的治疗策略，以靶向FMS样酪氨酸激酶3（FLT-3）为关键，已被视为一种有效的治疗手段。通过采用AI辅助的命中率优化技术，我们成功发现了一种新颖且高度选择性的化合物，该化合物具备期望的药物特性，并针对FLT-3（D835Y）突变体。在本研究中，我们运用AI辅助的从头设计方法，旨在识别FLT-3（D835Y）的新型抑制剂。我们实现了一个包含长短期记忆单元（LSTM）的循环神经网络，以生成与我们内部命中率化合物（PCW-1001）相关的潜在候选物。经过20个epoch的生成，共产生了大约10,416个命中率化合物。这些化合物随后通过多种毒性和合成可行性过滤器进行了进一步的筛选。基于对接和自由能排名，我们选取了排名最高的化合物进行合成和筛选。在这三种化合物中，PCW-A1001表现出对FLT-3（D835Y）突变体的高度选择性，其半数抑制浓度（IC50）为764 nM，而FLT-3野生型的IC50为2.54 μM。