Jang Seong Hun, Sivakumar Dakshinamurthy, Mudedla Sathish Kumar, Choi Jaehan, Lee Sungmin, Jeon Minjun, Bvs Suneel Kumar, Hwang Jinha, Kang Minsung, Shin Eun Gyeong, Lee Kyu Myung, Jung Kwan-Young, Kim Jae-Sung, Wu Sangwook
R&D Center, PharmCADD, Busan, South Korea.
Division of Radiation Biomedical Research, Korea Institute of Radiological and Medical Sciences, Seoul, South Korea.
Front Mol Biosci. 2022 Nov 25;9:1072028. doi: 10.3389/fmolb.2022.1072028. eCollection 2022.
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 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 IC of 764 nM, whereas the IC of FLT-3 WT was 2.54 μM.
通过靶向FMS样酪氨酸激酶3（FLT-3）治疗急性髓系白血病（AML）被认为是一种有效的治疗策略。通过使用人工智能辅助的命中优化方法，我们发现了一种具有理想类药物性质的新型高选择性化合物，用于靶向FLT-3（D835Y）突变体。在本研究中，我们应用人工智能辅助设计方法来鉴定一种新型的FLT-3（D835Y）抑制剂。实施了一个包含长短期记忆细胞（LSTM）的递归神经网络，以生成与我们内部的命中化合物（PCW-1001）相关的潜在候选物。从20个轮次中产生了约10416个命中物，并使用各种毒性和合成可行性筛选器对产生的命中物进行进一步筛选。基于对接和自由能排名，选择顶级化合物进行合成和筛选。在这三种化合物中，PCW-A1001被证明对FLT-3（D835Y）突变体具有高度选择性，IC为764 nM，而FLT-3野生型的IC为2.54 μM。
