摘要
一位新闻记者-机器人与机器学习的工作人员新闻编辑每日新闻-人工智能的新研究是一篇报道的主题。根据NewsRx记者在加州伯克利的新闻报道，研究表明：“确定一种新药物分子的生存能力是一项时间和资源密集型的任务，这使得计算机辅助评估成为快速发现药物的重要途径。在这里，我们开发了一种机器学习算法iMiner，它将深度强化学习与使用AutoDock Vina的实时3D分子识别相结合，为目标蛋白生成新的抑制剂分子。”"从而同时创造出限制分子形状和与靶活性位点的分子相容性的化学新颖性."新闻记者引用了加州大学的一句话，“此外，通过使用各种类型的奖励函数，我们在新分子的生成任务中引入了新的东西，例如与目标配体的化学相似性，从已知的蛋白质结合片段中生长的分子，iMiner算法嵌入一个复合工作流程中，过滤掉泛分析干扰化合物，违反Lipinski规则，药物化学中的常见结构，而且合成可及性较差，有与其他对接评分函数交叉验证的选项，以及测量姿态稳定性的分子动力学模拟自动化。我们还允许用户定义一套他们希望在训练过程和后过滤步骤中排除的结构规则。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Artificial Intelligenc e is the subject of a report. According to news reporting from Berkeley, Califor nia, by NewsRx journalists, research stated, "Determining the viability of a new drug molecule is a time- and resource-intensive task that makes computer-aided assessments a vital approach to rapid drug discovery. Here we develop a machine learning algorithm, iMiner, that generates novel inhibitor molecules for target proteins by combining deep reinforcement learning with realtime 3D molecular do cking using AutoDock Vina, thereby simultaneously creating chemical novelty whil e constraining molecules for shape and molecular compatibility with target activ e sites." The news correspondents obtained a quote from the research from the University o f California, "Moreover, through the use of various types of reward functions, w e have introduced novelty in generative tasks for new molecules such as chemical similarity to a target ligand, molecules grown from known protein bound fragmen ts, and creation of molecules that enforce interactions with target residues in the protein active site. The iMiner algorithm is embedded in a composite workflo w that filters out Pan-assay interference compounds, Lipinski rule violations, u ncommon structures in medicinal chemistry, and poor synthetic accessibility with options for cross-validation against other docking scoring functions and automa tion of a molecular dynamics simulation to measure pose stability. We also allow users to define a set of rules for the structures they would like to exclude du ring the training process and postfiltering steps."
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