The advancement of machine learning and the availability of large-scale reaction datasets have accelerated the development of data-driven models for computer-aided synthesis planning (CASP) in the past decade. Here, we detail the newest version of ASKCOS, an open source software suite for synthesis planning that makes available several research advances in a freely available, practical tool. Four one-step retrosynthesis models form the basis of both interactive planning and automatic planning modes. Retrosynthetic planning is complemented by other modules for feasibility assessment and pathway evaluation, including reaction condition recommendation, reaction outcome prediction, and auxiliary capabilities such as solubility prediction and quantum mechanical descriptor prediction. ASKCOS has assisted hundreds of medicinal, synthetic, and process chemists in their day-to-day tasks, complementing expert decision making. It is our belief that CASP tools like ASKCOS are an important part of modern chemistry research, and that they offer ever-increasing utility and accessibility.

翻译：过去十年中，机器学习的进步和大规模反应数据集的可用性加速了计算机辅助合成规划（CASP）中数据驱动模型的发展。本文详细介绍了ASKCOS的最新版本，这是一个用于合成规划的开源软件套件，它将多项研究进展集成于一个免费提供的实用工具中。四个单步逆合成模型构成了交互式规划和自动规划模式的基础。逆合成规划通过其他用于可行性评估和路径评价的模块得到补充，包括反应条件推荐、反应结果预测以及辅助功能，如溶解度预测和量子力学描述符预测。ASKCOS已协助数百名药物化学家、合成化学家和工艺化学家完成日常任务，补充了专家决策。我们相信，像ASKCOS这样的CASP工具是现代化学研究的重要组成部分，并且它们正提供着日益增长的实用性和可及性。