Elucidating Fingerprints – Towards a Holistic Explanatory Toolbox for Molecular Machine Learning

阐明指纹 â 走向分子机器学习的整体解释工具箱

• 批准号: 497089464
• 负责人: Professor Dr. Frank Glorius
• 金额: --
• 依托单位: Institut für Informatik
• 依托单位国家: 德国
• 项目类别: Priority Programmes
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/497089464?language=en
• 关键词: Elucidating; Fingerprints; Towards; Holistic; Explanatory

The central point of this proposal is the development of out-of-the-box for interpretable and Explainable Molecular Machine Learning on a structural level. Within this project broadly utilized molecular representations will be developed, adapted and used to train highly robust but accurate models (e.g. Gradient Boost algorithms). Starting from these models an open-source software pipeline will be employed to map feature importance, influence, interdependencies, as well as model confidences back to the molecular structure giving trained chemists a plain handle for molecular and reaction design. An important part of this work will involve the development of visualization based on analytic results that provide a high degree of accuracy on the one hand and are easy to understand for any scientist working in the field of molecular science on the other hand. Those tools shall be usable to investigate and improve underlaying datasets as well as for molecular design. In addition to the coloration and visualization of individual molecules, methods of statistical evaluation regarding the general influence of functional groups should be developed, so that rules for further reaction design can be derived. Finally, these rules should be used in the laboratory to validate the explanatory methods developed within the course of this proposal. By these objectives the proposal aims on fulfilling the following of the PPs general goals: “Application of state-of-the-art ML algorithms – Explainable AI”, “Development of (domain specific) molecular representations – Generally improved molecular representations” and “Prediction, understanding and interpretation of molecular properties – Improvement of current applications”. Within this scope a high focus lies on the interpretation and explanation models for quantitative yield prediction to find handles for a systematic improvement within this underdeveloped area of MML which also has defined as a major topic of this PP.

这项提议的中心点是在结构水平上开发可解释和可解释的分子机器学习的开箱即用的方法。在该项目中，将开发、调整和使用广泛使用的分子表示法来训练高度稳健但精确的模型(例如，梯度增强算法)。从这些模型开始，将使用开放源码软件管道将特征的重要性、影响、相互依赖以及模型置信度映射回分子结构，使训练有素的化学家对分子和反应设计有一个简单的把握。这项工作的一个重要部分将涉及基于分析结果的可视化开发，这些分析结果一方面提供高度的准确性，另一方面对于在分子科学领域工作的任何科学家来说都很容易理解。这些工具应可用于调查和改进基础数据集以及分子设计。除了单个分子的显色和可视化之外，还应该开发关于官能团的一般影响的统计评估方法，以便为进一步的反应设计得出规则。最后，应在实验室中使用这些规则来验证在本提案过程中开发的解释方法。通过这些目标，该提案旨在实现PPS的以下总体目标：“应用最先进的ML算法--可解释的人工智能”、“发展(领域特定的)分子表示法--普遍改进的分子表示法”和“预测、理解和解释分子特性--改进当前的应用”。在这一范围内，重点放在定量产量预测的解释和解释模型上，以找到在MML这一不发达领域系统改进的方法，这也被定义为本PP的一个主要主题。