Lewis Mervin, Alexey Voronov, Mikhail Kabeshov, Ola Engkvist
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引用次数: 0
摘要
在 "设计-制造-测试-分析"(DMTA)药物设计周期中,越来越多地采用机器学习(ML)和深度学习(DL)方法来预测小分子的分子特性。尽管如此,只有少数自动化软件包可以帮助开发和部署这些模型,同时还支持不确定性估计、模型可解释性以及模型使用的其他关键方面。这是该领域尚未满足的一个关键需求,而大量的分子表征和算法(以及相关参数)意味着要稳健地优化、评估、复制和部署模型并非易事。在此,我们介绍 QSARtuna,这是一个用 Python 编写的分子性质预测建模管道,它利用 Optuna、Scikit-learn、RDKit 和 ChemProp 软件包,实现了分子表征与机器学习模型之间的高效自动比较。该平台的开发考虑了日益重要的模型不确定性量化和可解释性设计。我们将详细介绍我们的框架,并举例说明该软件在应用于简单分子特性、反应/活性预测和 DNA 编码文库富集分类时的能力。我们希望 QSARtuna 的发布能进一步推动自动 ML 建模的创新,并为分子性质建模的最佳实践教育提供一个平台。QSARtuna 框架的代码可通过 GitHub 免费获取。
QSARtuna: An Automated QSAR Modeling Platform for Molecular Property Prediction in Drug Design.
Machine-learning (ML) and deep-learning (DL) approaches to predict the molecular properties of small molecules are increasingly deployed within the design-make-test-analyze (DMTA) drug design cycle to predict molecular properties of interest. Despite this uptake, there are only a few automated packages to aid their development and deployment that also support uncertainty estimation, model explainability, and other key aspects of model usage. This represents a key unmet need within the field, and the large number of molecular representations and algorithms (and associated parameters) means it is nontrivial to robustly optimize, evaluate, reproduce, and deploy models. Here, we present QSARtuna, a molecule property prediction modeling pipeline, written in Python and utilizing the Optuna, Scikit-learn, RDKit, and ChemProp packages, which enables the efficient and automated comparison between molecular representations and machine learning models. The platform was developed by considering the increasingly important aspect of model uncertainty quantification and explainability by design. We provide details for our framework and provide illustrative examples to demonstrate the capability of the software when applied to simple molecular property, reaction/reactivity prediction, and DNA encoded library enrichment classification. We hope that the release of QSARtuna will further spur innovation in automatic ML modeling and provide a platform for education of best practices in molecular property modeling. The code for the QSARtuna framework is made freely available via GitHub.
期刊介绍:
The Journal of Chemical Information and Modeling publishes papers reporting new methodology and/or important applications in the fields of chemical informatics and molecular modeling. Specific topics include the representation and computer-based searching of chemical databases, molecular modeling, computer-aided molecular design of new materials, catalysts, or ligands, development of new computational methods or efficient algorithms for chemical software, and biopharmaceutical chemistry including analyses of biological activity and other issues related to drug discovery.
Astute chemists, computer scientists, and information specialists look to this monthly’s insightful research studies, programming innovations, and software reviews to keep current with advances in this integral, multidisciplinary field.
As a subscriber you’ll stay abreast of database search systems, use of graph theory in chemical problems, substructure search systems, pattern recognition and clustering, analysis of chemical and physical data, molecular modeling, graphics and natural language interfaces, bibliometric and citation analysis, and synthesis design and reactions databases.