Distance-Aware Molecular Property Prediction in Nonlinear Structure–Property Space

IF 5.3 2区 化学 Q1 CHEMISTRY, MEDICINAL
Jae Young Kim,  and , Dionisios G. Vlachos*, 
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Abstract

Molecular property prediction with limited data in novel chemical domains remains challenging. We introduce an approach based on the hypothesis that prediction difficulty increases systematically with distance from well-characterized regions in an appropriately defined structure–property space. Our framework combines nonlinear structure–property space embedding with distance-aware domain classification and uncertainty quantification. We create a structure–property embedding connecting molecular similarity with prediction difficulty, implement distance-aware classification balancing precision and true positive rate, and provide distance-based uncertainty estimates scaled by molecular similarity. Across four ecotoxicity data sets, our local models reduced root mean squared error by 28–48% for truly in-domain molecules compared to global models, with strong correlations (r = 0.40–0.62) between distance and prediction error. In a biolubricant base oil property application, our approach reduced prediction error by 29% compared to a global model and outperformed transfer learning and standard machine learning approaches. This framework’s focus on relevant domains and distance-calibrated uncertainty estimates for limited, heterogeneous chemical data makes it broadly applicable across applications, such as toxicity prediction, drug discovery, and materials design.

Abstract Image

非线性结构-性质空间中距离感知分子性质预测。
在新的化学领域,利用有限的数据进行分子性质预测仍然具有挑战性。我们引入了一种基于假设的预测方法,该假设认为,在一个适当定义的结构-属性空间中,预测难度随着距离特征良好的区域的距离而系统地增加。该框架将非线性结构属性空间嵌入与距离感知域分类和不确定性量化相结合。我们建立了连接分子相似度和预测难度的结构属性嵌入,实现了距离感知分类平衡精度和真阳性率,并提供了基于距离的不确定性估计。在四个生态毒性数据集中,与全局模型相比,我们的局部模型将真正区域内分子的均方根误差降低了28-48%,距离和预测误差之间具有很强的相关性(r = 0.40-0.62)。在生物润滑剂基础油性能应用中,与全局模型相比,我们的方法将预测误差降低了29%,并且优于迁移学习和标准机器学习方法。该框架的重点是相关领域和距离校准的不确定性估计有限,异质化学数据使其广泛适用于各种应用,如毒性预测,药物发现和材料设计。
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来源期刊
CiteScore
9.80
自引率
10.70%
发文量
529
审稿时长
1.4 months
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.
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