Ordinal Confidence Level Assignments for Regression Model Predictions

IF 5.6 2区化学 Q1 CHEMISTRY, MEDICINAL

Journal of Chemical Information and Modeling Pub Date : 2024-12-09 DOI:10.1021/acs.jcim.4c0175510.1021/acs.jcim.4c01755

Steven Kearnes*, and , Patrick Riley*,

引用次数: 0

Abstract

We present a simple method for assigning accurate confidence levels to molecular property predictions from regression models. These confidence levels are easy to interpret and useful for making decisions in drug discovery programs. We demonstrate their performance using time-split validation with assay data from the Relay Therapeutics internal database.

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来源期刊

Journal of Chemical Information and Modeling 化学-化学综合

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.