Multimodal Machine Learning Models on Aqueous Direct Photodisappearance Rate Constants of Chemicals Exhibit Previously Unattainable Performance.

Direct photodisappearance rate constant (kd) is a key parameter determining the persistence of chemical pollutants in surface waters. High-throughput in silico models are preferable to tedious and expensive experimental tests for obtaining kd values of multitudinous chemicals. Previous models mainly relied on molecular descriptors as predictor variables for kd prediction, neglecting environmental conditions that have great impacts on direct photodisappearance kinetics, resulting in models with limited applicability. This study collected and curated a comprehensive data set consisting of 1281 records of kd values for 304 chemicals. Multimodal machine learning models that simultaneously consider molecular structures and environmental conditions, such as light source, pH, temperature, dissolved oxygen level, and initial concentration of chemicals, were constructed accordingly. Results indicate that the multimodal models significantly improved the coefficient of determination on the validation set (from 0.369 to 0.756) over the unimodal models. An innovative applicability domain (AD) characterization based on feature-response landscape analysis was proposed to define the ADs of the models. The models, coupled with the AD characterization, serve as an effective tool for assessing the environmental photochemical persistence of chemicals, supporting sound chemical management.