Two neural network models were trained to predict the Thermal Stability Score, a scalar representing the relative rank order of alkane half-lives, using a modified hinge loss function that converts Using a random training:testing split, the elementary MLP and message-passing Chemprop architectures show comparable performance (Fig. ). Although random splitting ensures that the testing set is structurally independent of the training data, the diversity of the training data is sufficient to guarantee that the branching and size distributions of the training and testing data obtained from the random splitting are similar. To provide a more rigorous test of transferability, four other case-studies were performed using distinct training and testing splits: (Fig. -ii) Total Branches ≤ 6: alkanes with at most six total branches for training, with the rest in the testing split, resulting in a 90:10 train:test split; (Fig. -iii) Core Branches ≤ 4: alkanes with at most four core branches for training, with the rest in the testing split, resulting in a 61:39 train:test split; (Fig. -iv) Backbone ≤ 10: alkanes with a backbone at most ten carbons long for training, with the rest for testing split, resulting in a 68:32 train:test split; and (Fig. ) Length ≤ 10: alkanes with at most 16 carbons for training, with the C17 alkanes in the testing split, resulting in a 47:53 train:test split. Here, backbone refers to the longest chain in the alkane structure. Core branches refers to the number of branches originating from the backbone, whereas total branches refer to all branches and sub-branches from the backbone.