Finally, model finetuning on molecule odor prediction was investigated. Similar to toxicity, perceived fragrance is highly dependent upon molecule structure, with most humans being able to distinguish between select enantiomers. However, odor is, by definition, perceived odor and the current mechanism by which olfactive detection and recognition occurs is still opaque. Indeed, it is well known that two individuals may interpret a molecule's fragrance differently or be anosmic to a given molecule (unable to smell the molecule). Thus, the finetuning task is not a regression, but a multilabel multiclass prediction. Multilabel refers to the fact that there are many possible odors for a molecule, and whilst there is no universal definition of fragrance classes, there are generally accepted realms: sweet vs herbaceous vs floral. Multiclass indicates the possibility that a molecule may have multiple possible olfactive notes; a molecule may smell sweet and buttery, not just one or the other. It can be observed from the fragrance distribution of the dataset that many molecules offer a fruity component (Figure ) which may itself be accompanied by a specific fruit, such as melon or blackcurrant (Figure ). have not yet made their code nor their dataset available for benchmarking. As such, I used two standard multilabel classification baselines: Random Forest and K-Nearest Neighbors, as the some of the previous baselines, Adaboost and Gaussian Process, are unable to work with multilabel classification. Random Forest models have been noted previously for their excellence in machine olfaction. Small molecule fragrance data was obtained from the Pyrfume project, an open-source database of pre-processed, literature-curated olfactive data. This dataset consisted of 3,502 small molecules each with a corresponding label which could be any number of combinations from the 113 odor classes. Interestingly, a minority of molecules in this dataset had no detectable fragrance (Figure ). A cross validation (5-fold) split was performed, where each molecule in the test split was an unseen molecule. Unlike yield and LD50 prediction, which were regression tasks, classification was to be performed and thus, the F-score classification metric was utilized. F-scores are typically used for binary classifications but the multiclass extension of the F-score has been developed. Briefly, the F-score for each class is computed before being averaged. The averaging can be unweighted (macro) or weighted by class size (weighted). For F-scores, higher values indicate better model performance. With Random Forest and K-Nearest Neighbor baselines, relatively low F-scores were observed, highlighting the challenge of predicting reasonable olfactive notes from structurally different molecules. However, my crystal structure foundational model combined with an olfactive-specific feedforward neural network, Crystal-Olfaction, observed a generous increase in both weighted and unweighted F-scores to 0.62 and 0.92 (Table ). One notable challenge for all networks was the prediction of non-fragrant molecules, which were all incorrectly assigned with multiple odor labels. This may be due to a dataset bias of few non-fragrant molecules or it is possible that these molecules have an olfactive profile, but the volatility of these compounds is so low, that they are not detectable to most humans. I believe this to be an interesting future direction for further exploration of this modular framework.