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Artificial intelligence (AI) and machine learning (ML) are expanding in popularity for broad applications to challenging tasks in chemistry and materials science. Examples include the prediction of properties, the discovery of new reaction pathways, or the design of new molecules. The machine needs to read and write fluently in a chemical language for each of these tasks. Strings are a common tool to represent molecular graphs, and the most popular molecular string representation, Smiles, has powered cheminformatics since the late 1980s. However, in the context of AI and ML in chemistry, Smiles has several shortcomings-most pertinently, most combinations of symbols lead to invalid results with no valid chemical interpretation. To overcome this issue, a new language for molecules was introduced in 2020 that guarantees 100% robustness: SELF-referencing embedded string (Selfies). Selfies has since simplified and enabled numerous new applications in chemistry. In this perspective, we look to the future and discuss molecular string representations, along with their respective opportunities and challenges. We propose 16 concrete future projects for robust molecular representations. These involve the extension toward new chemical domains, exciting questions at the interface of AI and robust languages, and interpretability for both humans and machines. We hope that these proposals will inspire several follow-up works exploiting the full potential of molecular string representations for the future of AI in chemistry and materials science.

Background: Patients cannot always share all necessary relevant information with doctors during medical consultations. Regardless, in order to ensure the best quality consultation and care, it is imperative that a doctor clearly understands each patient's agenda. The purpose of this study was to analyze the process of developing a shared-agenda during family physician consultations in Japan. Methods: We interviewed 15 first time patients visiting the outpatient clinic of the Department of Family Medicine in the hospital chosen for the investigation, and the 8 family physicians who examined them. In total we observed 16 consultations. We analyzed both patients' and doctors' narratives using a modified grounded theory approach. Results: For patients, we found four main factors that influenced the process of making a shared-agenda: past medical experiences, undisclosed but relevant information, relationship with the family physician, and the patient's own explanatory model. In addition, we found five factors that influenced the shared agenda making process for family physicians: understanding the patient's explanatory model, constructing the patient-doctor relationship, physical examination centered around the patient's explanatory model, discussion-styled explanation, and self-reflection on action. Conclusions: The findings suggest that patient satisfaction would be increased if family physicians are proactive in considering these factors with respect to both the patient's agenda, and their own. [ABSTRACT FROM AUTHOR]