Recent advances in the Self-Referencing Embedding Strings (SELFIES) library

String-based molecular representations play a crucial role in cheminformatics applications, and with the growing success of deep learning in chemistry, have been readily adopted into machine learning pipelines. However, traditional string-based representations such as SMILES are often prone to syntactic and semantic errors when produced by generative models. To address these problems, a novel representation, SELF-referencIng Embedded Strings (SELFIES), was proposed that is inherently 100% robust, alongside an accompanying open-source implementation. Since then, we have generalized SELFIES to support a wider range of molecules and semantic constraints and streamlined its underlying grammar. We have implemented this updated representation in subsequent versions of \selfieslib, where we have also made major advances with respect to design, efficiency, and supported features. Hence, we present the current status of \selfieslib (version 2.1.1) in this manuscript.Comment: 11 pages, 2 figure

A narrative synthesis of the impact of primary health care delivery models for refugees in resettlement countries on access, quality and coordination

Introduction. Refugees have many complex health care needs which should be addressed by the primary health care services, both on their arrival in resettlement countries and in their transition to long-term care. The aim of this narrative synthesis is to identify the components of primary health care service delivery models for such populations which have been effective in improving access, quality and coordination of care. Methods. A systematic review of the literature, including published systematic reviews, was undertaken. Studies between 1990 and 2011 were identified by searching Medline, CINAHL, EMBASE, Cochrane Library, Scopus, Australian Public Affairs Information Service-Health, Health and Society Database, Multicultural Australian and Immigration Studies and Google Scholar. A limited snowballing search of the reference lists of all included studies was also undertaken. A stakeholder advisory committee and international advisers provided papers from grey literature. Only English language studies of evaluated primary health care models of care for refugees in developed countries of resettlement were included. Results: Twenty-five studies met the inclusion criteria for this review of which 15 were Australian and 10 overseas models. These could be categorised into six themes: service context, clinical model, workforce capacity, cost to clients, health and non-health services. Access was improved by multidisciplinary staff, use of interpreters and bilingual staff, no-cost or low-cost services, outreach services, free transport to and from appointments, longer clinic opening hours, patient advocacy, and use of gender-concordant providers. These services were affordable, appropriate and acceptable to the target groups. Coordination between the different health care services and services responding to the social needs of clients was improved through case management by specialist workers. Quality of care was improved by training in cultural sensitivity and appropriate use of interpreters. Conclusion: The elements of models most frequently associated with improved access, coordination and quality of care were case management, use of specialist refugee health workers, interpreters and bilingual staff. These findings have implications for workforce planning and training

A systematic policy approach to changing the food system and physical activity environments to prevent obesity

As obesity prevention becomes an increasing health priority in many countries, including Australia and New Zealand, the challenge that governments are now facing is how to adopt a systematic policy approach to increase healthy eating and regular physical activity. This article sets out a structure for systematically identifying areas for obesity prevention policy action across the food system and full range of physical activity environments. Areas amenable to policy intervention can be systematically identified by considering policy opportunities for each level of governance (local, state, national, international and organisational) in each sector of the food system (primary production, food processing, distribution, marketing, retail, catering and food service) and each sector that influences physical activity environments (infrastructure and planning, education, employment, transport, sport and recreation). Analysis grids are used to illustrate, in a structured fashion, the broad array of areas amenable to legal and regulatory intervention across all levels of governance and all relevant sectors. In the Australian context, potential regulatory policy intervention areas are widespread throughout the food system, e.g., land-use zoning (primary production within local government), food safety (food processing within state government), food labelling (retail within national government). Policy areas for influencing physical activity are predominantly local and state government responsibilities including, for example, walking and cycling environments (infrastructure and planning sector) and physical activity education in schools (education sector). The analysis structure presented in this article provides a tool to systematically identify policy gaps, barriers and opportunities for obesity prevention, as part of the process of developing and implementing a comprehensive obesity prevention strategy. It also serves to highlight the need for a coordinated approach to policy development and implementation across all levels of government in order to ensure complementary policy action

SELFIES and the future of molecular string representations

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: SELFIES (SELF-referencIng Embedded Strings). SELFIES has since simplified and enabled numerous new applications in chemistry. In this manuscript, 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

SELFIES and the future of molecular string representations

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: SELFIES (SELF-referencIng Embedded Strings). SELFIES has since simplified and enabled numerous new applications in chemistry. In this manuscript, 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.Comment: 34 pages, 15 figures, comments and suggestions for additional references are welcome

SELFIES and the future of molecular string representations

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

31st Annual Meeting and Associated Programs of the Society for Immunotherapy of Cancer (SITC 2016) : part two

Background The immunological escape of tumors represents one of the main ob- stacles to the treatment of malignancies. The blockade of PD-1 or CTLA-4 receptors represented a milestone in the history of immunotherapy. However, immune checkpoint inhibitors seem to be effective in specific cohorts of patients. It has been proposed that their efficacy relies on the presence of an immunological response. Thus, we hypothesized that disruption of the PD-L1/PD-1 axis would synergize with our oncolytic vaccine platform PeptiCRAd. Methods We used murine B16OVA in vivo tumor models and flow cytometry analysis to investigate the immunological background. Results First, we found that high-burden B16OVA tumors were refractory to combination immunotherapy. However, with a more aggressive schedule, tumors with a lower burden were more susceptible to the combination of PeptiCRAd and PD-L1 blockade. The therapy signifi- cantly increased the median survival of mice (Fig. 7). Interestingly, the reduced growth of contralaterally injected B16F10 cells sug- gested the presence of a long lasting immunological memory also against non-targeted antigens. Concerning the functional state of tumor infiltrating lymphocytes (TILs), we found that all the immune therapies would enhance the percentage of activated (PD-1pos TIM- 3neg) T lymphocytes and reduce the amount of exhausted (PD-1pos TIM-3pos) cells compared to placebo. As expected, we found that PeptiCRAd monotherapy could increase the number of antigen spe- cific CD8+ T cells compared to other treatments. However, only the combination with PD-L1 blockade could significantly increase the ra- tio between activated and exhausted pentamer positive cells (p= 0.0058), suggesting that by disrupting the PD-1/PD-L1 axis we could decrease the amount of dysfunctional antigen specific T cells. We ob- served that the anatomical location deeply influenced the state of CD4+ and CD8+ T lymphocytes. In fact, TIM-3 expression was in- creased by 2 fold on TILs compared to splenic and lymphoid T cells. In the CD8+ compartment, the expression of PD-1 on the surface seemed to be restricted to the tumor micro-environment, while CD4 + T cells had a high expression of PD-1 also in lymphoid organs. Interestingly, we found that the levels of PD-1 were significantly higher on CD8+ T cells than on CD4+ T cells into the tumor micro- environment (p < 0.0001). Conclusions In conclusion, we demonstrated that the efficacy of immune check- point inhibitors might be strongly enhanced by their combination with cancer vaccines. PeptiCRAd was able to increase the number of antigen-specific T cells and PD-L1 blockade prevented their exhaus- tion, resulting in long-lasting immunological memory and increased median survival