Information School News

  “We rely so much on Google these days”, says Dr Frank Hopfgartner, Senior Lecturer at the Information School and Investigator on the ‘Do You See What I See’ project. “Google has a search engine market share of over 90% worldwide.” Undertaken by Dr Hopfgartner with several fellow members of the Cyprus Center for Algorithmic Transparency (CyCAT) - which was profiled in the research magazine Inform II in 2019 (page 29) - this project aimed to discover the differences in the search results that Google provides to users in different parts of the world. Google states that their mission is to “organize the world’s information and make it universally accessible and useful”. This project asked: is that true? Does everyone everywhere have equal access to the same information? And if not, what impact might that have? Dr Frank Hopfgartner “CyCAT for me was very interesting because algorithmic transparency and bias is a very timely topic and one which is receiving a lot of attention”, says ...

PhD student Gianmarco Ghiandoni recently attended well known chemoinformatics conference GCC 2019 , in Mainz, Germany, as an official speaker. 'I presented some content from my PhD project which describes the use of Reaction Class Recommendation models in de novo Drug Design', says Gianmarco. 'These models have shown to have a role as deterministic search components which maximise the chance of generating meaningful synthetic patterns in de novo design and compound optimisation.' 'In addition to this, the application of these models has resulted to yield product libraries characterised by higher synthetic accessibility, whilst reducing drastically the algorithmic enumeration times.'

Gianmarco Ghiandoni, PhD student in our Chemoinformatics research group, recently attended and presented at the UK-QSAR conference in Cambridge. Gianmarco attended the conference and presented a part of his PhD project, which involves the development of "Reaction Class Recommender Systems in de novo Drug Design". 'These algorithms are machine learning models that have recently acquired great importance due to their effectiveness in product recommendation', Gianmarco said. 'In particular, companies such as Amazon, Netflix, Spotify, etc., have built their reputations and businesses on the top of these models. At Sheffield, we have decided to apply these methods in order to produce suggestions for decision making in automated molecular design. The results from their application indicate that recommender systems can improve the synthetic accessibility of the designed molecules whilst reducing the computational requirements.'