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.'
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.'
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