Saskia Teri, Anita Acai, Douglas Griffith, Qusay Mahmoud, David W L Ma, Genevieve Newton
Student use and pedagogical impact of a mobile learning application
Biochemistry and molecular biology education : a bimonthly publication of the International Union of Biochemistry and Molecular Biology
many traditional quantitative structureactivity relationship qsar models based correlation highdimensional highly variable molecular features raw form limiting generalizing capabilities despite use large training sets also lack elements causality reasoning issues mind developed method learning higherlevel abstract representations effects interactions molecular features biology named representations reason vectors composed series computed activity substructures obtained stepwise reconstruction molecule representation different fingerprints composed molecular features directly vectors capture reasons bioactivity chemicals absence thereof abstract form uncover causality interactions chemical features generalize beyond specific chemical classes bioactivity reason vectors contain key attributes much smaller molecular fingerprints allow vague conceptual similarity searches less susceptible failure novel combinations query molecule features likely identify reasons activity chemical classes absent training data reason vectors compared activity computed matching vectors molecules known bioactivity single molecule produces many reason vectors heavy atoms simple count vectors series activity ranges needed predict bioactivity thus prediction method devoid gradient optimization statistical fitting