When:  Thursday, November 15, 10:30 am US Eastern.

Zoom Detail Below

Background:

Teodoro Laino received his degree in theoretical chemistry in 2001 (University of Pisa and Scuola Normale Superiore di Pisa) and the doctorate in 2006 in computational chemistry at the Scuola Normale Superiore di Pisa, Italy. His doctoral thesis, entitled “Multi-Grid QM/ MM Approaches in ab initio Molecular Dynamics” was supervised by Prof. Dr. Michele Parrinello. From 2006 to 2008, he worked as a post-doctoral researcher in the research group of Prof. Dr. Jürg Hutter at the University of Zurich, where he developed algorithms for ab initio and classical molecular dynamics simulations. Since 2008, he has been working in the department of Cognitive Computing and Industry Solutions at the IBM Research – Zurich Laboratory (ZRL). The focus of his research is on complex molecular dynamics simulations for industrial-related problems (energy storage, life sciences and nano-electronics) and on the application of machine learning/artificial intelligence technologies to chemistry and materials science problems. 

Organic synthesis is one of the key stumbling blocks in medicinal chemistry. A necessary yet unsolved step in planning synthesis is solving the forward problem: given reactants and reagents, predict the products. We treat reaction prediction as a machine translation problem between SMILES strings of reactants-reagents and the products. We show that a multi-head attention Molecular Transformer model outperforms all algorithms in the literature, achieving a top-1 accuracy above 90% on a common benchmark dataset. Our algorithm requires no handcrafted rules, and accurately predicts subtle chemical transformations. Crucially, our model can accurately estimate its own uncertainty, with an uncertainty score that is 89% accurate in terms of classifying whether a prediction is correct. In this talk, he  will present the underlying model as well as the free online platform for reaction predictions, named IBM RXN for chemistry, http://rxn.res.ibm.com

Zoom meeting Link: https://zoom.us/j/7371462221