Abstract: Finding synthesis routes for molecules of interest is essential in the discovery of new drugs and materials. To find such routes, computer-assisted synthesis planning (CASP) methods are employed, which rely on a single-step model of chemical reactivity. In this study, we introduce a template-based single-step retrosynthesis model based on Modern Hopfield Networks, which learn an encoding of both molecules and reaction templates in order to predict the relevance of templates for a given molecule. The template representation allows generalization across different reactions and significantly improves the performance of template relevance prediction, especially for templates with few or zero training examples. With inference speed up to orders of magnitude faster than baseline methods, we improve or match the state-of-the-art performance for top-k exact match accuracy for k ≥ 3 in the retrosynthesis benchmark USPTO-50k.
Bio: Philipp Seidl is a PhD candidate at the Institute for Machine Learning - JKU Linz. He has a double Bachelor degree in Information Systems and Medical Engineering and a Msc degree in Bioinformatics. His current research focus on how to leverage machine learning methods especially Transformers, Modern Hopfield Networks and Few-Shot Learning algorithms for drug discovery applications
Welcome to this space dedicated to the M2D2 Talks co-organized by Valence Discovery and Mila - Quebec AI Institute.
From applied research papers to open source projects, we’re hoping to use these talks to help demystify AI for drug discovery and make the field more accessible for newcomers. M2D2 will bring our vibrant AI & drug discovery communities together and spark new perspectives, provoke discussions, and offer a safe space to share new ideas.
A wide range of drug discovery related topics will be covered reflecting the vibrant diversity of tools and methodologies in the community:
Whenever possible, slides and videos will be available after each talk.
Bayesian Optimization for Ternary Complex Prediction
Learning 3D Representations of Molecular Chirality with Invariance to Bond Rotations
Exposing the limitations of molecular machine learning with activity cliffs
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