Data-Efficient Graph Grammar Learning for Molecular Generation - Minghao Guo
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Title: Data-Efficient Graph Grammar Learning for Molecular Generation
Abstract: The problem of molecular generation has received significant attention recently. Existing methods are typically based on deep neural networks and require training on large datasets with tens of thousands of samples. In practice, however, the size of class-specific chemical datasets is usually limited (e.g., dozens of samples) due to labor-intensive experimentation and data collection. This presents a considerable challenge for the deep learning generative models to comprehensively describe the molecular design space. Another major challenge is to generate only physically synthesizable molecules. This is a non-trivial task for neural network-based generative models since the relevant chemical knowledge can only be extracted and generalized from the limited training data. In this work, we propose a data-efficient generative model that can be learned from datasets with orders of magnitude smaller sizes than common benchmarks. At the heart of this method is a learnable graph grammar that generates molecules from a sequence of production rules. Without any human assistance, these production rules are automatically constructed from training data. Furthermore, additional chemical knowledge can be incorporated in the model by further grammar optimization.
Speaker: Minghao Guo - / guomh14
Twitter Prudencio: / tossouprudencio
Twitter Therence: / therence_mtl
Twitter Cas: / cas_wognum
Twitter Valence Discovery: / valence_ai
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Chapters:
00:00 Introduction speaker
01:07 Introduction to molecular generation
05:00 Efficiency of different representations
14:11 Formal grammar as generative model
22:37 Related work
24:48 Grammar construction for molecular generation
28:45 Grammar optimization
39:08 Experimental results
42:18 PolyGrammar - Scaling from monomers to polymers
50:23 Future work and summary
52:58 Q&A