Junction Tree Variational Autoencoder for Molecular Graph Generation
We seek to automate the design of molecules based on specific chemical properties. In computational terms, this task involves continuous embedding and generation of molecular graphs. Our primary contribution is the direct realization of molecular graphs, a task previously approached by generating linear SMILES strings instead of graphs. Our junction tree variational autoencoder generates molecular graphs in two phases, by first generating a tree-structured scaffold over chemical substructures, and then combining them into a molecule with a graph message passing network. This approach allows us to incrementally expand molecules while maintaining chemical validity at every step. We evaluate our model on multiple tasks ranging from molecular generation to optimization. Across these tasks, our model outperforms previous state-of-the-art baselines by a significant margin.
Code (11)
Tasks
Drug DiscoveryGraph GenerationMolecular Graph GenerationSimilar Papers 제목 키워드 기반
Semi-Supervised Junction Tree Variational Autoencoder for Molecular Property Prediction
Molecular Representation Learning is essential to solving many drug discovery and computational chemistry problems. It is a challenging problem due to the complex structure of molecules and the vast chemical space. Graph…
Computational chemistryDrug DiscoveryMolecular Property Predictionmolecular representation+2Variational Autoencoder for Anti-Cancer Drug Response Prediction
Cancer is a primary cause of human death, but discovering drugs and tailoring cancer therapies are expensive and time-consuming. We seek to facilitate the discovery of new drugs and treatment strategies for cancer using …
Drug Response PredictionPredictionConditioned Generative Modeling of Molecular Glues: A Realistic AI Approach for Synthesizable Drug-like Molecules
Alzheimer's disease (AD) is marked by the pathological accumulation of amyloid beta-42 (Abeta-42), contributing to synaptic dysfunction and neurodegeneration. While extracellular amyloid plaques are well-studied, increas…
Leveraging Active Subspaces to Capture Epistemic Model Uncertainty in Deep Generative Models for Molecular Design
Deep generative models have been accelerating the inverse design process in material and drug design. Unlike their counterpart property predictors in typical molecular design frameworks, generative molecular design model…
Bayesian InferenceDiversityDrug DesignUncertainty QuantificationPathway-Guided Optimization of Deep Generative Molecular Design Models for Cancer Therapy
The data-driven drug design problem can be formulated as an optimization task of a potentially expensive black-box objective function over a huge high-dimensional and structured molecular space. The junction tree variati…
Drug Design