Learning Multimodal Graph-to-Graph Translation for Molecular Optimization
We view molecular optimization as a graph-to-graph translation problem. The goal is to learn to map from one molecular graph to another with better properties based on an available corpus of paired molecules. Since molecules can be optimized in different ways, there are multiple viable translations for each input graph. A key challenge is therefore to model diverse translation outputs. Our primary contributions include a junction tree encoder-decoder for learning diverse graph translations along with a novel adversarial training method for aligning distributions of molecules. Diverse output distributions in our model are explicitly realized by low-dimensional latent vectors that modulate the translation process. We evaluate our model on multiple molecular optimization tasks and show that our model outperforms previous state-of-the-art baselines.
Code (5)
Tasks
DecoderGraph-To-Graph TranslationTranslationSimilar Papers 제목 키워드 기반
Learning Multimodal Graph-to-Graph Translation for Molecule Optimization
We view molecule optimization as a graph-to-graph translation problem. The goal is to learn to map from one molecular graph to another with better properties based on an available corpus of paired molecules. Since molecu…
DecoderGraph-To-Graph TranslationTranslationHierarchical Graph-to-Graph Translation for Molecules
The problem of accelerating drug discovery relies heavily on automatic tools to optimize precursor molecules to afford them with better biochemical properties. Our work in this paper substantially extends prior state-of-…
DecoderDrug DiscoveryGraph-To-Graph TranslationTranslationMol-LLM: Multimodal Generalist Molecular LLM with Improved Graph Utilization
Recent advances in large language models (LLMs) have led to models that tackle diverse molecular tasks, such as chemical reaction prediction and molecular property prediction. Large-scale molecular instruction-tuning dat…
Chemical Reaction PredictionMolecular Property PredictionMolecule CaptioningPrediction+1Black Box Recursive Translations for Molecular Optimization
Machine learning algorithms for generating molecular structures offer a promising new approach to drug discovery. We cast molecular optimization as a translation problem, where the goal is to map an input compound to a t…
Drug DiscoveryMolecular Graph GenerationTranslationBack to Basics: Improving Molecular Understanding in LLMs via SMILES-Graph Translation
Recent advances in molecular large language models have led to strong performance on molecular understanding and generation tasks, yet these gains often come without reliable structural grounding. In particular, existing…