Scaffold-Induced Molecular Graph (SIMG): Effective Graph Sampling Methods for High-Throughput Computational Drug Discovery
Scaffold based drug discovery (SBDD) is a technique for drug discovery which pins chemical scaffolds as the framework of design. Scaffolds, or molecular frameworks, organize the design of compounds into local neighborhoods. We formalize scaffold based drug discovery into a network design. Utilizing docking data from SARS-CoV-2 virtual screening studies and JAK2 kinase assay data, we showcase how a scaffold based conception of chemical space is intuitive for design. Lastly, we highlight the utility of scaffold based networks for chemical space as a potential solution to the intractable enumeration problem of chemical space by working inductively on local neighborhoods.
Code (0)
등록된 구현이 없습니다.
Tasks
Drug DiscoveryGraph SamplingSimilar Papers 제목 키워드 기반
Representational Alignment with Chemical Induced Fit for Molecular Relational Learning
Molecular Relational Learning (MRL) is widely applied in natural sciences to predict relationships between molecular pairs by extracting structural features. The representational similarity between substructure pairs det…
Inductive BiasRelational ReasoningScaffold Embeddings: Learning the Structure Spanned by Chemical Fragments, Scaffolds and Compounds
Molecules have seemed like a natural fit to deep learning's tendency to handle a complex structure through representation learning, given enough data. However, this often continuous representation is not natural for unde…
Drug DiscoveryRepresentation LearningLearning Topology-Specific Experts for Molecular Property Prediction
Recently, graph neural networks (GNNs) have been successfully applied to predicting molecular properties, which is one of the most classical cheminformatics tasks with various applications. Despite their effectiveness, w…
Molecular Property PredictionPredictionProperty PredictionScaffold-based molecular design using graph generative model
Searching new molecules in areas like drug discovery often starts from the core structures of candidate molecules to optimize the properties of interest. The way as such has called for a strategy of designing molecules r…
Drug DiscoverymodelZero Shot Molecular Generation via Similarity Kernels
Generative modelling aims to accelerate the discovery of novel chemicals by directly proposing structures with desirable properties. Recently, score-based, or diffusion, generative models have significantly outperformed …