Semi-Supervised GCN for learning Molecular Structure-Activity Relationships
Since the introduction of artificial intelligence in medicinal chemistry, the necessity has emerged to analyse how molecular property variation is modulated by either single atoms or chemical groups. In this paper, we propose to train graph-to-graph neural network using semi-supervised learning for attributing structure-property relationships. As initial case studies we apply the method to solubility and molecular acidity while checking its consistency in comparison with known experimental chemical data. As final goal, our approach could represent a valuable tool to deal with problems such as activity cliffs, lead optimization and de-novo drug design.
Code (0)
등록된 구현이 없습니다.
Tasks
Drug DesignGraph Neural NetworkSimilar Papers 제목 키워드 기반
MaskMol: Knowledge-guided Molecular Image Pre-Training Framework for Activity Cliffs
Activity cliffs, which refer to pairs of molecules that are structurally similar but show significant differences in their potency, can lead to model representation collapse and make the model challenging to distinguish …
Drug DiscoveryRepresentation LearningSelf-Supervised LearningA semi-supervised learning framework for quantitative structure-activity regression modelling
Supervised learning models, also known as quantitative structure-activity regression (QSAR) models, are increasingly used in assisting the process of preclinical, small molecule drug discovery. The models are trained on …
Drug DiscoveryregressionSelection biasMulti-channel learning for integrating structural hierarchies into context-dependent molecular representation
Reliable molecular property prediction is essential for various scientific endeavors and industrial applications, such as drug discovery. However, the data scarcity, combined with the highly non-linear causal relationshi…
Drug DiscoveryMolecular Property Predictionmolecular representationProperty Prediction+1A Semi-supervised Molecular Learning Framework for Activity Cliff Estimation
Machine learning (ML) enables accurate and fast molecular property predictions, which are of interest in drug discovery and material design. Their success is based on the principle of similarity at its heart, assuming th…
Drug DiscoveryProbabilistic Generative Deep Learning for Molecular Design
Probabilistic generative deep learning for molecular design involves the discovery and design of new molecules and analysis of their structure, properties and activities by probabilistic generative models using the deep …
Deep Learning