AI and computational methods for molecular representation, property prediction, virtual screening, molecular design, and knowledge-based drug discovery. The emphasis is on computational prioritization and analysis rather than laboratory experimentation.
Development and use of computational representations of molecules for machine learning, similarity analysis, and downstream modeling.
Machine learning models for predicting molecular properties, activities, or other computationally measurable outcomes.
Computational methods for ranking and prioritizing molecular candidates before experimental evaluation.
AI methods for generating, evaluating, and optimizing molecular structures according to computational objectives.
Computational analysis of scientific and molecular knowledge to identify relationships that may support drug repositioning research.