Development and use of computational representations of molecules for machine learning, similarity analysis, and downstream modeling.
Representations may include fingerprints, molecular graphs, descriptors, learned embeddings, and structure-aware features.
These representations support molecular similarity, property prediction, virtual screening, and generative molecular design.
Implementation is selected according to the available data, technical constraints, required level of automation, and the existing software or research environment. The solution can be implemented as a standalone component or integrated into a larger system.
The resulting system is intended to provide a clear computational workflow that can be evaluated, maintained, and extended as the project develops. Model choice, data processing, interfaces, and deployment can therefore be adapted to the requirements of the specific project.