Machine learning models for predicting molecular properties, activities, or other computationally measurable outcomes.
Work can include dataset preparation, molecular representation, model development, validation, uncertainty analysis, and interpretation.
Potential applications include activity prediction, physicochemical properties, toxicity-related modeling, and compound prioritization.
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.