Computational methods for ranking and prioritizing molecular candidates before experimental evaluation.
Approaches can combine molecular descriptors, machine learning, similarity methods, docking-related data, and multi-stage ranking.
The goal is to reduce large candidate collections to smaller sets suitable for further computational or experimental investigation.
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.