Abstract
Aim: Development of a new, practically applicable computational method to monitor progress in lead optimization. Computational approaches that aid in compound optimization are discussed and the Compound Optimization Monitor (COMO) method is introduced and put into scientific context. Methodology & calculations: The methodological concept and the COMO scoring scheme are described in detail. Results & discussions: Calculation parameters are evaluated, and profiling results reported for an ensemble of analog series. Future perspective: The dual role of virtual analogs as diagnostic tools for progress evaluation and as potential candidates for lead optimization is discussed. In light of this dual role, interfacing COMO with machine learning for compound activity prediction and prioritization of candidates is highlighted as a future research objective.
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