It is generally difficult to estimate the odds of success in hit-to-lead and lead optimization projects. In medicinal chemistry, decisions to continue or discontinue individual series are typically made on the basis of subjective judgment. A computational method termed Compound Optimization MOnitor (COMO) is presented that helps to determine if further optimization progress can be expected for a given series or if sufficient numbers of analogs have been generated. The approach integrates the assessment of chemical saturation of analog series and SAR progression. Chemical space distributions of existing analogs and virtual candidates are compared and neighborhoods of analogs are systematically analyzed. Chemical saturation and SAR progression scores are calculated, which make it possible to predict the potential of series for further chemical optimization. COMO also includes a compound design component. Virtual analogs generated for chemical saturation analysis provide a pool of candidates for synthesis. Machine learning enables the prediction of active analogs to further advance series prioritized by COMO. The methodology is easily expandable to include multiple optimization relevant properties.

Monitoring progress in lead optimization

2019 Fall National Meeting

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