Both corporate and academic drug discovery groups routinely leverage robotic high throughput screening techniques to enable the exploration of enormous compound collections for specific and desired biological activities. These datasets, while extremely powerful and informative, have not eliminated the need for significant investments in chemistry and pharmacokinetic optimization. Because resources are finite and chemical space is relatively unlimited, it is imperative that discovery program teams selectively invest their resources in the chemical matter having the highest likelihood of delivering safe and efficacious drug candidates. While it is standard practice for teams to leverage parameters such as molecular weight, logP, Ro5, and chemical structural information to prioritize their efforts, the incorporation of in silico profiling of HTS hits leveraging QSAR models built for specific ADMET endpoints in addition to (or in place of) the routinely applied physical properties has not been described. Herein, we disclose a strategy that we have taken in order to quickly identify the most promising chemical starting points for drug discovery programs by integrating detailed in silico ADMET properties with decision-enabling visualizations. Several prospective examples will be presented to substantiate the value of this novel approach to hit finding.

Increasing the probability of success of hit series with in silico ADME profiles

252nd National Meeting (2016)

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