Background/Objectives: An impediment to successful drug discovery is the potential for off-target liabilities to eliminate otherwise promising candidates. As the drug discovery process is time-consuming and expensive, the use of artificial intelligence (AI) methods such as machine learning (ML) has drastically increased. It is invaluable to generate models that can quickly differentiate between successful and unsuccessful small-molecule drug candidates. Previous efforts established that molecular similarity could be used with other metrics to inform predictions of potential activity against a protein target. Similar methods were pursued here to combine similarity and machine learning for a collection of models called OPLE. Methods: Models were trained with proprietary and publicly available data to predict the likelihood of a given compound to be active against targets present in existing experimental SafetyScreen panels 18 and 44. Two-dimensional (2D) Tanimoto similarity from extended-connectivity fingerprints (ECFPs) and trained ML models were combined to obtain predictions. Results: Using all training data, a relationship between similarity and activity was established by fitting a probability assignment curve. Calibrated ML label assignment likelihoods were joined with the predictions from ECFP Tanimoto similarity to known active compounds using the belief theory formula, which maintains that activity prediction increases when both pieces of evidence support it. When assessing the performance of OPLE models for SafetyScreen 18 and 44 targets with external data from ChEMBL, more than 80% of the models had recall values greater than 0.8. This indicated favorable predictive ability to identify active molecules while limiting false negative predictions. Conclusions: Predicting and experimentally verifying safety liabilities is insightful at every stage of small-molecule drug discovery. This early detection tool can help project teams save resources that could be better deployed on series with no predicted or measured off-target liabilities.
Biehn et al. (Wed,) studied this question.