MolScore is an open-source Python framework for scoring and evaluating molecules in the context of goal-directed generative models as used in de novo drug design. MolScore includes many relevant scoring functions for de novo drug design such as molecular similarity, docking software, predictive models, and synthesizability, as well as commonly used performance metrics to evaluate generative model performance based on chemistry generated. Integration into an existing generative model framework is simple, requiring just three lines of code, and graphical user interfaces are provided for objective configuration and for monitoring de novo molecules generated. As a real-world demonstration of its use, we use it to design selective 5-HT2a ligands using 266 pre-trained off-target predictive models. MolScore can also be used for generative model evaluation as we demonstrate by analysing and selecting fine-tuning epochs of an RNN-based generative model. Moreover, the use of configuration files allows the sharing of objectives within the community for the purposes of reproducibility, comparison, and benchmarking; making it easier to propose drug discovery relevant objective functions as benchmark tasks. The code is freely available and hosted on GitHub, https://github.com/MorganCThomas/MolScore.
Supporting Information for “MolScore: A scoring and evaluation framework for de novo drug design”