De novo drug design involves the enumeration of new molecules with the aim of satisfying multiple activity and pharmacokinetic objectives. The combination of multiparameter optimisation, along with the vast area of chemical search space, makes enumeration and ranking a significant challenge for any de novo design project. This #BucketListPapers introduces MOARF (MultiObjective Automated Replacement of Fragments), a workflow for the multiobjective design of synthetically-accessible small molecules. Specifically, the protocol involves the implementation of SynDiR, a fragmentation algorithm that uses retrosynthetic rule-based cuts, to an input molecule. Replacement fragments are then identified from a large dataset of predefined fragments, generated by using SynDiR on molecule libraries, based on their physicochemical properties, similarity to the original fragment and alignment using Rapid Alignment Searching (RATS). The reconstructed molecules are then filtered and scored using a combined scoring function assembled from a range of user-defined functions, such as activity prediction and shape similarity models.
Importantly, the MOARF workflow was tested on a historical project from the Institute of Cancer Research (ICR), in which a CDK2 inhibitor was optimised for potency and metabolic stability. The results showed that MOARF generated a diverse dataset of suggestions within relevant areas of chemical space, and optimising against shape similarity (using ROCS), atom pair similarity, CDK2 activity prediction and CLogP resulted in the synthesis of several molecules with CDK2 activity and improved metabolic stability. This demonstrates the value of using computational enumeration and scoring methods to guide de novo design and multiparameter optimisation.
MOARF, an Integrated Workflow for Multiobjective Optimization: Implementation, Synthesis, and Biological Evaluation