Fragment growth with reaction predictions for hit discovery
Molecular generation using AI approaches have gained wide recognition, thanks to the availability of numerous approaches (LSTM, VAE, GAN…).1 Unfortunately those algorithms suffer from producing complex to unfeasible molecules in terms of synthetic feasibility.2 Recently new AI approaches involving chemical reactions have been described.3 Mixing an initial molecular structure with commercial starting materials in the context of a reaction is a natural way to define a policy for generating new compounds. This method ensures synthetic accessibility of the generated molecules as the synthetic scheme is inherently obtained during the design process.
In this work we built a library of molecules starting from a defined fragment possessing two exit vectors where commercial starting materials can react with. The reaction prediction was performed with a template-based neural network coupled with an applicability domain estimator. The goal was to find hits for the PIM-1 protein,4 an isozyme of PIM kinase found in many cancers which inhibition is a promising approach to stop cell-growing and reproduction.
The library was generated under the constraints of drug likeness metrics and structure-based scoring. The best scoring compounds were profiled by medicinal chemist and some of them were found similar to known PIM-1 inhibitors. According to our knowledge it is the first report that uses a generative AI incorporating synthetic feasibility by design, under structure-based constraints with a single fragment as starting point. This clearly demonstrates the tremendous potential of such approach to easily generate valuable new starting points in the context of a hit discovery program.