In silico design of small molecular libraries via Reinforcement learning

During the last decade, there is an increasing interest in applying deep learning in de novo drug design. In this thesis, a tool is developed to address the specific needs for generating small library for lead optimization. The optimization of small molecules is conducted given an input scaffold with defined attachment points. Various chemical fragments are proposed by the generative model and reinforcement learning is used to guide the generation to produce a library of molecules that satisfy user-defined properties. The generation is also constrained to follow user-defined reactions which makes synthesis controllable. Several experiments are executed to find the optimal hyperparameters, make comparison of different learning strategies, demonstrate the superiority of slicing molecules based on defined reactions compared to RECAP rules, showcase the model’s ability to follow different synthetic routes as well as its capability of decorating scaffolds with various attachment points. Results have shown that DAP learning strategy outperforms all other learning strategies. The use of reaction based slicing is superior than utilising RECAP rules slicing, it helps the model to learn the reaction filter faster. Also, the model was capable of satisfying different reaction filters and decorating scaffolds with various attachment points. In conclusion, the model is able to rapidly generate a molecular library which contains a large number of molecules sharing the same scaffold, with desirable properties and can be synthesised under specified reactions