In this work, we explore the potential of deep learning to streamline the process of identifying new potential drugs through the computational generation of molecules with interesting biological properties. Two deep neural networks compose our targeted generation framework: the Generator, which is trained to learn the building rules of valid molecules employing SMILES strings notation, and the Predictor which evaluates the newly generated compounds by predicting their aﬃnity for the desired target. Then, the Generator is optimized through Reinforcement Learning to produce molecules with bespoken properties.
The innovation of this approach is the exploratory strategy applied during the reinforcement training process that seeks to add novelty to the generated compounds. This training strategy employs two Generators interchangeably to sample new SMILES: the initially trained model that will remain ﬁxed and a copy of the previous one that will be updated during the training to uncover the most promising molecules. The evolution of the reward assigned by the Predictor determines how often each one is employed to select the next token of the molecule. This strategy establishes a compromise between the need to acquire more information about the chemical space and the need to sample new molecules, with the experience gained so far.
To demonstrate the eﬀectiveness of the method, the Generator is trained to design molecules with high inhibitory power for the adenosine A2A and κ opioid receptors. The results reveal that the model can eﬀectively modify the biological aﬃnity of the newly generated molecules towards the craved direction. More importantly, it was possible to ﬁnd auspicious sets of unique and diverse molecules, which was the main purpose of the newly implemented strategy.