Magicmol - A light-weighted pipeline for drug-like molecule evolution and quick chemical space exploration

The flourishment of machine learning and deep learning methods have boosted the development of cheminformatics, especially when it comes to the application of drug discovery and new materials exploration. Lower time and space expenses make it possible for scientists to search the enormous chemical space. Recently, some work combines reinforcement learning strategies with an RNN-based (Recurrent Neural Networks) model to optimize the property of generated drug-like molecules, which notably improved a batch of critical factors for these drug candidates. However, a common problem of these RNN-based methods is that several generated molecules have difficulty in synthesizing even if owning higher desired properties such as binding affinity. But still, the RNN-based framework appears well in reproducing the molecule distribution among the training set than other categories of models when it comes to the molecule exploration tasks. Thus, to optimize the whole exploring process and make it contribute to the optimization of specified molecules. In this paper, we devised a light-weighted pipeline called - Magicmol with a re-mastered RNN network and use SELFIES presentation instead of SMILES. Our backbone model achieve extraordinary performance in evaluating metrics meanwhile reduced the training cost and we devised reward truncate strategies to eliminate the ”model collapse” problem. Also, adopting SELFIES presentation makes it possible for combining STONED-SELFIES as a post-processing procedure for specified molecule optimization and quick chemical space exploration.