This paper is concerned with a molecular optimization framework using variational autoencoders (VAEs). In this paradigm, VAE allows us to convert a molecular graph into from its latent continuous vector, and therefore, the molecular optimization problem can be solved by continuous optimization techniques. One of the longstanding issues in this area is that it is difficult to always generate valid molecules. The very recent work called the junction tree variational autoencoder (JT-VAE) successfully solved this issue by generating a molecule fragment-by-fragment. While it achieves the state-of-the-art performance, it requires several neural networks to be trained, which predict which atoms are used to connect fragments and stereochemistry of each bond. In this paper, we present a molecular hypergraph grammar variational autoencoder (MHG-VAE), which uses a single VAE to address the issue. Our idea is to develop a novel graph grammar for molecular graphs called molecular hypergraph grammar (MHG), which can specify the connections between fragments and the stereochemistry on behalf of neural networks. This capability allows us to address the issue using only a single VAE. We empirically demonstrate the effectiveness of MHG-VAE over existing methods.
Molecular optimization has a longstanding history especially in drug discovery, and mostly combinatorial methods have been used to generate novel molecules @cite_7 . The paper by , for the first time, applies modern machine learning techniques to this problem, and since then, growing number of papers have been tackling this problem.
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We present a framework, which we call Molecule Deep @math -Networks (MolDQN), for molecule optimization by combining domain knowledge of chemistry and state-of-the-art reinforcement learning techniques (prioritized experience replay, double @math -learning, and randomized value functions). We directly define modifications on molecules, thereby ensuring 100 chemical validity. Further, we operate without pre-training on any dataset to avoid possible bias from the choice of that set. As a result, our model outperforms several other state-of-the-art algorithms by having a higher success rate of acquiring molecules with better properties. Inspired by problems faced during medicinal chemistry lead optimization, we extend our model with multi-objective reinforcement learning, which maximizes drug-likeness while maintaining similarity to the original molecule. We further show the path through chemical space to achieve optimization for a molecule to understand how the model works.