Fragment-based molecular generative model with high generalization ability and synthetic accessibility

TL;DR

This paper introduces a fragment-based molecular generative model that enhances property control, generalization, and synthetic accessibility, enabling the design of molecules with desired features and potential drug activity.

Contribution

The proposed model uniquely combines fragment-based generation with high generalization and property control, improving upon atom-based methods for molecular design.

Findings

Successfully controls multiple target properties simultaneously

Generates molecules with high synthetic accessibility

Effective in generating potential SARS-CoV-2 inhibitors

Abstract

Deep generative models are attracting great attention for molecular design with desired properties. Most existing models generate molecules by sequentially adding atoms. This often renders generated molecules with less correlation with target properties and low synthetic accessibility. Molecular fragments such as functional groups are more closely related to molecular properties and synthetic accessibility than atoms. Here, we propose a fragment-based molecular generative model which designs new molecules with target properties by sequentially adding molecular fragments to any given starting molecule. A key feature of our model is a high generalization ability in terms of property control and fragment types. The former becomes possible by learning the contribution of individual fragments to the target properties in an auto-regressive manner. For the latter, we used a deep neural network that predicts the bonding probability of two molecules from the embedding vectors of the two molecules as input. The high synthetic accessibility of the generated molecules is implicitly considered while preparing the fragment library with the BRICS decomposition method. We show that the model can generate molecules with the simultaneous control of multiple target properties at a high success rate. It also works equally well with unseen fragments even in the property range where the training data is rare, verifying the high generalization ability. As a practical application, we demonstrated that the model can generate potential inhibitors with high binding affinities against the 3CL protease of SARS-COV-2 in terms of docking score.