Molecule Optimization via Fragment-based Generative Models
- URL: http://arxiv.org/abs/2012.04231v2
- Date: Tue, 12 Jan 2021 16:39:36 GMT
- Title: Molecule Optimization via Fragment-based Generative Models
- Authors: Ziqi Chen, Martin Renqiang Min, Srinivasan Parthasarathy, Xia Ning
- Abstract summary: In drug discovery, molecule optimization is an important step in order to modify drug candidates into better ones in terms of desired drug properties.
We present an innovative in silico approach to computationally optimizing molecules and formulate the problem as to generate optimized molecular graphs.
Our generative models follow the key idea of fragment-based drug design, and optimize molecules by modifying their small fragments.
- Score: 21.888942129750124
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In drug discovery, molecule optimization is an important step in order to
modify drug candidates into better ones in terms of desired drug properties.
With the recent advance of Artificial Intelligence, this traditionally in vitro
process has been increasingly facilitated by in silico approaches. We present
an innovative in silico approach to computationally optimizing molecules and
formulate the problem as to generate optimized molecular graphs via deep
generative models. Our generative models follow the key idea of fragment-based
drug design, and optimize molecules by modifying their small fragments. Our
models learn how to identify the to-be-optimized fragments and how to modify
such fragments by learning from the difference of molecules that have good and
bad properties. In optimizing a new molecule, our models apply the learned
signals to decode optimized fragments at the predicted location of the
fragments. We also construct multiple such models into a pipeline such that
each of the models in the pipeline is able to optimize one fragment, and thus
the entire pipeline is able to modify multiple fragments of molecule if needed.
We compare our models with other state-of-the-art methods on benchmark datasets
and demonstrate that our methods significantly outperform others with more than
80% property improvement under moderate molecular similarity constraints, and
more than 10% property improvement under high molecular similarity constraints.
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