Differentiable Scaffolding Tree for Molecular Optimization
- URL: http://arxiv.org/abs/2109.10469v1
- Date: Wed, 22 Sep 2021 01:16:22 GMT
- Title: Differentiable Scaffolding Tree for Molecular Optimization
- Authors: Tianfan Fu, Wenhao Gao, Cao Xiao, Jacob Yasonik, Connor W. Coley,
Jimeng Sun
- Abstract summary: We propose differentiable scaffolding tree (DST) that utilizes a learned knowledge network to convert discrete chemical structures to locally differentiable ones.
Our empirical studies show the gradient-based molecular optimizations are both effective and sample efficient.
- Score: 47.447362691543304
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The structural design of functional molecules, also called molecular
optimization, is an essential chemical science and engineering task with
important applications, such as drug discovery. Deep generative models and
combinatorial optimization methods achieve initial success but still struggle
with directly modeling discrete chemical structures and often heavily rely on
brute-force enumeration. The challenge comes from the discrete and
non-differentiable nature of molecule structures. To address this, we propose
differentiable scaffolding tree (DST) that utilizes a learned knowledge network
to convert discrete chemical structures to locally differentiable ones. DST
enables a gradient-based optimization on a chemical graph structure by
back-propagating the derivatives from the target properties through a graph
neural network (GNN). Our empirical studies show the gradient-based molecular
optimizations are both effective and sample efficient. Furthermore, the learned
graph parameters can also provide an explanation that helps domain experts
understand the model output.
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