Reinforcement Learning for Molecular Design Guided by Quantum Mechanics
- URL: http://arxiv.org/abs/2002.07717v2
- Date: Mon, 29 Jun 2020 14:16:34 GMT
- Title: Reinforcement Learning for Molecular Design Guided by Quantum Mechanics
- Authors: Gregor N. C. Simm, Robert Pinsler, Jos\'e Miguel Hern\'andez-Lobato
- Abstract summary: We present a novel RL formulation for molecular design in coordinates, thereby extending the class of molecules that can be built.
Our reward function is directly based on fundamental physical properties such as the energy, which we approximate via fast quantum-chemical methods.
In our experiments, we show that our agent can efficiently learn to solve these tasks from scratch by working in a translation and rotation invariant state-action space.
- Score: 10.112779201155005
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Automating molecular design using deep reinforcement learning (RL) holds the
promise of accelerating the discovery of new chemical compounds. Existing
approaches work with molecular graphs and thus ignore the location of atoms in
space, which restricts them to 1) generating single organic molecules and 2)
heuristic reward functions. To address this, we present a novel RL formulation
for molecular design in Cartesian coordinates, thereby extending the class of
molecules that can be built. Our reward function is directly based on
fundamental physical properties such as the energy, which we approximate via
fast quantum-chemical methods. To enable progress towards de-novo molecular
design, we introduce MolGym, an RL environment comprising several challenging
molecular design tasks along with baselines. In our experiments, we show that
our agent can efficiently learn to solve these tasks from scratch by working in
a translation and rotation invariant state-action space.
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