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Deep inverse reinforcement learning for structural evolution of small molecules.

Brighter Agyemang¹, Wei-Ping Wu², Daniel Addo²

¹University of Electronic Science and Technology of China.

Briefings in Bioinformatics

|December 21, 2020

Summary

This study introduces a new framework for drug discovery using inverse reinforcement learning (IRL) to generate novel chemical compounds. This approach simplifies the process by learning a transferable reward function, making drug design more accessible.

Keywords:

de novo drug design inverse reinforcement learning recurrent neural networks reinforcement learning

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Area of Science:

Medicinal Chemistry
Computational Chemistry
Drug Discovery

Background:

Chemical library size and quality are critical for drug discovery and repurposing.
Current methods like combinatorial synthesis and high-throughput screening are complex due to the vast chemical search space.
Reinforcement learning for novel compound generation faces challenges in defining effective reward functions.

Purpose of the Study:

To propose a novel framework for training compound generators.
To learn a transferable reward function using the entropy maximization inverse reinforcement learning (IRL) paradigm.
To provide an alternative to complex reward function engineering in drug discovery.

Main Methods:

Framework for training a compound generator.
Utilizing entropy maximization inverse reinforcement learning (IRL).
Learning a transferable reward function from available data.

Main Results:

The proposed IRL framework effectively trains compound generators.
A transferable reward function was successfully learned.
Experimental results demonstrate the viability of the IRL approach.

Conclusions:

Inverse reinforcement learning offers a rational alternative for chemical compound generation.
This method is particularly useful in domains where reward function engineering is difficult or impossible.
The approach is effective when data reflecting the desired objective is available.