# Reversible Upper Confidence Bound Algorithm to Generate Diverse Optimized Candidates

arXiv: https://arxiv.org/abs/2112.14893 · HTML: https://mlsanity.com/paper/2112.14893/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 54 references that are in the map.
- First posted on arXiv 30 Dec 2021.

## Builds on (most cited first, 4 of 4)

- [Molecular De Novo Design through Deep Reinforcement Learning](https://mlsanity.com/paper/1704.07555/) (2017)
- [Optimization of Molecules via Deep Reinforcement Learning](https://mlsanity.com/paper/1810.08678/) (2018)
- [Deep Reinforcement Learning for De-Novo Drug Design](https://mlsanity.com/paper/1711.10907/) (2017)
- [Solving Statistical Mechanics Using Variational Autoregressive Networks](https://mlsanity.com/paper/1809.10606/) (2018)

## Abstract (arXiv)

Most algorithms for the multi-armed bandit problem in reinforcement learning aimed to maximize the expected reward, which are thus useful in searching the optimized candidate with the highest reward (function value) for diverse applications (e.g., AlphaGo). However, in some typical application scenaios such as drug discovery, the aim is to search a diverse set of candidates with high reward. Here we propose a reversible upper confidence bound (rUCB) algorithm for such a purpose, and demonstrate its application in virtual screening upon intrinsically disordered proteins (IDPs). It is shown that rUCB greatly reduces the query times while achieving both high accuracy and low performance loss.The rUCB may have potential application in multipoint optimization and other reinforcement-learning cases.