In this work, we propose a novel reinforcement learning (RL) setup for drug discovery that addresses this challenge by embedding the concept of synthetic accessibility directly into the de novo compound design system.
Our framework is based on a parallel sequence decoding algorithm named Mask-Predict considering the bi-directional and conditional independent nature of BERT, and can be adapted to traditional autoregressive decoding easily.
The applications of short-term user-generated video (UGV), such as Snapchat, and Youtube short-term videos, booms recently, raising lots of multimodal machine learning tasks.
Over the last decade, there has been significant progress in the field of machine learning for de novo drug design, particularly in deep generative models.
In this article, we introduce the polar coordinate system to the deep learning detector for the first time, and propose an anchor free Polar Remote Sensing Object Detector (P-RSDet), which can achieve competitive detection accuracy via uses simpler object representation model and less regression parameters.
Experiments also show that back-translation with these diverse translations could bring significant improvement on performance on translation tasks.
The aim of the inverse chemical design is to develop new molecules with given optimized molecular properties or objectives.
We build a profitable electronic trading agent with Reinforcement Learning that places buy and sell orders in the stock market.
Motivated by the development of deep convolution neural networks (DCNNs), tremendous progress has been gained in the field of aircraft detection.