In recent years the scientific community has devoted much effort in the development of deep learning models for the generation of new molecules with desirable properties (i.e. drugs). This has produced many proposals in literature. However, a systematic comparison among the different VAE methods is still missing. For this reason, we propose an extensive testbed for the evaluation of generative models for drug discovery, and we present the results obtained by many of the models proposed in literature.
Delta Score: Improving the Binding Assessment of Structure-Based Drug Design Methods
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Institute for AI Industry Research (AIR), Tsinghua University（清华大学人工智能产业研究院）；Peking University（北京大学）
MolGenSurvey: A Systematic Survey in Machine Learning Models for Molecule Design
George Mason University（乔治梅森大学）；Georgia Institute of Technology（佐治亚理工学院）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Mila - Université de Montréal（米拉 - 蒙特利尔大学）
Intelligent System for Automated Molecular Patent Infringement Assessment
DP Technology（深势科技）；University of Science and Technology of China（中国科学技术大学）；National University of Singapore（新加坡国立大学）；Sun Yat-Sen University（中山大学）；AI for Science Institute, Beijing（北京科学智能研究院）；Peking University（北京大学）
URSA: Chemistry-Aware Benchmark for Utilitarian Retrosynthesis Assessment
Insilico Medicine AI Limited（Insilico Medicine AI有限公司）；Independent researcher（独立研究者）；Insilico Medicine Canada Inc.（Insilico Medicine加拿大公司）；Insilico Medicine Hong Kong Ltd.（Insilico Medicine香港有限公司）
AI in Supply Chain Risk Assessment: A Systematic Literature Review and Bibliometric Analysis
Khulna University of Engineering and Technology (KUET)（库尔纳工程技术大学）；Okinawa Institute of Science and Technology Graduate University (OIST)（冲绳科学技术大学院大学）；American International University-Bangladesh (AIUB)（孟加拉国美国国际大学）
