Work

A Systematic Assessment of Deep Learning Models for Molecule Generation

Year: 2020

Type: preprint

Abstract: 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.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2008.09168

Authors: Rigoni, Davide, Navarin, Nicolò, Sperduti, Alessandro

Institutions: University of Padua, Fondazione Bruno Kessler, Ghent University, University of Nottingham

Language: English

Topic: Computational Drug Discovery Methods

Subfield: Computational Theory and Mathematics

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W3080969965

Cites: 15

Cited by: 6

Related to: 20