2019
DOI: 10.1039/c9me00039a
|View full text |Cite
|
Sign up to set email alerts
|

Deep learning for molecular design—a review of the state of the art

Abstract: In the space of only a few years, deep generative modeling has revolutionized how we think of artificial creativity, yielding autonomous systems which produce original images, music, and text. Inspired by these successes, researchers are now applying deep generative modeling techniques to the generation and optimization of molecules-in our review we found 45 papers on the subject published in the past two years. These works point to a future where such systems will be used to generate lead molecules, greatly r… Show more

Help me understand this report
View preprint versions

Search citation statements

Order By: Relevance

Paper Sections

Select...
4
1

Citation Types

3
543
0
8

Year Published

2019
2019
2024
2024

Publication Types

Select...
4
3
2

Relationship

0
9

Authors

Journals

citations
Cited by 556 publications
(554 citation statements)
references
References 121 publications
(141 reference statements)
3
543
0
8
Order By: Relevance
“…8 In the past decade, following on the advent of Variational Auto-Encoders (VAEs) 9 and Generative Adversarial Networks (GANs), 10 there has been a flood of new deep learning (DL) methods for this task. 11 Many of these methods learn a mapping from a continuous lower-dimensional real number space to a discrete chemical space. Jointly trained with a structure-property regression, one can obtain novel chemical structures conditioned on desired properties.…”
Section: Introductionmentioning
confidence: 99%
“…8 In the past decade, following on the advent of Variational Auto-Encoders (VAEs) 9 and Generative Adversarial Networks (GANs), 10 there has been a flood of new deep learning (DL) methods for this task. 11 Many of these methods learn a mapping from a continuous lower-dimensional real number space to a discrete chemical space. Jointly trained with a structure-property regression, one can obtain novel chemical structures conditioned on desired properties.…”
Section: Introductionmentioning
confidence: 99%
“…These methods include Variational AutoEncoders (VAEs), Recurrent Neural Network (RNN), Generative Adversarial Networks (GANs) and reinforcement learning (RL) [ 11 ]. Another alternative to build generative molecules models without SMILES is based on molecular graph representation [11]. Contrary to earlier reports [11], we demonstrate herein that text learning on SMILES is highly efficient to explore the training space with a high degree of novelty.…”
Section: Introductionmentioning
confidence: 88%
“…Since 2016, SMILES-based machine-learned methods are used to produce de novo molecules. These methods include Variational AutoEncoders (VAEs), Recurrent Neural Network (RNN), Generative Adversarial Networks (GANs) and reinforcement learning (RL) [ 11 ]. Another alternative to build generative molecules models without SMILES is based on molecular graph representation [11].…”
Section: Introductionmentioning
confidence: 99%
“…In total, 4,922 unique valid structures were automatically generated and all matched the defined rules by using ADQN-FBDD without any pre-training as many other methods need. 35,40,41,54,55 Next, All the molecules with high deep reinforcement learning scores (DRL score: R(S)>0.6) were kept (47 molecules). Then, these 47 unique molecules were prepared to generate at least 1 conformation with the local energy minimization using the OPLS-2005 force field by the "ligand prepare" module of Schrödinger 2015 software.…”
Section: Molecular Generation and Selectionmentioning
confidence: 99%