Accelerating Prototype-Based Drug Discovery using Conditional Diversity Networks

Harel, Shahar; Radinsky, Kira

doi:10.1145/3219819.3219882

Cited by 18 publications

(6 citation statements)

References 27 publications

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“…Conditional Diversity Networks and prototype‐driven diversity networks are VAE‐based networks set by Harel and Radinsky. The encoder had convolutions, acting like substructure filters, and a diversity layer.…”

Section: Deep Learning For Molecular Generationmentioning

confidence: 99%

Inverse‐QSPR for de novo Design: A Review

Gantzer

Creton

Nieto‐Draghi

2019

Molecular Informatics

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The use of computer tools to solve chemistry‐related problems has given rise to a large and increasing number of publications these last decades. This new field of science is now well recognized and labelled Chemoinformatics. Among all chemoinformatics techniques, the use of statistical based approaches for property predictions has been the subject of numerous research reflecting both new developments and many cases of applications. The so obtained predictive models relating a property to molecular features – descriptors – are gathered under the acronym QSPR, for Quantitative Structure Property Relationships. Apart from the obvious use of such models to predict property values for new compounds, their use to virtually synthesize new molecules – de novo design – is currently a high‐interest subject. Inverse‐QSPR (i‐QSPR) methods have hence been developed to accelerate the discovery of new materials that meet a set of specifications. In the proposed manuscript, we review existing i‐QSPR methodologies published in the open literature in a way to highlight developments, applications, improvements and limitations of each.

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Section: Deep Learning For Molecular Generationmentioning

confidence: 99%

Inverse‐QSPR for de novo Design: A Review

Gantzer

Creton

Nieto‐Draghi

2019

Molecular Informatics

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“…For example, to translate Japanese to English correctly, the ED model must understand not only the characters of both Japanese and English but also the context of the strings [11,12]. The latent representation of NMT models learning SMILES includes the context of SMILES, i.e., the entire chemical structure [13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Investigation of chemical structure recognition by encoder–decoder models in learning progress

2023

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Descriptor generation methods using latent representations of encoder–decoder (ED) models with SMILES as input are useful because of the continuity of descriptor and restorability to the structure. However, it is not clear how the structure is recognized in the learning progress of ED models. In this work, we created ED models of various learning progress and investigated the relationship between structural information and learning progress. We showed that compound substructures were learned early in ED models by monitoring the accuracy of downstream tasks and input–output substructure similarity using substructure-based descriptors, which suggests that existing evaluation methods based on the accuracy of downstream tasks may not be sensitive enough to evaluate the performance of ED models with SMILES as descriptor generation methods. On the other hand, we showed that structure restoration was time-consuming, and in particular, insufficient learning led to the estimation of a larger structure than the actual one. It can be inferred that determining the endpoint of the structure is a difficult task for the model. To our knowledge, this is the first study to link the learning progress of SMILES by ED model to chemical structures for a wide range of chemicals. Graphical Abstract

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“…For example, in order to correctly translate Japanese to English, the ED model must understand not only the characters of both Japanese and English but also the context of the strings [11,12]. The latent representation of NMT models learning SMILES includes the context of SMILES, i.e., the entire chemical structure [13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Investigation of chemical structure recognition by encoder-decoder models in learning progress

Nemoto

Mizuno

Kusuhara

2022

Preprint

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Descriptor generation methods using latent representations of Encoder-Decoder (ED) models with SMILES as input is useful because of continuity of descriptor and restorability to structure. However, it is not clear how the structure is recognized in the learning progress of ED model. In this work, we created ED models of various learning progress and investigated the relationship between structural information and the learning progress. We showed that compound substructures were learned early in ED models by monitoring the accuracy of downstream tasks and input-output substructure similarity using substructure-based descriptor, which suggests that existing evaluation methods based on the accuracy of downstream tasks may not be sensitive enough to evaluate the performance of ED models few with SMILES as descriptor generation methods. On the other hand, we showed that structure restoration was time consuming, and in particular, insufficient learning led to estimation of a larger structure than the actual one. It can be inferred that determining the end point of the structure is a difficult task for the model. To the best of our knowledge, this is the first study to link the learning progress of SMILES by ED model to chemical structures for a wide range of chemicals.

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Accelerating Prototype-Based Drug Discovery using Conditional Diversity Networks

Cited by 18 publications

References 27 publications

Inverse‐QSPR for de novo Design: A Review

Inverse‐QSPR for de novo Design: A Review

Investigation of chemical structure recognition by encoder–decoder models in learning progress

Investigation of chemical structure recognition by encoder-decoder models in learning progress

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