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Deep Evolutionary Learning for Molecular Design

In this paper, a prototypical deep evolutionary learning (DEL) process is proposed to integrate deep generative model and multi-objective evolutionary computation for molecular design. Our approach enables (1) evolutionary operations in the latent space of the generative model, rather than the structural space, to generate promising novel molecular structures for the next evolutionary generation, and (2) generative model fine-tuning using newly generated high-quality samples. Thus, DEL implements a data-model co-evolution concept which improves both sample population and generative model learning. Experiments on public datasets indicate that the sample population obtained by DEL exhibits improvement on property distributions, and dominates samples generated by other baseline molecular optimization algorithms. Furthermore, comparisons with a range of deep generative models show that DEL is beneficial for improving sample populations.

Ketersediaan

Informasi Detail

Judul Seri

IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE; Vol.17 No.2 May 2022

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 14-28

Bahasa

English

ISBN/ISSN

-

Klasifikasi

NONE

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

STATISTICS
OPTIMIZATION
BENTUK GALAKSI
EVOLUTIONARY COMPUTATION
COMPUTATIONAL MODELING
SOCIAL FACTORS
GENERATIVE ADVERSARIAL NETWORKS
MOLECULAR COMPUTING

Info Detail Spesifik

DOI: 10.1109/MCI.2022.3155308

Pernyataan Tanggungjawab

Karl Grantham, Muhetaer Mukaidaisi, Hsu Kiang Ooi, Mohammad Sajjad Ghaemi, Alain Tchagang, Yifeng Li

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