Progress of Application Research on Cheminformatics in Deep Learning

doi:10.19894/j.issn.1000-0518.220229

Chinese Journal of Applied Chemistry ›› 2023, Vol. 40 ›› Issue (3): 360-373.DOI: 10.19894/j.issn.1000-0518.220229

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Progress of Application Research on Cheminformatics in Deep Learning

Zhen-Bang LIU¹, Shuo ZHANG², Yu BAO²(), Ying-Ming MA², Wei-Qi LIANG², Wei WANG², Ying HE², Li NIU²

^1.School of Computer Science and Cyber Engineering，Guangzhou University，Guangzhou 510006，China
^2.Center for Advanced Analytical Science，School of Chemistry and Chemical Engineering，Guangzhou University，Guangzhou 510006，China

Received:2022-07-01 Accepted:2022-11-10 Published:2023-03-01 Online:2023-03-27
Contact: Yu BAO
About author:baoyu@gzhu.edu.cn

Abstract

Abstract:

Deep learning has gone through breakthroughs in many research fields including computer vision， natural language processing， etc. due to multiple driving factors such as knowledge， data， algorithms and computing power. In addition， it has gradually spawned a number of new research directions with the migration and application as well as cross-integration among various disciplines. Cheminformatics is a discipline that solves chemical problems with the applied informatics methods， and deep learning can be useful since it is very powerful in nonlinear learning. Deep learning model can be used to screen and predict in the data set， and then verify the feasibility of the results based on theoretical calculation. Finally， the results are represented by experiments， which shortens the experimental period， reduces the labor cost and accelerates the intelligence of cheminformatics. This paper briefly introduces the development history and main network model architecture of deep learning as well as the latest research and application status of deep learning in synthesis planning， compound structure-activity relation and catalyst design in recent years， and also discusses and expects the future development direction.

Key words: Deep learning, Cheminformatics, Structure-activity relationship, Synthesis planning, Catalysis chemistry

CLC Number:

O657

Zhen-Bang LIU, Shuo ZHANG, Yu BAO, Ying-Ming MA, Wei-Qi LIANG, Wei WANG, Ying HE, Li NIU. Progress of Application Research on Cheminformatics in Deep Learning[J]. Chinese Journal of Applied Chemistry, 2023, 40(3): 360-373.

Figures/Tables 6

References 78

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Resear chinterests	Algorithm model	Purpose of study	Datasets	Ref.
Structure-activity relationship	DNN， MT-DNN	Prediction of Target structural protein	ChEMBL， A4， B1， PP4， IVINT	［42］
	LSTM-QSAR	Data augmentation with SMILES descriptors	—	［45］
	LSTM	Generation of model molecular structures	ChEMBL	［46］
	—	Feature extraction based on molecular fingerprint	The Harvard clean energy project	［47］
	CNN	Feature extraction of molecular structural formula	Abraham octanol solubility，Delaney small aqueous solubility，Tox21	［48］
Synthesis planning	seq2seq	Machine learning for organic reaction equations	Jin's USPTO	［54］
	seq2seq	Extraction of unsupervised molecular descriptors	—	［55］
	Expansion policy network， Rollout policy network	Optimization of retrosynthetic routes	Reaxys database	［56］
Catalysis chemistry	Transferlearning	Search and design of alloy catalysts	—	［40］
	Decision tree	Prediction of interfacial thermal resistance in nanostructures	—	［61］
	TPOT	Carbon dioxide reduction in electrocatalysis	GASpy	［67］
	Gradient boosting regression	Prediction of the activity trends of metal surface	—	［68］
	Extra tree regression	Prediction of adsorption on alloy surfaces	Surface energies of elemental crystals	［69］
	CNN	Local structure identification in TEM images	Atomic Simulation Environment	［74］
	DTNN， AI-Spectroscopist	Prediction of molecular excitation spectra	QM7b，M9	［75］
	CNN	Identifying chemical substances using Raman spectroscopy	RRUFF	［76］

Resear chinterests	Algorithm model	Purpose of study	Datasets	Ref.
Structure-activity relationship	DNN， MT-DNN	Prediction of Target structural protein	ChEMBL， A4， B1， PP4， IVINT	［42］
	LSTM-QSAR	Data augmentation with SMILES descriptors	—	［45］
	LSTM	Generation of model molecular structures	ChEMBL	［46］
	—	Feature extraction based on molecular fingerprint	The Harvard clean energy project	［47］
	CNN	Feature extraction of molecular structural formula	Abraham octanol solubility，Delaney small aqueous solubility，Tox21	［48］
Synthesis planning	seq2seq	Machine learning for organic reaction equations	Jin's USPTO	［54］
	seq2seq	Extraction of unsupervised molecular descriptors	—	［55］
	Expansion policy network， Rollout policy network	Optimization of retrosynthetic routes	Reaxys database	［56］
Catalysis chemistry	Transferlearning	Search and design of alloy catalysts	—	［40］
	Decision tree	Prediction of interfacial thermal resistance in nanostructures	—	［61］
	TPOT	Carbon dioxide reduction in electrocatalysis	GASpy	［67］
	Gradient boosting regression	Prediction of the activity trends of metal surface	—	［68］
	Extra tree regression	Prediction of adsorption on alloy surfaces	Surface energies of elemental crystals	［69］
	CNN	Local structure identification in TEM images	Atomic Simulation Environment	［74］
	DTNN， AI-Spectroscopist	Prediction of molecular excitation spectra	QM7b，M9	［75］
	CNN	Identifying chemical substances using Raman spectroscopy	RRUFF	［76］

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Progress of Application Research on Cheminformatics in Deep Learning

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