Deep learning in retrosynthesis planning: datasets, models and tools

Dong, Jingxin; Zhao, Mingyi; Liu, Yuansheng; Su, Yansen; Zeng, Xiangxiang

doi:10.1093/bib/bbab391

Cited by 63 publications

(67 citation statements)

References 81 publications

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“…These genes can be targeted to design and screen effective therapeutic drugs. Compared with traditional drug design strategies, the computational model is more directed, so it can shorten the development time for new drugs ( Yu et al, 2020b ; Zeng et al, 2020b ; Huo et al, 2020 ; Li et al, 2020 ; Cheng et al, 2021b ; Wang et al, 2021b ; Dong et al, 2021 ). For example, Paul et al used a biological metabolic network of leishmaniasis and deletion mutations designed using bioinformatics methods to identify a collection of essential proteins for this disease ( Ao et al, 2021 ; Hu et al, 2021b ), and this method was more than five times more efficient than the method of randomly selecting potential drug targets ( Stanly Paul et al, 2014 ; Chiu et al, 2020 ; Wang et al, 2020 ).…”

Section: Methods For Identifying Essential Genesmentioning

confidence: 99%

Research on the Computational Prediction of Essential Genes

Guo

Dong

et al. 2021

Front. Cell Dev. Biol.

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Genes, the nucleotide sequences that encode a polypeptide chain or functional RNA, are the basic genetic unit controlling biological traits. They are the guarantee of the basic structures and functions in organisms, and they store information related to biological factors and processes such as blood type, gestation, growth, and apoptosis. The environment and genetics jointly affect important physiological processes such as reproduction, cell division, and protein synthesis. Genes are related to a wide range of phenomena including growth, decline, illness, aging, and death. During the evolution of organisms, there is a class of genes that exist in a conserved form in multiple species. These genes are often located on the dominant strand of DNA and tend to have higher expression levels. The protein encoded by it usually either performs very important functions or is responsible for maintaining and repairing these essential functions. Such genes are called persistent genes. Among them, the irreplaceable part of the body’s life activities is the essential gene. For example, when starch is the only source of energy, the genes related to starch digestion are essential genes. Without them, the organism will die because it cannot obtain enough energy to maintain basic functions. The function of the proteins encoded by these genes is thought to be fundamental to life. Nowadays, DNA can be extracted from blood, saliva, or tissue cells for genetic testing, and detailed genetic information can be obtained using the most advanced scientific instruments and technologies. The information gained from genetic testing is useful to assess the potential risks of disease, and to help determine the prognosis and development of diseases. Such information is also useful for developing personalized medication and providing targeted health guidance to improve the quality of life. Therefore, it is of great theoretical and practical significance to identify important and essential genes. In this paper, the research status of essential genes and the essential genome database of bacteria are reviewed, the computational prediction method of essential genes based on communication coding theory is expounded, and the significance and practical application value of essential genes are discussed.

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Section: Methods For Identifying Essential Genesmentioning

confidence: 99%

Research on the Computational Prediction of Essential Genes

Guo

Dong

et al. 2021

Front. Cell Dev. Biol.

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“…Traditional antioxidant drug screening and discovery are carried out through biochemical experiments, which not only has a long time period and high cost, but also has the risk of failure in experiments ( Lv et al, 2020a ; Cheng et al, 2020 ; Cheng Y et al, 2021 ; Lv Z et al, 2021 ; Dong et al, 2021 ; Goto et al, 2021 ; Zeng et al, 2022 ). With the continuous improvement of computer technology and genome databases, methods such as data mining and machine learning are more and more widely used in biological information, drug screening and other fields ( Cheng et al, 2018 ; Wang et al, 2018 ; Ding et al, 2019 ; Wang et al, 2019 ; Zeng et al, 2020a ; Zhang CH et al, 2020 ; Zhang J et al, 2020 ; Lyu et al, 2020 ; Zhao X et al, 2021 ; Niu et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

AOPM: Application of Antioxidant Protein Classification Model in Predicting the Composition of Antioxidant Drugs

Zhai

Zhang

et al. 2022

Front. Pharmacol.

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Antioxidant proteins can not only balance the oxidative stress in the body, but are also an important component of antioxidant drugs. Accurate identification of antioxidant proteins is essential to help humans fight diseases and develop new drugs. In this paper, we developed a friendly method AOPM to identify antioxidant proteins. 188D and the Composition of k-spaced Amino Acid Pairs were adopted as the feature extraction method. In addition, the Max-Relevance-Max-Distance algorithm (MRMD) and random forest were the feature selection and classifier, respectively. We used 5-folds cross-validation and independent test dataset to evaluate our model. On the test dataset, AOPM presented a higher performance compared with the state-of-the-art methods. The sensitivity, specificity, accuracy, Matthew’s Correlation Coefficient and an Area Under the Curve reached 87.3, 94.2, 92.0%, 0.815 and 0.972, respectively. In addition, AOPM still has excellent performance in predicting the catalytic enzymes of antioxidant drugs. This work proved the feasibility of virtual drug screening based on sequence information and provided new ideas and solutions for drug development.

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“…Machine learning can identify desired proteins from a large number of sequences within a short time to guide the experimental discovery process ( Guo et al, 2020 ; Liu et al, 2020 ; Song G. et al, 2021 ; Cheng et al, 2021 ; Deng et al, 2021 ; Dong et al, 2021 ; Guo et al, 2021 ; Tang et al, 2021 ; Yu et al, 2021 ; Zhao et al, 2021 ). Over the past decades, researchers have developed many machine learning–based techniques for protein sequence analysis ( Zhai et al, 2020 ; Zeng et al, 2020 ; Chen et al, 2021 ; Li et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Immunoglobulin Classification Based on FC* and GC* Features

et al. 2022

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Immunoglobulins have a pivotal role in disease regulation. Therefore, it is vital to accurately identify immunoglobulins to develop new drugs and research related diseases. Compared with utilizing high-dimension features to identify immunoglobulins, this research aimed to examine a method to classify immunoglobulins and non-immunoglobulins using two features, FC* and GC*. Classification of 228 samples (109 immunoglobulin samples and 119 non-immunoglobulin samples) revealed that the overall accuracy was 80.7% in 10-fold cross-validation using the J48 classifier implemented in Weka software. The FC* feature identified in this study was found in the immunoglobulin subtype domain, which demonstrated that this extracted feature could represent functional and structural properties of immunoglobulins for forecasting.

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Deep learning in retrosynthesis planning: datasets, models and tools

Cited by 63 publications

References 81 publications

Research on the Computational Prediction of Essential Genes

Research on the Computational Prediction of Essential Genes

AOPM: Application of Antioxidant Protein Classification Model in Predicting the Composition of Antioxidant Drugs

Immunoglobulin Classification Based on FC* and GC* Features

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