Active Semisupervised Model for Improving the Identification of Anticancer Peptides

Cai, Liang; Wang, Li; Fu, Xiquan; Zeng, Xiangxiang

doi:10.1021/acsomega.1c03132

Cited by 9 publications

(2 citation statements)

References 46 publications

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“…Bayesian optimization focuses on finding the optimal value, while active learning is to reduce the amount of labeled data by selecting the most informative examples to label 27 . Current research has demonstrated that, when using the same annotation cost, exploring more informative regions through active learning can achieve higher performance than random exploration 28,29 . With the query strategy guided by the active learning algorithm, we can effectively evaluate the informativeness of each molecule and selectively explore the vast chemical space, reducing the huge annotation cost.…”

Section: Introductionmentioning

confidence: 99%

Deep active learning with high structural discriminability for molecular mutagenicity prediction

He,

Xu,

Zhao

et al. 2023

Preprint

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The assessment of mutagenicity is essential in drug discovery, as it may lead to cancer and germ cells damage. Although in silico methods have been proposed for mutagenicity prediction, their performance is hindered by the scarcity of labeled molecules. However, experimental mutagenicity testing can be time-consuming and costly. One solution to lower the annotation cost is active learning, where the algorithm actively selects the most valuable samples from a vast chemical space and hands them over to the oracle (e.g., human expert) for annotation. In this paper, we propose TOX-AL, a deep active learning framework, which can actively explore the chemical space and identify the most valuable molecules, resulting in competitive performance with a small number of labeled samples. Compared to the random strategy, TOX-AL can reduce about 57% of training molecules. Surprisingly, TOX-AL exhibits outstanding molecular structural discriminability, allowing it to pick molecules with high structural similarity but opposite properties.

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Section: Introductionmentioning

confidence: 99%

Deep active learning with high structural discriminability for molecular mutagenicity prediction

He,

Xu,

Zhao

et al. 2023

Preprint

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“…Deep learning methods, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), have been developed for the detection of 4mC sites, leveraging their ability to capture sequence patterns and dependencies, thereby contributing to accurate identification of these sites and enhancing our understanding of DNA methylation in gene regulation and epigenetics ( Xu et al, 2021 ; Liu et al, 2022 ). Yet there are still many deep learning methods that have not been applied, which have achieved great success in various application scenarios, including computer vision, speech recognition, biomarker identification ( Zeng et al, 2020 ; Cai et al, 2021 ) and drug discovery ( Chen et al, 2021 ; Zhang et al, 2021 ; Hu et al, 2022b ; Dong et al, 2022 ; Pan et al, 2022 ; Song et al, 2022 ).…”

Section: Introductionmentioning

confidence: 99%

Comparative evaluation and analysis of DNA N4-methylcytosine methylation sites using deep learning

Ju,

Bai,

Jiang

et al. 2023

Front. Genet.

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DNA N4-methylcytosine (4mC) is significantly involved in biological processes, such as DNA expression, repair, and replication. Therefore, accurate prediction methods are urgently needed. Deep learning methods have transformed applications that previously require sequencing expertise into engineering challenges that do not require expertise to solve. Here, we compare a variety of state-of-the-art deep learning models on six benchmark datasets to evaluate their performance in 4mC methylation site detection. We visualize the statistical analysis of the datasets and the performance of different deep-learning models. We conclude that deep learning can greatly expand the potential of methylation site prediction.

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Active learning approaches in molecule pKi prediction

Kashafutdinova,

Poyezzhayeva,

Gimadiev

et al. 2024

Molecular Informatics

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During the early stages of drug design, identifying compounds with suitable bioactivities is crucial. Given the vast array of potential drug databases, it′s feasible to assay only a limited subset of candidates. The optimal method for selecting the candidates, aiming to minimize the overall number of assays, involves an active learning (AL) approach. In this work, we benchmarked a range of AL strategies with two main objectives: (1) to identify a strategy that ensures high model performance and (2) to select molecules with desired properties using minimal assays. To evaluate the different AL strategies, we employed the simulated AL workflow based on “virtual” experiments. These experiments leveraged ChEMBL datasets, which come with known biological activity values for the molecules. Furthermore, for classification tasks, we proposed the hybrid selection strategy that unified both exploration and exploitation AL strategies into a single acquisition function, defined by parameters n and c. We have also shown that popular minimal margin and maximal variance selection approaches for exploration selection correspond to minimization of the hybrid acquisition function with n=1 and 2 respectively. The balance between the exploration and exploitation strategies can be adjusted using a coefficient (c), making the optimal strategy selection straightforward. The primary strength of the hybrid selection method lies in its adaptability; it offers the flexibility to adjust the criteria for molecule selection based on the specific task by modifying the value of the contribution coefficient. Our analysis revealed that, in regression tasks, AL strategies didn't succeed at ensuring high model performance, however, they were successful in selecting molecules with desired properties using minimal number of tests. In analogous experiments in classification tasks, exploration strategy and the hybrid selection function with a constant c<1 (for n=1) and c≤0.2 (for n=2) were effective in achieving the goal of constructing a high‐performance predictive model using minimal data. When searching for molecules with desired properties, exploitation, and the hybrid function with c≥1 (n=1) and c≥0.7 (n=2) demonstrated efficiency identifying molecules in fewer iterations compared to random selection method. Notably, when the hybrid function was set to an intermediate coefficient value (c=0.7), it successfully addressed both tasks simultaneously.

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Active Semisupervised Model for Improving the Identification of Anticancer Peptides

Cited by 9 publications

References 46 publications

Deep active learning with high structural discriminability for molecular mutagenicity prediction

Deep active learning with high structural discriminability for molecular mutagenicity prediction

Comparative evaluation and analysis of DNA N4-methylcytosine methylation sites using deep learning

Active learning approaches in molecule pKi prediction

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