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Cancer, being the second leading cause of death globally. So, the development of effective anticancer treatments is crucial in the field of medicine. Anticancer peptides (ACPs) have shown promising therapeutic potential in cancer treatment compared to traditional methods. However, the process of identifying ACPs through experimental means is often time-intensive and expensive. To overcome this issue, we employed a machine learning-based approach for the first time to develop an anticancer model using small molecules. Anticancer small molecules (ACSMs) are compounds that have been developed to target and inhibit cancer cells. In this study, we used 10,000 compounds to develop the machine learning models using five algorithms such as, Random Forest (RF), Light gradient boosting machine (LightGBM), K-nearest neighbors (KNN), Decision tree (DT) and Extreme Gradient Boosting (XGB). The developed models were evaluated using the test set and top three models were identified (RF, LightGBM and XGB). Furthermore, to validate the predictive performance of our models, we have performed external validation using an FDA approved anticancer compounds/drugs. Following this analysis, we found that our LightGBM model correctly predicted 9 compounds as active. However, RF and XGB exhibited some limitations by predicting 8 and 7 compounds as active out of 10, respectively. These results demonstrate that, when compared to RF and XGB, the LightGBM model showcase robust prediction capabilities, achieving a superior accuracy of 79% with an AUC of 0.88. These findings provide promising insights into the potential of our approach for predicting anticancer small molecules, highlighting the role of machine learning in advancing cancer treatment research., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Recently, anticancer peptides (ACPs) have emerged as unique and promising therapeutic agents for cancer treatment compared with antibody and small molecule drugs. In addition to experimental methods of ACPs discovery, it is also necessary to develop accurate machine learning models for ACP prediction. In this study, features were extracted from the three-dimensional (3D) structure of peptides to develop the model, compared to most of the previous computational models, which are based on sequence information. In order to develop ACPs with more potency, more selectivity and less toxicity, the model for predicting ACPs, hemolytic peptides and toxic peptides were established by peptides 3D structure separately. Multiple datasets were collected according to whether the peptide sequence was chemically modified. After feature extraction and screening, diverse algorithms were used to build the model. Twelve models with excellent performance (Acc > 90%) in the ACPs mixed datasets were used to form a hybrid model to predict the candidate ACPs, and then the optimal model of hemolytic peptides (Acc = 73.68%) and toxic peptides (Acc = 85.5%) was used for safety prediction. Novel ACPs were found by using those models, and five peptides were randomly selected to determine their anticancer activity and toxic side effects in vitro experiments.

Increasing use of therapeutic peptides for treating cancer has received considerable attention of the scientific community in the recent years. The present study describes the in silico model developed for predicting and designing anticancer peptides (ACPs). ACPs residue composition analysis show the preference of A, F, K, L and W. Positional preference analysis revealed that residues A, F and K are favored at N-terminus and residues L and K are preferred at C-terminus. Motif analysis revealed the presence of motifs like LAKLA, AKLAK, FAKL and LAKL in ACPs. Machine learning models were developed using various input features and implementing different machine learning classifiers on two datasets main and alternate dataset. In the case of main dataset, dipeptide composition based ETree classifier model achieved maximum Matthews correlation coefficient (MCC) of 0.51 and 0.83 area under receiver operating characteristics (AUROC) on the training dataset. In the case of alternate dataset, amino acid composition based ETree classifier performed best and achieved the highest MCC of 0.80 and AUROC of 0.97 on the training dataset. Five-fold cross-validation technique was implemented for model training and testing, and their performance was also evaluated on the validation dataset. Best models were implemented in the webserver AntiCP 2.0, which is freely available at https://webs.iiitd.edu.in/raghava/anticp2/. The webserver is compatible with multiple screens such as iPhone, iPad, laptop and android phones. The standalone version of the software is available at GitHub; docker-based container also developed., (© The Author(s) 2020. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

Peptides act as promising anticancer agents due to their ease of synthesis and modifications, enhanced tumor penetration, and less systemic toxicity. However, only limited success has been achieved so far, as experimental design and synthesis of anticancer peptides (ACPs) are prohibitively costly and time-consuming. Furthermore, the sequential increase in the protein sequence data via highthroughput sequencing makes it difficult to identify ACPs only through experimentation, which often involves months or years of speculation and failure. All these limitations could be overcome by applying machine learning (ML) approaches, which is a field of artificial intelligence that automates analytical model building for rapid and accurate outcome predictions. Recently, ML approaches hold great promise in the rapid discovery of ACPs, which could be witnessed by the growing number of MLbased anticancer prediction tools. In this review, we aim to provide a comprehensive view on the existing ML approaches for ACP predictions. Initially, we will briefly discuss the currently available ACP databases. This is followed by the main text, where state-of-the-art ML approaches working principles and their performances based on the ML algorithms are reviewed. Lastly, we discuss the limitations and future directions of the ML methods in the prediction of ACPs., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

Purpose of Review: This review aims to elucidate the mechanisms employed by Host Defense Peptides (HDPs) in propagating their effector functions, associated bottlenecks complicating their therapeutic applications, and how these peptides can be optimized or modified as potential next-generation lead agents in targeted therapy in managing infectious diseases and cancer. Recent Findings: Emerging studies are affirming that due to dwindling therapeutic options, infectious diseases and cancers undoubtedly remain major threats to public health. This is mostly attributable to the toxicity and resistance of classical drugs in combating these health challenges, thus, novel bio-inspired treatment strategies are being sought. As the first line of defense in all organisms, the innate immune system produces many immune effector molecules including a group of biologically active endogenous peptides collectively known as Host Defense Peptides (HDPs), which can be further sub-classified into Antimicrobial Peptides (AMPs), or Anticancer Peptides (ACPs) based on their effector functions. These highly diverse, ubiquitous, and low-molecular-weight biomolecules are being identified and proposed as models for new drug/lead candidates. This is owing to their unique mechanism of action, sequence, and structural diversities which are integral to their downstream biological functions. Therefore, continuous efforts to elucidate the exploitability of these quintessential peptides in drug discovery research against these pathologies are worth exploring. Summary: The ubiquity, significant diversities, and neoteric novel biological activities of HDPs as one of the effectors of the innate immune system establish them as a unique and valuable therapeutic strategy for combating infectious diseases and cancer. They are especially becoming prominent in overcoming the limitations posed by conventional therapeutic agents in managing these diseases, making them promising candidates for future drug development. Furthermore, their diverse functions and numerous molecular targets will play a crucial role in their discovery as new therapeutics. [ABSTRACT FROM AUTHOR]

Abstract The identification of anticancer peptides (ACPs) is crucial, especially in the development of peptide-based cancer therapy. The classical models such as Split Amino Acid Composition (SAAC) and Pseudo Amino Acid Composition (PseAAC) lack the incorporation of feature representation. These advancements improve the predictive accuracy and efficiency of ACP identification. Thus, the effort of this research is to propose and develop an advanced framework based on feature extraction. Thus, to achieve this objective herein we propose an Extended Dipeptide Composition (EDPC) framework. The proposed EDPC framework extends the dipeptide composition by considering the local sequence environment information and reforming the CD-HIT framework to remove noise and redundancy. To measure the accuracy, we have performed several experiments. These experiments were employed using four famous machine learning (ML) algorithms named; Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and K Nearest Neighbor (KNN). For comparisons, we have used accuracy, specificity, sensitivity, precision, recall, and F1-Score as evaluation criteria. The reliability of the proposed framework is further evaluated using statistical significance tests. As a result, the proposed EDPC framework exhibited enhanced performance than SAAC and PseAAC, where the SVM model delivered the highest accuracy of 96. 6% and significant enhancements in specificity, sensitivity, precision, and F1-score over multiple datasets. Due to the incorporation of enhanced feature representation and the incorporation of local and global sequence profiles proposed EDPC achieves higher classification performance. The proposed frameworks can deal with noise and also duplicating features. These are accompanied by a wide range of feature representations. Finally, our proposed framework can be used for clinical applications where ACP identification is essential. Future works will include extending to a larger variety of datasets, incorporating tertiary structural information, and using deep learning techniques to improve the proposed EDPC.

The identification of anticancer peptides (ACPs) is crucial, especially in the development of peptide-based cancer therapy. The classical models such as Split Amino Acid Composition (SAAC) and Pseudo Amino Acid Composition (PseAAC) lack the incorporation of feature representation. These advancements improve the predictive accuracy and efficiency of ACP identification. Thus, the effort of this research is to propose and develop an advanced framework based on feature extraction. Thus, to achieve this objective herein we propose an Extended Dipeptide Composition (EDPC) framework. The proposed EDPC framework extends the dipeptide composition by considering the local sequence environment information and reforming the CD-HIT framework to remove noise and redundancy. To measure the accuracy, we have performed several experiments. These experiments were employed using four famous machine learning (ML) algorithms named; Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and K Nearest Neighbor (KNN). For comparisons, we have used accuracy, specificity, sensitivity, precision, recall, and F1-Score as evaluation criteria. The reliability of the proposed framework is further evaluated using statistical significance tests. As a result, the proposed EDPC framework exhibited enhanced performance than SAAC and PseAAC, where the SVM model delivered the highest accuracy of 96. 6% and significant enhancements in specificity, sensitivity, precision, and F1-score over multiple datasets. Due to the incorporation of enhanced feature representation and the incorporation of local and global sequence profiles proposed EDPC achieves higher classification performance. The proposed frameworks can deal with noise and also duplicating features. These are accompanied by a wide range of feature representations. Finally, our proposed framework can be used for clinical applications where ACP identification is essential. Future works will include extending to a larger variety of datasets, incorporating tertiary structural information, and using deep learning techniques to improve the proposed EDPC. [ABSTRACT FROM AUTHOR]

Background: Cancer remains one of the leading causes of mortality globally, with conventional chemotherapy often resulting in severe side effects and limited effectiveness. Recent advancements in bioinformatics and machine learning, particularly deep learning, offer promising new avenues for cancer treatment through the prediction and identification of anticancer peptides. Objective: This study aimed to develop and evaluate a deep learning model utilizing a two-dimensional convolutional neural network (2D CNN) to enhance the prediction accuracy of anticancer peptides, addressing the complexities and limitations of current prediction methods. Methods: A diverse dataset of peptide sequences with annotated anticancer activity labels was compiled from various public databases and experimental studies. The sequences were preprocessed and encoded using one-hot encoding and additional physicochemical properties. The 2D CNN model was trained and optimized using this dataset, with performance evaluated through metrics such as accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC-ROC). Results: The proposed 2D CNN model achieved superior performance compared to existing methods, with an accuracy of 0.87, precision of 0.85, recall of 0.89, F1-score of 0.87, and an AUC-ROC value of 0.91. These results indicate the model's effectiveness in accurately predicting anticancer peptides and capturing intricate spatial patterns within peptide sequences. Conclusion: The findings demonstrate the potential of deep learning, specifically 2D CNNs, in advancing the prediction of anticancer peptides. The proposed model significantly improves prediction accuracy, offering a valuable tool for identifying effective peptide candidates for cancer treatment. Future Work: Further research should focus on expanding the dataset, exploring alternative deep learning architectures, and validating the model's predictions through experimental studies. Efforts should also aim at optimizing computational efficiency and translating these predictions into clinical applications. [ABSTRACT FROM AUTHOR]

Background Cancer remains one of the leading causes of mortality globally, with conventional chemotherapy often resulting in severe side effects and limited effectiveness. Recent advancements in bioinformatics and machine learning, particularly deep learning, offer promising new avenues for cancer treatment through the prediction and identification of anticancer peptides. Objective This study aimed to develop and evaluate a deep learning model utilizing a two-dimensional convolutional neural network (2D CNN) to enhance the prediction accuracy of anticancer peptides, addressing the complexities and limitations of current prediction methods. Methods A diverse dataset of peptide sequences with annotated anticancer activity labels was compiled from various public databases and experimental studies. The sequences were preprocessed and encoded using one-hot encoding and additional physicochemical properties. The 2D CNN model was trained and optimized using this dataset, with performance evaluated through metrics such as accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC-ROC). Results The proposed 2D CNN model achieved superior performance compared to existing methods, with an accuracy of 0.87, precision of 0.85, recall of 0.89, F1-score of 0.87, and an AUC-ROC value of 0.91. These results indicate the model’s effectiveness in accurately predicting anticancer peptides and capturing intricate spatial patterns within peptide sequences. Conclusion The findings demonstrate the potential of deep learning, specifically 2D CNNs, in advancing the prediction of anticancer peptides. The proposed model significantly improves prediction accuracy, offering a valuable tool for identifying effective peptide candidates for cancer treatment. Future Work Further research should focus on expanding the dataset, exploring alternative deep learning architectures, and validating the model’s predictions through experimental studies. Efforts should also aim at optimizing computational efficiency and translating these predictions into clinical applications.

Cancer is one of the most dangerous diseases in the world, killing millions of people every year. Drugs composed of anticancer peptides have been used to treat cancer with low side effects in recent years. Therefore, identifying anticancer peptides has become a focus of research. In this study, an improved anticancer peptide predictor named ACP-GBDT, based on gradient boosting decision tree (GBDT) and sequence information, is proposed. To encode the peptide sequences included in the anticancer peptide dataset, ACP-GBDT uses a merged-feature composed of AAIndex and SVMProt-188D. A GBDT is adopted to train the prediction model in ACP-GBDT. Independent testing and ten-fold crossvalidation show that ACP-GBDT can effectively distinguish anticancer peptides from non-anticancer ones. The comparison results of the benchmark dataset show that ACP-GBDT is simpler and more effective than other existing anticancer peptide prediction methods. [ABSTRACT FROM AUTHOR]

Anticancer peptides (ACPs) are rising as a new strategy for cancer therapy. However, traditional laboratory screening to find and identify novel ACPs from hundreds to thousands of peptides is costly and time consuming. Here, a sequential procedure is applied to identify candidate ACPs from a computer‐generated peptide library inspired by alpha‐lactalbumin, a milk protein with known anticancer properties. A total of 2688 distinct peptides, 5–25 amino acids in length, are generated from alpha‐lactalbumin. In silico ACP screening using the physicochemical and structural filters and three machine learning models lead to the top candidate peptides ALA‐A1 and ALA‐A2. In vitro screening against five human cancer cell lines supports ALA‐A2 as the positive hit. ALA‐A2 selectively kills A549 lung cancer cells in a dose‐dependent manner, with no hemolytic side effects, and acts as a cell penetrating peptide without membranolytic effects. Sequential window acquisition of all theorical fragment ions‐proteomics and functional validation reveal that ALA‐A2 induces autophagy to mediate lung cancer cell death. This approach to identify ALA‐A2 is time and cost‐effective. Further investigations are warranted to elucidate the exact intracellular targets of ALA‐A2. Moreover, these findings support the use of larger computational peptide libraries built upon multiple proteins to further advance ACP research and development. [ABSTRACT FROM AUTHOR]

Abstract Anticancer peptides (ACPs) are rising as a new strategy for cancer therapy. However, traditional laboratory screening to find and identify novel ACPs from hundreds to thousands of peptides is costly and time consuming. Here, a sequential procedure is applied to identify candidate ACPs from a computer‐generated peptide library inspired by alpha‐lactalbumin, a milk protein with known anticancer properties. A total of 2688 distinct peptides, 5–25 amino acids in length, are generated from alpha‐lactalbumin. In silico ACP screening using the physicochemical and structural filters and three machine learning models lead to the top candidate peptides ALA‐A1 and ALA‐A2. In vitro screening against five human cancer cell lines supports ALA‐A2 as the positive hit. ALA‐A2 selectively kills A549 lung cancer cells in a dose‐dependent manner, with no hemolytic side effects, and acts as a cell penetrating peptide without membranolytic effects. Sequential window acquisition of all theorical fragment ions‐proteomics and functional validation reveal that ALA‐A2 induces autophagy to mediate lung cancer cell death. This approach to identify ALA‐A2 is time and cost‐effective. Further investigations are warranted to elucidate the exact intracellular targets of ALA‐A2. Moreover, these findings support the use of larger computational peptide libraries built upon multiple proteins to further advance ACP research and development.

The use of therapeutic peptides for the treatment of cancer has received tremendous attention in recent years. Anticancer peptides (ACPs) are considered new anticancer drugs which have several advantages over chemistry-based drugs including high specificity, strong tumor penetration capacity, and low toxicity level for normal cells. Due to the rise of experimentally verified bioactive peptides, several in silico approaches became imperative for the investigation of the characteristics of ACPs. In this paper, we proposed a new machine learning tool named iACP-RF that uses a combination of several sequence-based features and an ensemble of three heterogeneously trained Random Forest classifiers to accurately predict anticancer peptides. Experimental results show that our proposed model achieves an accuracy of 75.9% which outperforms other state-of-the-art methods by a significant margin. We also achieve 0.52, 75.6%, and 76.2% in terms of Matthews Correlation Coefficient (MCC), Sensitivity, and Specificity, respectively. iACP-RF as a standalone tool and its source code are publicly available at: https://github.com/MLBC-lab/iACP-RF.

[Display omitted] • mACPpred 2.0 is an upgraded version, that utilizes an up-to-date anticancer peptide (ACP) benchmark dataset. • mACPpred 2.0 leverages advanced stacking deep learning approach and integrates spatial and probabilistic feature representations. • Rigorous cross-validation and independent tests conclusively demonstrated that mACPpred 2.0 not only surpasses its predecessor (mACPpred) but also outperforms existing predictors. • mACPpred 2.0 is now publicly accessible at https://balalab-skku.org/mACPpred2/ , and its standalone program is available at https://github.com/nhattruongpham/mACPpred2/ for local deployment. Anticancer peptides (ACPs), naturally occurring molecules with remarkable potential to target and kill cancer cells. However, identifying ACPs based solely from their primary amino acid sequences remains a major hurdle in immunoinformatics. In the past, several web-based machine learning (ML) tools have been proposed to assist researchers in identifying potential ACPs for further testing. Notably, our meta-approach method, mACPpred, introduced in 2019, has significantly advanced the field of ACP research. Given the exponential growth in the number of characterized ACPs, there is now a pressing need to create an updated version of mACPpred. To develop mACPpred 2.0, we constructed an up-to-date benchmarking dataset by integrating all publicly available ACP datasets. We employed a large-scale of feature descriptors, encompassing both conventional feature descriptors and advanced pre-trained natural language processing (NLP)-based embeddings. We evaluated their ability to discriminate between ACPs and non-ACPs using eleven different classifiers. Subsequently, we employed a stacked deep learning (SDL) approach, incorporating 1D convolutional neural network (1D CNN) blocks and hybrid features. These features included the top seven performing NLP-based features and 90 probabilistic features, allowing us to identify hidden patterns within these diverse features and improve the accuracy of our ACP prediction model. This is the first study to integrate spatial and probabilistic feature representations for predicting ACPs. Rigorous cross-validation and independent tests conclusively demonstrated that mACPpred 2.0 not only surpassed its predecessor (mACPpred) but also outperformed the existing state-of-the-art predictors, highlighting the importance of advanced feature representation capabilities attained through SDL. To facilitate widespread use and accessibility, we have developed a user-friendly for mACPpred 2.0, available at https://balalab-skku.org/mACPpred2/. [ABSTRACT FROM AUTHOR]

In the modern technological era, Anti-cancer peptides (ACPs) have been considered a promising cancer treatment. It's critical to find new ACPs to ensure a better knowledge of their functioning processes and vaccine development. Thus, timely and efficient ACPs using a computational technique are highly needed because of the enormous peptide sequences generated in the post-genomic era. Recently, numerous adaptive statistical algorithms have been developed for separating ACPs and NACPs. Despite great advancements, existing approaches still have insufficient feature descriptors and learning methods, limiting predictive performance. To address this, a trustworthy framework is developed for the precise identification of ACPs. Particularly, the presented approach incorporates four hypothetical feature encoding mechanisms namely: amino acid, dipeptide, tripeptide, and an improved version of pseudo amino acid composition are applied to indicate the motif of the target class. Moreover, principal component analysis (PCA) is employed for feature pruning, while selecting optimal, deep, and highly variated features. Due to the diverse nature of learning, experiments are performed over numerous algorithms to select the optimum operating method. After investigating the empirical outcomes, the support vector machine with hybrid feature space shows better performance. The proposed framework achieved an accuracy of 97.09% and 98.25% over the benchmark and independent datasets, respectively. The comparative analysis demonstrates that our proposed model outperforms as compared to the existing methods and is beneficial in drug development, and oncology. [ABSTRACT FROM AUTHOR]

Anticancer peptides (ACPs) are short protein sequences; they perform functions like some hormones and enzymes inside the body. The role of any protein or peptide is related to its structure and the sequence of amino acids that make up it. There are 20 types of amino acids in humans, and each of them has a particular characteristic according to its chemical structure. Current machine and deep learning models have been used to classify ACPs problems. However, these models have neglected Amino Acid Repeats (AARs) that play an essential role in the function and structure of peptides. Therefore, in this paper, ACPs offer a promising route for novel anticancer peptides by extracting AARs based on N-Grams and k-mers using two peptides' datasets. These datasets pointed to breast and lung cancer cells assembled and curated manually from the Cancer Peptide and Protein Database (CancerPPD). Every dataset consists of a sequence of peptides and their synthesis and anticancer activity on breast and lung cancer cell lines. Five different feature selection methods were used in this paper to improve classification performance and reduce the experimental costs. After that, ACPs were classified using four classifiers, namely AdaBoost, Random Forest Tree (RFT), Multi-class Support Vector Machine (SVM), and Multi-Layer Perceptron (MLP). These classifiers were evaluated by applying five well-known evaluation metrics. Experimental results showed that the breast and lung ACPs classification process provided an accurate performance that reached 89.25% and 92.56%, respectively. In terms of AUC, it reached 95.35% and 96.92% for both breast and lung ACPs, respectively. The proposed classifiers performed competently somewhat equally in AUC, accuracy, precision, F-measures, and recall, except for Multi-class SVM-based feature selection, which showed superior performance. As a result, this paper significantly improved the predictive performance that can effectively distinguish ACPs as virtual inactive, experimental inactive, moderately active, and very active. [ABSTRACT FROM AUTHOR]

Cancer is a group of diseases causing abnormal cell growth, altering the genome, and invading or spreading to other parts of the body. Among therapeutic peptide drugs, anticancer peptides (ACPs) have been considered to target and kill cancer cells because cancer cells have unique characteristics such as a high negative charge and abundance of microvilli in the cell membrane when compared to a normal cell. ACPs have several advantages, such as high specificity, cost-effectiveness, low immunogenicity, minimal toxicity, and high tolerance under normal physiological conditions. However, the development and identification of ACPs are time-consuming and expensive in traditional wet-lab-based approaches. Thus, the application of artificial intelligence on the approaches can save time and reduce the cost to identify candidate ACPs. Recently, machine learning (ML), deep learning (DL), and hybrid learning (ML combined DL) have emerged into the development of ACPs without experimental analysis, owing to advances in computer power and big data from the power system. Additionally, we suggest that combination therapy with classical approaches and ACPs might be one of the impactful approaches to increase the efficiency of cancer therapy. [ABSTRACT FROM AUTHOR]

Cancer is a group of diseases causing abnormal cell growth, altering the genome, and invading or spreading to other parts of the body. Among therapeutic peptide drugs, anticancer peptides (ACPs) have been considered to target and kill cancer cells because cancer cells have unique characteristics such as a high negative charge and abundance of microvilli in the cell membrane when compared to a normal cell. ACPs have several advantages, such as high specificity, cost-effectiveness, low immunogenicity, minimal toxicity, and high tolerance under normal physiological conditions. However, the development and identification of ACPs are time-consuming and expensive in traditional wet-lab-based approaches. Thus, the application of artificial intelligence on the approaches can save time and reduce the cost to identify candidate ACPs. Recently, machine learning (ML), deep learning (DL), and hybrid learning (ML combined DL) have emerged into the development of ACPs without experimental analysis, owing to advances in computer power and big data from the power system. Additionally, we suggest that combination therapy with classical approaches and ACPs might be one of the impactful approaches to increase the efficiency of cancer therapy.

In the modern technological era, Anti-cancer peptides (ACPs) have been considered a promising cancer treatment. It’s critical to find new ACPs to ensure a better knowledge of their functioning processes and vaccine development. Thus, timely and efficient ACPs using a computational technique are highly needed because of the enormous peptide sequences generated in the post-genomic era. Recently, numerous adaptive statistical algorithms have been developed for separating ACPs and NACPs. Despite great advancements, existing approaches still have insufficient feature descriptors and learning methods, limiting predictive performance. To address this, a trustworthy framework is developed for the precise identification of ACPs. Particularly, the presented approach incorporates four hypothetical feature encoding mechanisms namely: amino acid, dipeptide, tripeptide, and an improved version of pseudo amino acid composition are applied to indicate the motif of the target class. Moreover, principal component analysis (PCA) is employed for feature pruning, while selecting optimal, deep, and highly variated features. Due to the diverse nature of learning, experiments are performed over numerous algorithms to select the optimum operating method. After investigating the empirical outcomes, the support vector machine with hybrid feature space shows better performance. The proposed framework achieved an accuracy of 97.09% and 98.25% over the benchmark and independent datasets, respectively. The comparative analysis demonstrates that our proposed model outperforms as compared to the existing methods and is beneficial in drug development, and oncology.

Anticancer peptides (ACPs) are short protein sequences; they perform functions like some hormones and enzymes inside the body. The role of any protein or peptide is related to its structure and the sequence of amino acids that make up it. There are 20 types of amino acids in humans, and each of them has a particular characteristic according to its chemical structure. Current machine and deep learning models have been used to classify ACPs problems. However, these models have neglected Amino Acid Repeats (AARs) that play an essential role in the function and structure of peptides. Therefore, in this paper, ACPs offer a promising route for novel anticancer peptides by extracting AARs based on N-Grams and k-mers using two peptides’ datasets. These datasets pointed to breast and lung cancer cells assembled and curated manually from the Cancer Peptide and Protein Database (CancerPPD). Every dataset consists of a sequence of peptides and their synthesis and anticancer activity on breast and lung cancer cell lines. Five different feature selection methods were used in this paper to improve classification performance and reduce the experimental costs. After that, ACPs were classified using four classifiers, namely AdaBoost, Random Forest Tree (RFT), Multi-class Support Vector Machine (SVM), and Multi-Layer Perceptron (MLP). These classifiers were evaluated by applying five well-known evaluation metrics. Experimental results showed that the breast and lung ACPs classification process provided an accurate performance that reached 89.25% and 92.56%, respectively. In terms of AUC, it reached 95.35% and 96.92% for both breast and lung ACPs, respectively. The proposed classifiers performed competently somewhat equally in AUC, accuracy, precision, F-measures, and recall, except for Multi-class SVM-based feature selection, which showed superior performance. As a result, this paper significantly improved the predictive performance that can effectively distinguish ACPs as virtual inactive, experimental inactive, moderately active, and very active.

Background: Millions of people are suffering from cancers, but accurate early diagnosis and effective treatment are still tough for all doctors. Common ways against cancer include surgical operation, radiotherapy and chemotherapy. However, they are all very harmful for patients. Recently, the anticancer peptides (ACPs) have been discovered to be a potential way to treat cancer. Since ACPs are natural biologics, they are safer than other methods. However, the experimental technology is an expensive way to find ACPs so we purpose a new machine learning method to identify the ACPs. Results: Firstly, we extracted the feature of ACPs in two aspects: sequence and chemical characteristics of amino acids. For sequence, average 20 amino acids composition was extracted. For chemical characteristics, we classified amino acids into six groups based on the patterns of hydrophobic and hydrophilic residues. Then, deep belief network has been used to encode the features of ACPs. Finally, we purposed Random Relevance Vector Machines to identify the true ACPs. We call this method 'DRACP' and tested the performance of it on two independent datasets. Its AUC and AUPR are higher than 0.9 in both datasets. Conclusion: We developed a novel method named 'DRACP' and compared it with some traditional methods. The cross-validation results showed its effectiveness in identifying ACPs. [ABSTRACT FROM AUTHOR]

Cancer imposes a global health burden as it represents one of the leading causes of morbidity and mortality while also giving rise to significant economic burden owing to the associated expenditures for its monitoring and treatment. In spite of advancements in cancer therapy, the low success rate and recurrence of tumor has necessitated the ongoing search for new therapeutic agents. Aside from drugs based on small molecules and protein-based biopharmaceuticals, there has been an intense effort geared towards the development of peptide-based therapeutics owing to its favorable and intrinsic properties of being relatively small, highly selective, potent, safe and low in production costs. In spite of these advantages, there are several inherent weaknesses that are in need of attention in the design and development of therapeutic peptides. An abundance of data on bioactive and therapeutic peptides have been accumulated over the years and the burgeoning area of artificial intelligence has set the stage for the lucrative utilization of machine learning to make sense of these large and high-dimensional data. This review summarizes the current state-of-the-art on the application of machine learning for studying the bioactivity of anticancer peptides along with future outlook of the field. Data and R codes used in the analysis herein are available on GitHub at https://github.com/Shoombuatong2527/anticancer-peptides-review. [ABSTRACT FROM AUTHOR]

Anticancer peptides (ACPs) have been emerged as a potential safe therapeutic agent for treating cancer. Identifying novel ACPs is crucial for understanding deep insight their functional mechanisms and vaccine production. Conventional wet-lab technological methods for finding ACPs are overpriced, slow, and resource-intensive. Thus, fast and accurate ACPs prediction through computational approach is highly desired because of massive peptide sequences accumulated in the post-genomic era. Recently, several intelligent statistical approaches have been designed for discriminating ACPs from non-ACPs. Although remarkable achievements have been accomplished, available methods still have inadequate feature descriptors and learning algorithms, thereby restricting the predictive performance. To address this, we develop a novel predictor called Stack-ACPred for the correct identification of ACPs. More specifically, the proposed method possesses three nominal feature encoding strategies i.e., evolutionary-profile and physicochemical information as segmented position-specific scoring matrix (SegPSSM), pseudo (PsePSSM), and extended pseudo amino acid composition (PseAAC). The extracted features are serially fused and further optimized through a powerful support vector machine recursive feature elimination and correlation bias reduction (SVM-RFE ​+ ​CBR) algorithm. The optimal selected attributes are provided to build the stacking-base ensemble model for targeting effective ACPs. The proposed StackACPred attained 84.45% and 86.21% accuracy based on ACP740 and ACP240 datasets with 5-fold cross-validation test, which was 2.97% and 0.79% higher than other existing studies, respectively. The empirical outcomes of our developed automated tool demonstrate the excellent discriminative power for annotating large scale ACPs in particular and other peptides in general. • We developed an intelligent predictor named StackACPred for correct identification of ACPs. • Three nominal feature encoding strategies on the bases of evolutionary-profile and physicochemical information as: N-Segmentation position-specific scoring matrix (N-SegPSSM), pseudo (PsePSSM), and extended pseudo amino acid composition (PseAAC). • Powerful support vector machine recursive feature elimination and correlation bias reduction (SVM-RFE ​+ ​CBR) algorithm was used to select the optimal features. • LightGMB and stacking-base ensemble classifiers were used for predicting ACPs with k-fold cross-validation test. • StackACPred produced better results than others state-of-the-art predictors. [ABSTRACT FROM AUTHOR]

Anticancer peptides (ACPs) are promising therapeutic agents for targeting and killing cancer cells. The accurate prediction of ACPs from given peptide sequences remains as an open problem in the field of immunoinformatics. Recently, machine learning algorithms have emerged as a promising tool for helping experimental scientists predict ACPs. However, the performance of existing methods still needs to be improved. In this study, we present a novel approach for the accurate prediction of ACPs, which involves the following two steps: (i) We applied a two-step feature selection protocol on seven feature encodings that cover various aspects of sequence information (composition-based, physicochemical properties and profiles) and obtained their corresponding optimal feature-based models. The resultant predicted probabilities of ACPs were further utilized as feature vectors. (ii) The predicted probability feature vectors were in turn used as an input to support vector machine to develop the final prediction model called mACPpred. Cross-validation analysis showed that the proposed predictor performs significantly better than individual feature encodings. Furthermore, mACPpred significantly outperformed the existing methods compared in this study when objectively evaluated on an independent dataset. [ABSTRACT FROM AUTHOR]