Pub Date : 2026-06-01Epub Date: 2026-01-18DOI: 10.1016/j.ailsci.2026.100156
Ivon Acosta-Ramirez , Ferhat Sadak , Sruti Das Choudhury , James Thomson , Salome Perez-Rosero , Portia N.A. Plange , Sofia E. Morales-Mendivelso , Nicole M. Iverson
Detecting the spatial release of extracellular nitric oxide (NO) is essential for understanding the dynamics in cell communication for physiological and pathological processes. This study presents an innovative methodology that integrates fluorescence-based sensing platforms utilizing single walled carbon nanotubes (SWNT) with machine learning models to expedite the spatial data analysis of extracellular analytes. The deep learning model You Only Look Once (YOLOv8) segmentation achieves accurate cell identification across diverse morphologies and clustered cell groups, with a recall of 98% and a precision of 83%. The spatial analysis of extracellular NO is achieved by extracting the cell contour coordinates from the YOLO-identified cells and translocating the boundaries onto SWNT fluorescence files. The model enables rapid analysis for multiple cells across numerous images, with 100 image pairs completed in just 68 s. The combination of nanotechnology with automated neural network-based cell detection establishes a robust sensing framework with pixel-level spatial resolution of NO dynamics, delivering critical insights into cellular communication and holding promising implications for diagnostic and therapeutic applications.
检测细胞外一氧化氮（NO）的空间释放对于理解生理和病理过程中细胞通讯的动力学至关重要。本研究提出了一种创新的方法，将利用单壁碳纳米管（SWNT）的基于荧光的传感平台与机器学习模型相结合，以加快细胞外分析物的空间数据分析。深度学习模型You Only Look Once （YOLOv8）分割在不同形态和集群细胞群中实现了准确的细胞识别，召回率为98%，精度为83%。细胞外NO的空间分析是通过从yolo识别的细胞中提取细胞轮廓坐标并将边界转移到SWNT荧光文件中来实现的。该模型可以快速分析众多图像中的多个细胞，只需68秒即可完成100对图像。纳米技术与基于自动神经网络的细胞检测相结合，建立了一个具有NO动态像素级空间分辨率的强大传感框架，为细胞通信提供了关键见解，并为诊断和治疗应用带来了希望。
{"title":"Development of a deep neural network model for simultaneous analysis of extracellular analyte gradients for a population of cells","authors":"Ivon Acosta-Ramirez , Ferhat Sadak , Sruti Das Choudhury , James Thomson , Salome Perez-Rosero , Portia N.A. Plange , Sofia E. Morales-Mendivelso , Nicole M. Iverson","doi":"10.1016/j.ailsci.2026.100156","DOIUrl":"10.1016/j.ailsci.2026.100156","url":null,"abstract":"<div><div>Detecting the spatial release of extracellular nitric oxide (NO) is essential for understanding the dynamics in cell communication for physiological and pathological processes. This study presents an innovative methodology that integrates fluorescence-based sensing platforms utilizing single walled carbon nanotubes (SWNT) with machine learning models to expedite the spatial data analysis of extracellular analytes. The deep learning model You Only Look Once (YOLOv8) segmentation achieves accurate cell identification across diverse morphologies and clustered cell groups, with a recall of 98% and a precision of 83%. The spatial analysis of extracellular NO is achieved by extracting the cell contour coordinates from the YOLO-identified cells and translocating the boundaries onto SWNT fluorescence files. The model enables rapid analysis for multiple cells across numerous images, with 100 image pairs completed in just 68 s. The combination of nanotechnology with automated neural network-based cell detection establishes a robust sensing framework with pixel-level spatial resolution of NO dynamics, delivering critical insights into cellular communication and holding promising implications for diagnostic and therapeutic applications.</div></div>","PeriodicalId":72304,"journal":{"name":"Artificial intelligence in the life sciences","volume":"9 ","pages":"Article 100156"},"PeriodicalIF":5.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146038571","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-01Epub Date: 2025-09-17DOI: 10.1016/j.ailsci.2025.100142
Sofia Larsson , Miranda Carlsson , Richard Beckmann , Filip Miljković , Rocío Mercado
Metabolite identification studies are an essential but costly and time-consuming component of drug development. Computational methods have the potential to accelerate early-stage drug discovery, particularly with recent advances in deep learning which offer new opportunities to accelerate the process of metabolite prediction. We present LAGOM (Language-model Assisted Generation Of Metabolites), a Transformer-based approach built upon the Chemformer architecture, designed to predict likely metabolic transformations of drug candidates. Our results show that LAGOM performs competitively with, and in some cases surpasses, existing state-of-the-art metabolite prediction tools, demonstrating the potential of language-model-based architectures in chemoinformatics. By integrating diverse data sources and employing data augmentation strategies, we further improve the model’s generalisation and predictive accuracy. The implementation of LAGOM is publicly available at github.com/tsofiac/LAGOM.
{"title":"LAGOM: A transformer-based chemical language model for drug metabolite prediction","authors":"Sofia Larsson , Miranda Carlsson , Richard Beckmann , Filip Miljković , Rocío Mercado","doi":"10.1016/j.ailsci.2025.100142","DOIUrl":"10.1016/j.ailsci.2025.100142","url":null,"abstract":"<div><div>Metabolite identification studies are an essential but costly and time-consuming component of drug development. Computational methods have the potential to accelerate early-stage drug discovery, particularly with recent advances in deep learning which offer new opportunities to accelerate the process of metabolite prediction. We present LAGOM (Language-model Assisted Generation Of Metabolites), a Transformer-based approach built upon the Chemformer architecture, designed to predict likely metabolic transformations of drug candidates. Our results show that LAGOM performs competitively with, and in some cases surpasses, existing state-of-the-art metabolite prediction tools, demonstrating the potential of language-model-based architectures in chemoinformatics. By integrating diverse data sources and employing data augmentation strategies, we further improve the model’s generalisation and predictive accuracy. The implementation of LAGOM is publicly available at <span><span>github.com/tsofiac/LAGOM</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":72304,"journal":{"name":"Artificial intelligence in the life sciences","volume":"8 ","pages":"Article 100142"},"PeriodicalIF":5.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145104218","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-01Epub Date: 2025-10-24DOI: 10.1016/j.ailsci.2025.100145
Jason Granstedt, Prabhat Kc, Rucha Deshpande, Victor Garcia, Aldo Badano
Computer methods in medical devices are frequently imperfect and are known to produce errors in clinical or diagnostic tasks. However, when deep learning and data-based approaches yield output that exhibit errors, the devices are frequently said to hallucinate. Drawing from theoretical developments and empirical studies in multiple medical device areas, we introduce a practical and universal definition that denotes hallucinations as a type of error that is plausible and can be either impactful or benign to the task at hand. The definition aims at facilitating the evaluation of medical devices that suffer from hallucinations across product areas. Using examples from imaging and non-imaging applications, we explore how the proposed definition relates to evaluation methodologies and discuss existing approaches for minimizing the prevalence of hallucinations.
{"title":"Hallucinations in medical devices","authors":"Jason Granstedt, Prabhat Kc, Rucha Deshpande, Victor Garcia, Aldo Badano","doi":"10.1016/j.ailsci.2025.100145","DOIUrl":"10.1016/j.ailsci.2025.100145","url":null,"abstract":"<div><div>Computer methods in medical devices are frequently imperfect and are known to produce errors in clinical or diagnostic tasks. However, when deep learning and data-based approaches yield output that exhibit errors, the devices are frequently said to hallucinate. Drawing from theoretical developments and empirical studies in multiple medical device areas, we introduce a practical and universal definition that denotes hallucinations as a type of error that is plausible and can be either impactful or benign to the task at hand. The definition aims at facilitating the evaluation of medical devices that suffer from hallucinations across product areas. Using examples from imaging and non-imaging applications, we explore how the proposed definition relates to evaluation methodologies and discuss existing approaches for minimizing the prevalence of hallucinations.</div></div>","PeriodicalId":72304,"journal":{"name":"Artificial intelligence in the life sciences","volume":"8 ","pages":"Article 100145"},"PeriodicalIF":5.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145473665","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-01Epub Date: 2025-11-23DOI: 10.1016/j.ailsci.2025.100147
Yinli Shi , Jun Liu , Sicun Wang , Shuang Guan , Muzhi Li , Yanan Yu , Hu Yang , Wei Yang , Bing Li , Weibin Yang , Xuezhong Zhou , Zhong Wang
Although combination drug therapies hold great promise for complex diseases, their development is hindered by the complexity of biological networks and the combinatorial explosion of possible drug interactions. Deep learning (DL) models offer a transformative solution by integrating multimodal data and biomedical networks to predict drug combination synergy with high accuracy. These models automatically extract complex patterns from high-dimensional data, overcoming limitations of conventional methods, accelerating rational combination discovery. Here, we systematically examined diverse network-based DL frameworks, analyzing how increasing structural complexity enhances prediction performance while maintaining interpretability. While current methodologies show encouraging results, challenges remain in data quality, model generalization, and clinical translation. Here, we highlight pivotal studies demonstrating in different DL models’ potential, outlines their key limitations, and discusses future directions including multimodal learning and mechanistic interpretability, to establish multilayer DL model as a cornerstone of next-generation drug combination discovery.
{"title":"Drug discovery of synergistic combinations via multilayer deep learning models:Advances and challenges","authors":"Yinli Shi , Jun Liu , Sicun Wang , Shuang Guan , Muzhi Li , Yanan Yu , Hu Yang , Wei Yang , Bing Li , Weibin Yang , Xuezhong Zhou , Zhong Wang","doi":"10.1016/j.ailsci.2025.100147","DOIUrl":"10.1016/j.ailsci.2025.100147","url":null,"abstract":"<div><div>Although combination drug therapies hold great promise for complex diseases, their development is hindered by the complexity of biological networks and the combinatorial explosion of possible drug interactions. Deep learning (DL) models offer a transformative solution by integrating multimodal data and biomedical networks to predict drug combination synergy with high accuracy. These models automatically extract complex patterns from high-dimensional data, overcoming limitations of conventional methods, accelerating rational combination discovery. Here, we systematically examined diverse network-based DL frameworks, analyzing how increasing structural complexity enhances prediction performance while maintaining interpretability. While current methodologies show encouraging results, challenges remain in data quality, model generalization, and clinical translation. Here, we highlight pivotal studies demonstrating in different DL models’ potential, outlines their key limitations, and discusses future directions including multimodal learning and mechanistic interpretability, to establish multilayer DL model as a cornerstone of next-generation drug combination discovery.</div></div>","PeriodicalId":72304,"journal":{"name":"Artificial intelligence in the life sciences","volume":"8 ","pages":"Article 100147"},"PeriodicalIF":5.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145623310","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-01Epub Date: 2025-08-20DOI: 10.1016/j.ailsci.2025.100137
Raphaël Rubrice , Virgile Gueneau , Romain Briandet , Antoine Cornuejols , Vincent Guigue
Biofilms are structured microbial communities that promote cell interactions through close spatial organization, leading to cooperative or competitive behaviors. Predicting microbial interactions in biofilms could aid in developing innovative strategies to prevent the colonization of undesirable bacteria. Here, we present a machine learning approach to predict the antagonistic effects of beneficial bacterial candidates Bacillus and Paenibacillus species against undesirable bacteria (Staphylococcus aureus, Enterococcus cecorum, Escherichia coli and Salmonella enterica), based on the morphological descriptors of single-species biofilms. We trained the models using quantitative features (e.g. biofilm volume, thickness, roughness, or substratum coverage). As a proxy for antagonism, an exclusion score was used as the supervised training target. The latter was calculated based on the ratio of biofilm volume between the undesirable bacteria and the beneficial strain. We subsequently applied diverse explainability methods to analyze the resulting model and found insights highlighting the importance of biofilm formation context when predicting antagonism. Our results demonstrate that machine learning offers an efficient, data-driven tool to predict microbial interactions within biofilms and support the selection of competitive beneficial strains against pathogens. This approach enables scalable screening of microbial interactions, making it applicable to both research and biotechnological applications.
{"title":"A machine learning framework for the prediction and analysis of bacterial antagonism in biofilms using morphological descriptors","authors":"Raphaël Rubrice , Virgile Gueneau , Romain Briandet , Antoine Cornuejols , Vincent Guigue","doi":"10.1016/j.ailsci.2025.100137","DOIUrl":"10.1016/j.ailsci.2025.100137","url":null,"abstract":"<div><div>Biofilms are structured microbial communities that promote cell interactions through close spatial organization, leading to cooperative or competitive behaviors. Predicting microbial interactions in biofilms could aid in developing innovative strategies to prevent the colonization of undesirable bacteria. Here, we present a machine learning approach to predict the antagonistic effects of beneficial bacterial candidates <em>Bacillus</em> and <em>Paenibacillus</em> species against undesirable bacteria (<em>Staphylococcus aureus</em>, <em>Enterococcus cecorum</em>, <em>Escherichia coli</em> and <em>Salmonella enterica</em>), based on the morphological descriptors of single-species biofilms. We trained the models using quantitative features (e.g. biofilm volume, thickness, roughness, or substratum coverage). As a proxy for antagonism, an exclusion score was used as the supervised training target. The latter was calculated based on the ratio of biofilm volume between the undesirable bacteria and the beneficial strain. We subsequently applied diverse explainability methods to analyze the resulting model and found insights highlighting the importance of biofilm formation context when predicting antagonism. Our results demonstrate that machine learning offers an efficient, data-driven tool to predict microbial interactions within biofilms and support the selection of competitive beneficial strains against pathogens. This approach enables scalable screening of microbial interactions, making it applicable to both research and biotechnological applications.</div></div>","PeriodicalId":72304,"journal":{"name":"Artificial intelligence in the life sciences","volume":"8 ","pages":"Article 100137"},"PeriodicalIF":5.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144893291","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-01Epub Date: 2025-07-10DOI: 10.1016/j.ailsci.2025.100132
Hannah Rosa Friesacher , Emma Svensson , Susanne Winiwarter , Lewis Mervin , Adam Arany , Ola Engkvist
The estimation of uncertainties associated with predictions from quantitative structure–activity relationship (QSAR) models can accelerate the drug discovery process by identifying promising experiments and allowing an efficient allocation of resources. Several computational tools exist that estimate the predictive uncertainty in machine learning models. However, deviations from the i.i.d. setting have been shown to impair the performance of these uncertainty quantification methods. We use a real-world pharmaceutical dataset to address the pressing need for a comprehensive, large-scale evaluation of uncertainty quantification approaches in the context of realistic distribution shifts over time. We investigate the performance of several popular uncertainty estimation methods for classification models, including ensemble-based and Bayesian approaches. Furthermore, we use this real-world setting to systematically assess the distribution shifts in label and descriptor space and their impact on the capability of the uncertainty quantification methods. Our study reveals significant shifts over time in both label and descriptor space and a clear connection between the magnitude of the shift and the nature of the assay. Moreover, we show that pronounced distribution shifts impair the performance of popular uncertainty quantification methods used in QSAR models. This work highlights the challenges of identifying uncertainty quantification techniques that remain reliable under distribution shifts introduced by real-world data.
{"title":"Temporal distribution shift in real-world pharmaceutical data: Implications for uncertainty quantification in QSAR models","authors":"Hannah Rosa Friesacher , Emma Svensson , Susanne Winiwarter , Lewis Mervin , Adam Arany , Ola Engkvist","doi":"10.1016/j.ailsci.2025.100132","DOIUrl":"10.1016/j.ailsci.2025.100132","url":null,"abstract":"<div><div>The estimation of uncertainties associated with predictions from quantitative structure–activity relationship (QSAR) models can accelerate the drug discovery process by identifying promising experiments and allowing an efficient allocation of resources. Several computational tools exist that estimate the predictive uncertainty in machine learning models. However, deviations from the i.i.d. setting have been shown to impair the performance of these uncertainty quantification methods. We use a real-world pharmaceutical dataset to address the pressing need for a comprehensive, large-scale evaluation of uncertainty quantification approaches in the context of realistic distribution shifts over time. We investigate the performance of several popular uncertainty estimation methods for classification models, including ensemble-based and Bayesian approaches. Furthermore, we use this real-world setting to systematically assess the distribution shifts in label and descriptor space and their impact on the capability of the uncertainty quantification methods. Our study reveals significant shifts over time in both label and descriptor space and a clear connection between the magnitude of the shift and the nature of the assay. Moreover, we show that pronounced distribution shifts impair the performance of popular uncertainty quantification methods used in QSAR models. This work highlights the challenges of identifying uncertainty quantification techniques that remain reliable under distribution shifts introduced by real-world data.</div></div>","PeriodicalId":72304,"journal":{"name":"Artificial intelligence in the life sciences","volume":"8 ","pages":"Article 100132"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144633004","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-01Epub Date: 2025-11-22DOI: 10.1016/j.ailsci.2025.100146
Jason Granstedt, Prabhat Kc, Rucha Deshpande, Victor Garcia, Aldo Badano
{"title":"Corrigendum to “Hallucinations in medical devices” [Artif. Intell. Life Sci. 8 (2025) 100145]","authors":"Jason Granstedt, Prabhat Kc, Rucha Deshpande, Victor Garcia, Aldo Badano","doi":"10.1016/j.ailsci.2025.100146","DOIUrl":"10.1016/j.ailsci.2025.100146","url":null,"abstract":"","PeriodicalId":72304,"journal":{"name":"Artificial intelligence in the life sciences","volume":"8 ","pages":"Article 100146"},"PeriodicalIF":5.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145747425","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-01Epub Date: 2025-08-23DOI: 10.1016/j.ailsci.2025.100140
Sharif Naser Makhadmeh , Yousef Sanjalawe , Mohammed Azmi Al-Betar , Ahmad Nasayreh , Mohammad Aladaileh
The DNA microarray technique involves using a chip embedded with numerous DNA sequences to simultaneously estimate the expression of a multitude of genes. This data, laid out in table format, is vital for employing pattern recognition algorithms that distinguish between samples from healthy individuals and those with cancer. However, identifying useful biomarkers within gene selection data presents significant challenges due to its vast dimensionality and the inclusion of noisy, irrelevant genes. To address these challenges, this paper introduces a sophisticated gene selection method using a robust filter called Minimum redundancy maximum relevancy, combined with a novel hybrid optimization algorithm. This algorithm integrates the Improved Marine Predator Optimizer (MPA) with the Crossover operator to form the MPAC method. The MPAC specifically aims to identify a concise set of biomarker genes that substantially improve cancer classification performance. It employs the k-nearest neighbor algorithm for classification tasks. The innovation in MPAC lies in its ability to significantly enhance the performance of the MPA’s search agents. It seeks the most effective gene subsets for cancer biomarkers and is designed to optimize both the depth (exploitation) and breadth (exploration) of the search. The effectiveness of this hybrid approach is rigorously tested against nine well-known microarray datasets. The performance of this hybrid model is compared against other base and advanced optimization algorithms. The findings from these comparisons highlight that the proposed MPAC approach excels in most of the datasets and remains highly competitive across the others.
{"title":"A crossover-enhanced Marine Predators Algorithm for gene selection in microarray-based cancer classification","authors":"Sharif Naser Makhadmeh , Yousef Sanjalawe , Mohammed Azmi Al-Betar , Ahmad Nasayreh , Mohammad Aladaileh","doi":"10.1016/j.ailsci.2025.100140","DOIUrl":"10.1016/j.ailsci.2025.100140","url":null,"abstract":"<div><div>The DNA microarray technique involves using a chip embedded with numerous DNA sequences to simultaneously estimate the expression of a multitude of genes. This data, laid out in table format, is vital for employing pattern recognition algorithms that distinguish between samples from healthy individuals and those with cancer. However, identifying useful biomarkers within gene selection data presents significant challenges due to its vast dimensionality and the inclusion of noisy, irrelevant genes. To address these challenges, this paper introduces a sophisticated gene selection method using a robust filter called Minimum redundancy maximum relevancy, combined with a novel hybrid optimization algorithm. This algorithm integrates the Improved Marine Predator Optimizer (MPA) with the Crossover operator to form the MPAC method. The MPAC specifically aims to identify a concise set of biomarker genes that substantially improve cancer classification performance. It employs the k-nearest neighbor algorithm for classification tasks. The innovation in MPAC lies in its ability to significantly enhance the performance of the MPA’s search agents. It seeks the most effective gene subsets for cancer biomarkers and is designed to optimize both the depth (exploitation) and breadth (exploration) of the search. The effectiveness of this hybrid approach is rigorously tested against nine well-known microarray datasets. The performance of this hybrid model is compared against other base and advanced optimization algorithms. The findings from these comparisons highlight that the proposed MPAC approach excels in most of the datasets and remains highly competitive across the others.</div></div>","PeriodicalId":72304,"journal":{"name":"Artificial intelligence in the life sciences","volume":"8 ","pages":"Article 100140"},"PeriodicalIF":5.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144908437","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-01Epub Date: 2025-10-23DOI: 10.1016/j.ailsci.2025.100144
Serkan Eti , Serhat Yüksel , Seçil Topaloğlu Eti , Hasan Dinçer , Ozan Emre Eyupoglu
The rapid escalation of antibiotic resistance is diminishing the effectiveness of current treatments and poses a severe threat to global health security. Addressing this challenge requires identifying the most critical criteria in the antibiotic development process and determining which approaches yield the most effective results. However, the literature reveals a significant gap: few studies systematically analyze the factors that shape the effectiveness of antibiotic development, and even fewer comparatively evaluate the most efficient development strategies. This study aims to fill this gap by providing a scientific roadmap for decision-makers through the integration of artificial intelligence (AI) methods into a fuzzy multi-criteria decision-making (MCDM) framework. A total of 15 evaluation criteria and eight antibiotic development approaches were identified through a comprehensive literature review. Expert opinions were collected from five specialists in the field, and their relative importance was objectively quantified using a dimensionality reduction technique, a machine learning–based AI approach. Subsequently, criteria weights were calculated via the LOPCOW method, while antibiotic development strategies were ranked using the CODAS method. To further enhance the robustness of decision-making under uncertainty, the newly introduced Koch Snowflake fuzzy sets were integrated into the AI-driven framework, marking an additional innovation in fuzzy set theory. This hybrid model contributes to the literature by (i) enabling a holistic analysis of critical factors and effective strategies in antibiotic development, (ii) demonstrating how AI-based dimensionality reduction can be combined with fuzzy decision-making tools for more objective and precise outcomes, and (iii) offering a more comprehensive evaluation than previous studies by incorporating an extended set of criteria. The study’s findings reveal that the most important factor in the antibiotic development process is smart biosafety and computerized control systems (0.0904), while the optimal development strategy is artificial intelligence-assisted molecule discovery (0.504). Additionally, antibiotic repositioning was found to play a significant supporting role. By highlighting the value of integrating machine learning techniques and fuzzy AI frameworks into drug discovery processes, this research not only addresses a pressing issue in global health but also demonstrates the transformative potential of artificial intelligence in advancing life sciences and accelerating antibiotic innovation.
{"title":"Leveraging artificial intelligence and koch snowflake fuzzy sets to optimize antibiotic development pathways","authors":"Serkan Eti , Serhat Yüksel , Seçil Topaloğlu Eti , Hasan Dinçer , Ozan Emre Eyupoglu","doi":"10.1016/j.ailsci.2025.100144","DOIUrl":"10.1016/j.ailsci.2025.100144","url":null,"abstract":"<div><div>The rapid escalation of antibiotic resistance is diminishing the effectiveness of current treatments and poses a severe threat to global health security. Addressing this challenge requires identifying the most critical criteria in the antibiotic development process and determining which approaches yield the most effective results. However, the literature reveals a significant gap: few studies systematically analyze the factors that shape the effectiveness of antibiotic development, and even fewer comparatively evaluate the most efficient development strategies. This study aims to fill this gap by providing a scientific roadmap for decision-makers through the integration of artificial intelligence (AI) methods into a fuzzy multi-criteria decision-making (MCDM) framework. A total of 15 evaluation criteria and eight antibiotic development approaches were identified through a comprehensive literature review. Expert opinions were collected from five specialists in the field, and their relative importance was objectively quantified using a dimensionality reduction technique, a machine learning–based AI approach. Subsequently, criteria weights were calculated via the LOPCOW method, while antibiotic development strategies were ranked using the CODAS method. To further enhance the robustness of decision-making under uncertainty, the newly introduced Koch Snowflake fuzzy sets were integrated into the AI-driven framework, marking an additional innovation in fuzzy set theory. This hybrid model contributes to the literature by (i) enabling a holistic analysis of critical factors and effective strategies in antibiotic development, (ii) demonstrating how AI-based dimensionality reduction can be combined with fuzzy decision-making tools for more objective and precise outcomes, and (iii) offering a more comprehensive evaluation than previous studies by incorporating an extended set of criteria. The study’s findings reveal that the most important factor in the antibiotic development process is smart biosafety and computerized control systems (0.0904), while the optimal development strategy is artificial intelligence-assisted molecule discovery (0.504). Additionally, antibiotic repositioning was found to play a significant supporting role. By highlighting the value of integrating machine learning techniques and fuzzy AI frameworks into drug discovery processes, this research not only addresses a pressing issue in global health but also demonstrates the transformative potential of artificial intelligence in advancing life sciences and accelerating antibiotic innovation.</div></div>","PeriodicalId":72304,"journal":{"name":"Artificial intelligence in the life sciences","volume":"8 ","pages":"Article 100144"},"PeriodicalIF":5.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145362197","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-01Epub Date: 2025-11-27DOI: 10.1016/j.ailsci.2025.100149
Marina Bilotta , Roberta Rocca , Stefano Alcaro
The integration of artificial intelligence (AI) into the drug discovery pipeline is redefining pharmaceutical research by enhancing efficiency, predictive accuracy, and innovation. Traditional drug development, constrained by high costs, long timelines, and low success rates, is being transformed through deep learning, predictive modeling, and explainable AI (XAI). These tools accelerate target identification, lead optimization, and drug repurposing by enabling high-throughput interpretation of multi-omics datasets spanning genomics, proteomics, and metabolomics. Generative models, including variational autoencoders (VAEs), generative adversarial networks (GANs), and transformer-based architectures, enable the de novo design of bioactive compounds, while reinforcement learning refines molecular properties. Structure-based drug design has been advanced by graph neural networks (GNNs) and convolutional neural networks (CNNs), improving virtual screening and binding affinity prediction. The coupling of AI with quantum chemistry enhances molecular property estimation, reducing reliance on experimental validation. AI-driven prediction of drug–target interactions (DTIs) supports both repurposing efforts and pharmacovigilance. This review presents a polypharmacology-aware, feedback-to-discovery framework, in which translational signals, such as biomarkers, molecular subtypes, and pathway constraints, are reintegrated into target selection and compound optimization to enhance decision quality. Unlike previous reviews focused on isolated AI applications, it offers a unified, end-to-end synthesis spanning target discovery to regulatory translation. We distinguish foundation models that learn transferable molecular representations from generative models that synthesize new compounds. Together with multimodal learning, explainable AI, and closed-loop design–make–test–learn systems linking molecular design to automated synthesis, these advances outline a mechanism-informed roadmap for AI-driven discovery across the modern pharmaceutical pipeline.
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