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Abstract: Since December 2019, the new SARS-CoV-2-related COVID-19 disease has caused a global pandemic and shut down the public life worldwide. Several proteins have emerged as potential therapeutic targets for drug development, and we sought out to review the commercially available and marketed SARS-CoV-2-targeted libraries ready for high-throughput virtual screening (HTVS). We evaluated the SARS-CoV-2-targeted, protease-inhibitor-focused and protein–protein-interactioninhibitor-focused libraries to gain a better understanding of how these libraries were designed. The most common were ligand- and structure-based approaches, along with various filtering steps, using molecular descriptors. Often, these methods were combined to obtain the final library. We recognized the abundance of targeted libraries offered and complimented by the inclusion of analytical data; however, serious concerns had to be raised. Namely, vendors lack the information on the library design and the references to the primary literature. Few references to active compounds were also provided when using the ligand-based design and usually only protein classes or a general panel of targets were listed, along with a general reference to the methods, such as molecular docking for the structure-based design. No receptor data, docking protocols or even references to the applied molecular docking software (or other HTVS software), and no pharmacophore or filter design details were given. No detailed functional group or chemical space analyses were reported, and no specific orientation of the libraries toward the design of covalent or noncovalent inhibitors could be observed. All libraries contained pan-assay interference compounds (PAINS), rapid elimination of swill compounds (REOS) and aggregators, as well as focused on the drug-like model, with the majority of compounds possessing their molecular mass around 500 g/mol. These facts do not bode well for the use of the reviewed libraries in drug design and lend themselves to commercial drug companies to focus on and improve.
Keywords: targeted libraries, focused libraries, computer-aided drug design, virtual screening, in silico drug design, high-throughput virtual screening
Published in DKUM: 09.04.2025; Views: 0; Downloads: 1
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Abstract: To equip computers with human communication skills and to enable natural interaction between the computer and a human, intelligent solutions are required based on artificial intelligence (AI) methods, algorithms, and sensor technology. This study aimed at identifying and analyzing the state-of-the-art AI methods and algorithms and sensors technology in existing human–computer intelligent interaction (HCII) research to explore trends in HCII research, categorize existing evidence, and identify potential directions for future research. We conduct a systematic mapping study of the HCII body of research. Four hundred fifty-four studies published in various journals and conferences between 2010 and 2021 were identified and analyzed. Studies in the HCII and IUI fields have primarily been focused on intelligent recognition of emotion, gestures, and facial expressions using sensors technology, such as the camera, EEG, Kinect, wearable sensors, eye tracker, gyroscope, and others. Researchers most often apply deep-learning and instance-based AI methods and algorithms. The support sector machine (SVM) is the most widely used algorithm for various kinds of recognition, primarily an emotion, facial expression, and gesture. The convolutional neural network (CNN) is the often-used deep-learning algorithm for emotion recognition, facial recognition, and gesture recognition solutions.
Keywords: human–computer intelligent interaction, intelligent user interfaces, IUI, sensors, artificial intelligence
Published in DKUM: 31.03.2025; Views: 0; Downloads: 3
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Keywords: computer-assisted drug design, CADD, computer-assisted drug design, furin inhibitors, protease inhibitors, antivirals, protease inhibitors, furin assay, antiviral drug design
Published in DKUM: 20.03.2025; Views: 0; Downloads: 1
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Keywords: brain-computer interfaces, electroencephalogram, feature and channel investigation, feature selection, machine learning, motor imagery task classification
Published in DKUM: 19.12.2024; Views: 0; Downloads: 4
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Abstract: The 2024 Student Computing Research Symposium (SCORES 2024), organized by the Faculty of Electrical Engineering and Computer Science at the University of Maribor (UM FERI) in collaboration with the University of Ljubljana and the University of Primorska, showcases innovative student research in computer science. This year’s symposium highlights advancements in fields such as artificial intelligence, data science, machine learning algorithms, computational problem-solving, and healthcare data analysis. The primary goal of SCORES 2024 is to provide a platform for students to present their research, fostering early engagement in academic inquiry. Beyond research presentations, the symposium seeks to create an environment where students from different institutions can meet, exchange ideas, and build lasting connections. It aims to cultivate friendships and future research collaborations among emerging scholars. Additionally, the conference offers an opportunity for students to interact with senior researchers from institutions beyond their own, promoting mentorship and broader academic networking.
Keywords: student conference, computer and information science, artificial intelligence, data science, data mining
Published in DKUM: 18.09.2024; Views: 0; Downloads: 32
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