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14 articles for “Predictive Biotechnology”
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AI-Driven Predictive Biotechnology Framework for Modeling Viral Mutation Hotspots Using Genomic Sequence Analysis
Abstract: Viral mutations represent one of the most critical challenges in global public health because genetic changes in viruses can significantly influence transmissibility, virulence, immune escape capability, and resistance to antiviral treatments. RNA viruses in particular exhibit high mutation rates due to error-prone replication mechanisms, making them capable of evolving rapidly in response to environmental pressures and host immune defenses. Traditional virological research primarily relies on retrospective genomic analysis, where viral …
Published in International Journal of Virus Studies · Vol. 3, Issue 2, 2026 Read article
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Enzyme Stability Prediction using BERT and CNN-A Deep Learning Approach for Enhanced Biocatalysis
Abstract: An important factor in determining the efficacy of industrial enzymes used in various biotechnological applications is their stability. The goal of this study is to develop a predictive model for industrial enzyme stability, which is essential to the efficiency of these enzymes in biotechnological applications. The research takes a comprehensive strategy to comprehend the parameters affecting enzyme stability by combining statistical analysis, deep learning algorithms (BERT and CNN), and molecular …
Published in Research and Reviews : A Journal of Life Sciences · Vol. 14, Issue 2, 2024 · pp. 19–35 Read article
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InSilico Analysis and Homology Modeling of Tetrahydroprotoberberine Oxide involved in the Berberine Biosynthesis
Abstract: Objective: Berberine, a bioactive compound found in various plant species, exhibits diverse pharmacological properties with potential applications in pharmaceutical research. The biosynthesis of berberine involves several enzymatic steps, with (S)-tetrahydroprotoberberine oxidase playing a pivotal role. This study aimed to elucidate the structural and functional characteristics of (S)-tetrahydroprotoberberine oxidase to better understand its role in berberine biosynthesis and its potential biotechnological applications. Methods: Using bioinformatics tools and computational methods, the physicochemical …
Published in Emerging Trends in Metabolites · Vol. 1, Issue 1, 2024 · pp. 7–26 Read article
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A Review on Protein Structure Prediction
Abstract: Protein structure prediction is the inference of the three-dimensional structure of a protein from its amino acid sequence. The prediction of folding and secondary and tertiary structure from its primary structure. Structure prediction is fundamentally different from the inverse problem of protein design. Protein structure prediction is one of the most important goals pursued by bioinformatics and theoretical chemistry. It is highly important in medicine and biotechnology. Every two years, …
Published in Research & Reviews: A Journal of Bioinformatics · Vol. 3, Issue 2, 2016 · pp. 1–7 Read article
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In silico PASS-Assisted Biological Activity Predictions of a Phytoconstituent Astilbin isolated from Thelypteroid fern Sphaerostephanos arbuscula (Willd.) Holttum
Abstract: Pteridophytes are the most important land plants with enormous medicinal properties. The medicinal properties of this group of plants mainly depend upon the numerous phytoconstituents present inside the plants. In the present study, the biological activity spectra of a phytoconstituent Astilbin isolated from Thelypteroid fern Sphaerostephanos arbuscula was predicted using computer-aided program—Prediction of Activity Spectra for Substances (PASS). The PASS predicted activity of Astilbin exhibited its various probable activities (Pa). …
Published in Research and Reviews : A Journal of Biotechnology · Vol. 6, Issue 2, 2016 · pp. 24–29 Read article
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3D-QSAR Studies, Molecular Docking and Adme Property Prediction of Mannosyl Triazoles Used as Potent Antiadhesives of Uropathogenic E. coli
Abstract: Urinary tract infections (UTIs) are among the most common infectious diseases, accounting for almost five million cases annually and causing considerable morbidity and mortality. Escherichia coli is a commensal inhabitant of the mammalian large intestine and the most common cause of UTIs in humans. Increasing incidence of antibiotic-resistant E. coli causing up to 95% of these infections, calls for additional therapeutic considerations. In this study, 3D-quantitative structure-activity relationship (3D-QSAR) analysis …
Published in Research and Reviews : A Journal of Biotechnology · Vol. 6, Issue 1, 2016 · pp. 26–33 Read article
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3D-QSAR Studies, Molecular Docking and ADME Property Prediction of 3, 4-dihydroxy Pyrrolidine Derivatives Used as A Potent Glycosidase Inhibitors Against Shiga Toxin
Abstract: Diarrhea is usually brought on by gastrointestinal infection caused by bacteria. The symptoms may range from mild abdominal discomfort to full blown dysentery, characterized by cramps, diarrhea, fever, vomiting, blood, pus, or mucus in stools. The Shiga toxin—a single 30 KDa A-subunit and 7 KDa B-subunit, binding to GB3 on target eukaryotic cells, the stx-receptor complex is internalized and locates with endosomes. In this study, four potent novel glycosidase inhibitors …
Published in Research and Reviews : A Journal of Biotechnology · Vol. 5, Issue 3, 2015 · pp. 29–34 Read article
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From Quantum Chemistry to Bioprocess Intensification: Advanced Computational Modeling and Enzyme-Based Catalytic Platforms for Green Chemical Transformations
Abstract: Green chemistry requires the development of sustainable catalytic systems that minimize waste generation, reduce energy consumption, and improve process efficiency. Computational chemistry and biocatalysis have emerged as complementary approaches for environmentally responsible chemical manufacturing. Computational techniques such as quantum chemistry, density functional theory (DFT), molecular dynamics, and machine learning provide mechanistic insights into catalytic reactions and support the rational design of efficient catalysts. These approaches enable the prediction of reaction …
Published in Journal of Catalyst & Catalysis · Vol. 13, Issue 2, 2026 · pp. 45–52 Read article
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The Convergence of AI and Composites - A Review Anchored in Patent Trends
Abstract: The integration of artificial intelligence (AI) and machine learning (ML) techniques is revolutionizing the design, analysis, and optimization of polymer (PC/FRP), metal (MC), and ceramic matrix composites (CC). Techniques such as artificial neural networks (ANN), deep learning (DL), genetic algorithms (GA), and physics-informed machine learning (PIML) are employed to enhance property estimation, process optimization, and predictive modeling. These AI-driven frameworks enable virtual testing, application-specific material design, and real-time decision-making, while …
Published in Journal of Polymer & Composites · Vol. 13, Issue 6, 2025 · pp. 182–198 Read article
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Integrating Biotechnology, Physiology, and Agroecological Practices for Sustainable Crop Production and Protection
Abstract: Global agriculture is currently confronting a wide range of complex challenges, including a rapidly growing population, climate change, increasing pest and disease pressures, soil degradation, water scarcity, and the urgent need for sustainable intensification of crop production. Addressing these issues requires integrated strategies that combine crop improvement (through modern breeding and biotechnology), precision agronomic practices related to soil, irrigation, and nutrition, as well as advancements in plant physiology, molecular biology, …
Published in International Journal of Trends in Horticulture · Vol. 2, Issue 2, 2025 · pp. 1–6 Read article
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Advancements in Molecular Engineering: Innovations at the Nexus of Chemistry and Technology
Abstract: Molecular engineering, a frontier of chemistry, merges precision and innovation to design and assemble molecular structures with unprecedented control. This abstract explores recent advances, highlighting key breakthroughs and their transformative impacts. Starting with its roots in chemical synthesis and materials science, it traces the evolution towards rational design driven by computational tools and advanced characterization techniques, enabling tailored molecular architectures. A focal point is programmable molecules, where DNA nanotechnology principles …
Published in International Journal of Advance in Molecular Engineering · Vol. 1, Issue 2, 2023 · pp. 37–43 Read article
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Digital Frontiers in Life Sciences: The Transformative Role of Computing in Modern Biology
Abstract: The integration of computers in the biological sciences has revolutionized research and experimentation, facilitating advancements in areas such as genomics, bioinformatics, systems biology, and ecological modeling. The ability to process vast amounts of biological data efficiently has transformed how scientists study complex biological systems and phenomena. Computational tools enable the analysis of DNA sequences, protein structures, metabolic pathways, and ecological dynamics, which were previously beyond the reach of traditional laboratory …
Published in International Journal of Bioinformatics and Computational Biology · Vol. 4, Issue 1, 2026 · pp. 1–20 Read article
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3D-QSAR and Molecular Docking Studies of Triazol-Cycloalkanol Derivatives Used as Potent 3-Dehydroquinase II Inhibitors Against Mycobacterium tuberculosis
Abstract: Tuberculosis is a life threatening infectious disease caused by gram positive bacteria mycobacterium tuberculosis. Emergence of drug resistance, multi drug resistance and extensively drug resistance strain of mycobacterium make the situation more worst. It increases the mortality rate of tuberculosis patient, time duration and cost of treatment. Various inhibitors and drug molecules are designed to combat this fatal disease. Enzymes of shikimate pathway are responsible for production of essential amino …
Published in Research and Reviews : A Journal of Biotechnology · Vol. 5, Issue 3, 2015 · pp. 18–28 Read article
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Integrated Computational and Bio-catalytic Transformations: DFT-Guided Mechanistic Insights, Machine Learning, and Nano-biocatalyst Engineering for Sustainable Catalysis
Abstract: Computational catalysis has emerged as a transformative scientific discipline that integrates quantum chemistry, molecular modeling, machine learning, and density functional theory (DFT) to understand catalytic mechanisms and design highly efficient catalytic systems for sustainable industrial applications. The increasing global demand for environmentally responsible chemical manufacturing has accelerated research on advanced catalytic materials including transition metal catalysts, metal–organic frameworks (MOFs), homogeneous catalysts, heterogeneous systems, and bimetallic catalysts involving nickel and iron. …
Published in Journal of Catalyst & Catalysis · Vol. 13, Issue 2, 2026 · pp. 36–44 Read article