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45 articles for “toxicity prediction”
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Gradient Boosted Regression Tree Approach to Predicting Toxic Interactions on X and YouTube
Abstract: In the digital age, social media platforms play a vital role in facilitating user engagement, encompassing both positive interactions and avenues for negative, often harmful behaviors. Recognizing and addressing toxic exchanges is paramount to nurturing healthy online communities and preserving users’ well-being. This study introduces a novel method for identifying toxic interactions by utilizing Gradient Boosting Regression Trees (GBRT) algorithm, a machine learning approach renowned for its exceptional accuracy and …
Published in Trends in Opto-electro & Optical Communication · Vol. 15, Issue 3, 2025 · pp. 7–14 Read article
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QSAR Modeling Techniques: A Comprehensive Review of Tools and Best Practices
Abstract: Quantitative Structure–Activity Relationship (QSAR) modeling has become an essential tool in drug discovery, toxicity assessment, and environmental chemistry. By correlating chemical structure with biological activity or toxicity, QSAR enables the prediction of compound behavior without extensive experimental testing. This approach not only saves time and resources but also supports ethical practices by reducing reliance on animal studies. The evolution of QSAR from basic linear models to advanced machine learning and …
Published in International Journal of Cheminformatics · Vol. 3, Issue 1, 2025 · pp. 56–63 Read article
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Valeton Phytoconstituents from Curcuma Phaeocaulis as Prion Protein Mutant V210I Inhibitors: A Computational Docking and Virtual Screening Study
Abstract: Objective: Creutzfeldt-Jakob disease (CJD), a neurological disorder that is sporadic, fatal, communicable, and worsens rapidly, is caused by abnormal folding of prion proteins. Identifying and assessing the potential of phytocompounds from the Curcuma phaeocaulis Valeton plant as a new therapeutic candidate aimed at the treatment of CJD is the objective of this research article. Methods: In this experiment, we assessed outcomes following an in-silico assessment to design a new oral …
Published in International Journal of Biochemistry and Biomolecule Research · Vol. 1, Issue 2, 2023 · pp. 1–16 Read article
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A Study on AI-Enhanced Environmental Toxicology: Sensor-Driven Predictive Framework
Abstract: Traditional environmental toxicology relies heavily on labor-intensive, often retrospective, sampling and analysis, limiting our understanding of dynamic pollutant behaviors and their real-time impact on ecosystems and human health. This study presents a novel, integrated framework leveraging advanced sensor networks and artificial intelligence (AI) to revolutionize the monitoring, assessment, and predictive modeling of environmental contaminants. We deployed a sophisticated array of multi-parameter sensors (e.g., electrochemical, optical, biosensors for heavy metals, organic …
Published in Research and Reviews: A Journal of Toxicology · Vol. 15, Issue 3, 2025 Read article
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In Silico Analysis and Docking Study of the Active Phytocompounds of Bacopa monnieri Against Alzheimer Disease
Abstract: Objective: Alzheimer’s is a neurodegenerative disease and is the cause of 60–70% of cases of dementia. It infected a million people worldwide. An effort was undertaken to explore the potential of natural compounds found in Bacopa monnieri, a plant renowned for its extensive medicinal properties in Indian Ayurveda, to combat the disease. This was achieved through molecular docking studies, evaluation of drug-likeness, and comprehensive ADME (absorption, distribution, metabolism, and excretion) …
Published in Research and Reviews : Journal of Computational Biology · Vol. 13, Issue 2, 2024 · pp. 1–13 Read article
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A Structure-based Design Approach to Catharanthus roseus Phytoconstituents as Potential Inhibitors of B-Cell Lymphoma 6 Protein
Abstract: Objectives: Lymphoma is a cancerous disease. It develops into millions of people worldwide. Identify the naturally active compounds from Catharanthus roseus, they have multiple values in the medicinal field of Siddha, and Indian Ayurveda are used as a disease-preventing agent. Drug illness prediction, toxicity prediction, statistical information, molecular docking, as well as absorption, distribution, metabolism, and excretion (ADME) analysis are used to predict the fit ligand as a drug. Methods: …
Published in International Journal of Bioinformatics and Computational Biology · Vol. 1, Issue 2, 2023 · pp. 14–34 Read article
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Targeting Dystrophin Restoration and Neuroprotection in Duchenne Muscular Dystrophy: Insights from Withania somnifera
Abstract: Duchenne muscular dystrophy is a genetic disorder. This disease affects men more common than women. Main objective of the present study was to identify the naturally active phytocompounds from Withania somnifera (Ashwagandha). Ashwagandha has a great value in the field of Ayurveda and Indian medicine and is used for treating muscular and neurological disorders. Toxicity prediction, molecular docking, statistical information, drug illness prediction, and Absorption, distribution, metabolism, excretion, and toxicity …
Published in Research and Reviews: A Journal of Pharmaceutical Science · Vol. 15, Issue 2, 2024 · pp. 64–84 Read article
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In Silico Screening of Terminalia chebula Phytocompounds as Potential Inhibitors of NTCP and DDB1 in Hepatitis B Virus Infection
Abstract: Hepatitis B virus (HBV) infection is a major global health concern, and chronic carriers are at risk of developing cirrhosis, hepatocellular carcinoma (HCC), and even death. Existing treatments can effectively inhibit viral replication, but they achieve little eradication of the virus because of covalently closed circular DNA (cccDNA) retention. In this investigation, bioactive compounds of Terminalia chebula, a plant known for its hepatoprotective and antiviral properties in traditional medicine, were …
Published in Research & Reviews: A Journal of Drug Formulation, Development and Production · Vol. 13, Issue 1, 2026 · pp. 33–44 Read article
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Screening Phytocompounds of Tinospora cordifolia to Find Potential Drug Targets for Cystic Fibrosis
Abstract: This study aimed to evaluate the therapeutic potential of phytocompounds derived from Tinospora cordifolia in treating cystic fibrosis (CF), a genetic disorder caused by mutations in the CFTR gene that lead to severe respiratory and digestive complications. Phytocompounds were retrieved from the IMPPAT database and subjected to molecular docking simulations using PyRx to assess their binding affinity to the CFTR protein. The top-scoring compounds – sterol, magnoflorine, tembetarine, kaempferol, and …
Published in International Journal of Genetic Modifications and Recombinations · Vol. 3, Issue 1, 2025 · pp. 16–24 Read article
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A Supervised Learning Approach for Toxic Comment Detection on Social Media Platforms
Abstract: Nowadays everyone uses social media platforms like X (formerly Twitter), Instagram, Facebook, etc. for various purposes. With the help of this, we share our opinions, ideas, and feelings. Generally, the datasets obtained from the internet are constructive; however, there is a significant proportion of toxic ones. The datasets are filtered to remove noise, and noise is removed in post-processing. The study initiates with the upload and preprocessing of a toxic …
Published in Journal of Mobile Computing, Communications & Mobile Networks · Vol. 11, Issue 2, 2024 · pp. 7–14 Read article
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An in-silico toxicity study of the phytocompounds found in Pongamia pinnata leaves using the ProTox-II web server
Abstract: The aim of the current study was to determine the Insilco toxicity of the phytocompound present in the leaves of Pongamia pinnata. The leaves of Pongamia pinnata have long been used in traditional medicine for their therapeutic properties. Nowadays, herbal toxicity is a common problem caused by incorrect dosage. The FDA reviews the safety and effectiveness of herbal products only in response to patient or health care provider complaints—problems with …
Published in Research and Reviews : Journal of Crop science and Technology · Vol. 13, Issue 1, 2024 Read article
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In Silico Prediction of Multitarget Mechanism of Quinoline and Its Analogs on Phosphoinositide-3-Kinase Pathway Proteins
Abstract: Objective: Phosphoinositide 3-kinases (PI3Ks), the target of rapamycin (PI3K/Akt/mTOR, PAM), are a family of enzymes that play a role in the growth, proliferation, differentiation, motility, survival, and intracellular trafficking of cells, all of which are essential for healthy cellular function and are also connected to cancer. In this study, quinoline and its derivatives were employed to analyze its inhibition activity on the phosphoinositide-3-kinase pathway. Methods: In this work, eight phytocompounds …
Published in International Journal of Molecular Biotechnological Research · Vol. 1, Issue 1, 2023 · pp. 57–76 Read article
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In Silico Exploration of Podophyllum Hexandrum-Derived Phytocompounds as Potential Therapeutics Against Small Cell Lung Cancer (SCLC): A Molecular Docking Approach
Abstract: Small Cell Lung Cancer (SCLC) is a fast-growing and aggressive type of lung cancer that spreads quickly strongly associated with smoking. It is characterized by symptoms, such as persistent cough, breathing difficulties, or hoarseness, though it can sometimes be asymptomatic which makes early detection challenging. The tumor suppressor gene TP53 is critical in regulating the cell cycle and preventing uncontrolled cell division. Mutations in TP53 result in the loss of …
Published in International Journal of Molecular Biotechnological Research · Vol. 3, Issue 1, 2025 · pp. 1–11 Read article
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AI-based Drug Discovery-Revolutionizing Pharmaceutical Research
Abstract: The traditional drug discovery process is often costly, time-consuming, and prone to high failure rates. The advent of Artificial Intelligence (AI) has revolutionized this field by significantly enhancing efficiency, reducing costs, and improving success rates. AI-driven approaches, including machine learning (ML), deep learning (DL), and natural language processing (NLP), have transformed key areas such as drug target identification, molecular screening, lead optimization, and clinical trial design. AI models can analyze …
Published in Research & Reviews: A Journal of Drug Design & Discovery · Vol. 12, Issue 2, 2025 · pp. 30–44 Read article
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Accelerating Drug Discovery with AI: Transforming the Pharmaceutical Pipeline
Abstract: The revolutionary potential of artificial intelligence (AI) is examined in this essay the pharmaceutical industry, highlighting its application across the drug development lifecycle. Artificial Intelligence, specifically via deep learning models and machine learning (ML) such as GANs, RNNs, and transformers, enhances drug discovery, formulation, toxicity prediction, and clinical trials. It streamlines processes like identification of targets, virtual screening, modelling of structure-activity relationships, and medication repurposing. AI is also employed in …
Published in Research & Reviews: A Journal of Drug Design & Discovery · Vol. 12, Issue 2, 2025 · pp. 77–84 Read article
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Molecular Docking, QSAR Modeling, and ADMET Evaluation of Novel Pyrazolo-Pyrimidine Derivatives as Potential CDK-2 Inhibitors for Cancer Therapy
Abstract: Cyclin-dependent kinase-2 (CDK-2) is an essential regulator in cell cycle progression and is an important therapeutic target in cancer drug development. In the present study, an integrated computational approach involving molecular docking studies, QSAR modeling, ADMET prediction, and artificial intelligence-based analysis was used to identify pyrazolo-pyrimidine derivatives as potential CDK-2 inhibitors. Based on the molecular docking results, it was found that selected compounds exhibited high binding affinity towards the ATP …
Published in Research and Reviews: A Journal of Pharmacology · Vol. 16, Issue 2, 2026 Read article
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Explainable Artificial Intelligence in Personalized Medicine: Emerging Clinical Perspectives
Abstract: The convergence of artificial intelligence (AI) and precision medicine has transformed contemporary healthcare by enabling data-driven clinical decision-making, individualized therapeutic interventions, and predictive diagnostics. However, despite remarkable advances in machine learning (ML) and deep learning (DL), the widespread adoption of AI in healthcare remains constrained by the “black-box” nature of many computational systems. Clinicians, regulatory agencies, and patients increasingly demand transparency, interpretability, and trustworthiness in AI-guided medical recommendations. Explainable Artificial …
Published in Emerging Trends in Personalized Medicines · Vol. 3, Issue 2, 2026 · pp. 13–29 Read article
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Use of AI Tools to Create New Drugs
Abstract: The emergence of artificial intelligence in pharmaceutical research [in drug discovery] is a revolution in pharmaceutical research, often combining computational methods with traditional research methods to solve problems. This review article describes various applications of artificial intelligence at various stages of drug development and highlights significant advances and approaches. He explores the critical role of intelligence in drug design, polypharmacology, drug synthesis, drug repurposing, and prediction of drug properties, such …
Published in International Journal of Bioinformatics and Computational Biology · Vol. 2, Issue 2, 2024 · pp. 22–49 Read article
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Pharma Tech: Leveraging Software for Drug Development & Clinical Research
Abstract: The pharmaceutical sector is progressively adopting software solutions to enhance the drug development process and optimize clinical research results. Drug development is a time-consuming, expensive, and intricate process that traditionally requires extensive laboratory research, preclinical testing, and several stages of clinical trials. Software tools are revolutionizing these stages by improving efficiency, minimizing errors, and speeding up timelines. During preclinical testing, predictive software tools are used to model toxicological effects and …
Published in International Journal of Bioinformatics and Computational Biology · Vol. 3, Issue 1, 2025 · pp. 11–19 Read article
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Drug Screening Using Stem Cell Technology
Abstract: Stem cell technology in drug screening has transformed the research and development sector of pharmaceuticals. Although stem cells imitate their human counterparts in terms of both safety and potential, it is a new paradigm for mimicking diseases of humans and scale measuring for toxicity. These tissues will, therefore, be more predictive of what will happen in humans during clinical situations, compared to conventional cell lines or animal models, since they …
Published in Research & Reviews: A Journal of Drug Design & Discovery · Vol. 12, Issue 3, 2025 · pp. 42–51 Read article