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283 articles for “Inhibition”
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Novel 1,3,4-Thiadiazole Derivatives: Design, Synthesis, Characterization and Anticancer Evaluation – A Review
Abstract: Cancer remains one of the leading causes of mortality worldwide despite significant advances in diagnosis and treatment. The clinical usefulness of many anticancer drugs is often restricted by toxicity, poor selectivity, and the emergence of drug resistance. These limitations have encouraged the search for novel heterocyclic compounds with improved therapeutic efficacy and safety. Among these, the 1,3,4-thiadiazole scaffold has gained considerable attention because of its favorable physicochemical characteristics, metabolic stability, …
Published in Research & Reviews: A Journal of Drug Design & Discovery · Vol. 13, Issue 2, 2026 Read article
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Host Invasion to Immune Evasion: Emerging Concepts in Pathogen Virulence Strategies
Abstract: The interaction between hosts and pathogens represents a continuous evolutionary struggle, shaping both microbial virulence and host immunity. Pathogens employ diverse strategies to evade immune surveillance, ranging from antigenic variation and molecular mimicry to hijacking of host immune checkpoints. Simultaneously, hosts refine innate and adaptive immune mechanisms to counteract infection and limit damage. This reciprocal adaptation, known as host–pathogen coevolution, influences pathogen persistence, transmission, and fitness, while also sculpting immune …
Published in International Journal of Pathogens · Vol. 3, Issue 1, 2026 Read article
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Neuro-Symbolic Agentic AI for Autonomous Scientific Discovery: Integrating Deep Reinforcement Learning, Quantum Simulation, and XAI-Audited LLM Hypothesis Generation in Drug Target Identification
Abstract: The exponential growth of multi-omics data and the increasing complexity of disease-associated protein interactomes have rendered conventional drug target identification pipelines computationally and epistemologically inadequate. This paper presents the Neuro-Symbolic Agentic AI for Scientific Discovery (NS-AASD) framework, a unified architecture that cohesively integrates deep reinforcement learning (DRL) exploration strategies, variational quantum simulation (VQS) of protein conformational dynamics, and XAI-audited large language model (LLM) hypothesis generation within an autonomous scientific discovery …
Published in Research and Reviews : Journal of Computational Biology · Vol. 15, Issue 2, 2026 Read article