Integration Of Computational Techniques Research Articles

A robust computational quest: Discovering potential hits to improve the treatment of pyrazinamide-resistant Mycobacterium tuberculosis.

The rise of pyrazinamide (PZA)-resistant strains of Mycobacterium tuberculosis (MTB) poses a major challenge to conventional tuberculosis (TB) treatments. PZA, a cornerstone of TB therapy, must be activated by the mycobacterial enzyme pyrazinamidase (PZase) to convert its active form, pyrazinoic acid, which targets the ribosomal protein S1. Resistance, often associated with mutations in the RpsA protein, complicates treatment and highlights a critical gap in the understanding of structural dynamics and mechanisms of resistance, particularly in the context of the G97D mutation. This study utilizes a novel integration of computational techniques, including multiscale biomolecular and molecular dynamics simulations, physicochemical and medicinal chemistry predictions, quantum computations and virtual screening from the ZINC and Chembridge databases, to elucidate the resistance mechanism and identify lead compounds that have the potential to improve treatment outcomes for PZA-resistant MTB, namely ZINC15913786, ZINC20735155, Chem10269711, Chem10279789 and Chem10295790. These computational methods offer a cost-effective, rapid alternative to traditional drug trials by bypassing the need for organic subjects while providing highly accurate insight into the binding sites and efficacy of new drug candidates. The need for rapid and appropriate drug development emphasizes the need for robust computational analysis to justify further validation through invitro and invivo experiments.

Unlocking the potential of aromatase inhibitors: recent advances in drug design, synthesis, docking activity, and in vitro bioactivity evaluations

Breast cancer, a global health concern claiming approximately 685,000 lives in 2020, necessitates continual advancements in therapeutic strategies. Estrogen and aromatase play pivotal roles in hormone-responsive breast cancer, with 80% of patients exhibiting estrogen receptor-positive tumors. Aromatase inhibitors (AIs), notably non-steroidal inhibitors like anastrozole and letrozole, have significantly improved outcomes, yet challenges persist, including side effects. This review focuses on recent developments in AIs, exploring xanthone derivatives, imidazole derivatives, and curcumin derivatives as potential inhibitors of aromatase. Molecular docking studies, employing Auto Dock and other tools, reveal the binding affinities and interactions of these compounds with the aromatase enzyme. Among xanthones, Erythrommone emerges as a potent inhibitor, holding promise for clinical trials. Imidazole derivatives, synthesized through the Debus-Radziszewski reaction, demonstrate anticancer potential, with compounds like 1a exhibiting superior efficacy against MCF7 cells. ADME-Tox analyses indicate promising drug-likeness but reveal potential mutagenic effects and environmental impacts. Curcumin derivatives, particularly 1,5-diaryl-1,4-pentadien-3-ones, present alternatives to address curcumin's bioavailability challenges. A study of 25 compounds (DKC) identifies DKC-10 as a potent inhibitor, outperforming established breast cancer drugs in terms of binding affinity and interactions with aromatase and ERα+ receptors. These findings underscore the importance of exploring diverse chemical structures in developing AIs, paving the way for more effective and well-tolerated therapeutics. The integration of computational techniques, such as molecular docking studies, accelerates drug discovery by predicting interactions at the molecular level. Overall, this comprehensive review provides valuable insights into the evolving landscape of aromatase inhibitors, offering a roadmap for future research and the development of advanced breast cancer therapeutics.

Application of Computational Techniques in Antibody Fc-Fused Molecule Design for Therapeutics.

Since the advent of hybridoma technology in the year 1975, it took a decade to witness the first approved monoclonal antibody Orthoclone OKT39 (muromonab-CD3) in the year 1986. Since then, continuous strides have been made to engineer antibodies for specific desired effects. The engineering efforts were not confined to only the variable domains of the antibody but also included the fragment crystallizable (Fc) region that influences the immune response and serum half-life. Engineering of the Fc fragment would have a profound effect on the therapeutic dose, antibody-dependent cell-mediated cytotoxicity as well as antibody-dependent cellular phagocytosis. The integration of computational techniques into antibody engineering designs has allowed for the generation of testable hypotheses and guided the rational antibody design framework prior to further experimental evaluations. In this article, we discuss the recent works in the Fc-fused molecule design that involves computational techniques. We also summarize the usefulness of in silico techniques to aid Fc-fused molecule design and analysis for the therapeutics application.

Sculpting Molecules in Silico: A Comprehensive Review of the Latest Innovations in Computer-Aided Drug Design to Revolutionize the Way Drugs are Engineered and Optimized

The realm of drug discovery has been significantly transformed by the integration of computational techniques into the design process. This review delves into the evolving landscape of computer-aided drug design, focusing on the latest innovations that are reshaping the traditional paradigms of drug engineering and optimization. We explore how in silico approaches, powered by advances in molecular modeling, machine learning, and structural bioinformatics, are revolutionizing the way new pharmaceutical agents are conceptualized, refined, and brought closer to clinical realization. By analyzing a range of cutting-edge methodologies, from molecular simulations to data-driven strategies, this comprehensive review provides a deep insight into the intricate synergy between computer-aided drug design and modern pharmaceutical research. The transformative potential of these innovations is underscored, offering a glimpse into the future of drug discovery where computational precision and predictive capabilities are poised to shape the development of novel therapeutic interventions. Keywords: Drug discovery, Computational techniques, In silico methods, Molecular modeling, Machine learning, Structural bioinformatics

TLR4-Targeting Therapeutics: Structural Basis and Computer-Aided Drug Discovery Approaches.

The integration of computational techniques into drug development has led to a substantial increase in the knowledge of structural, chemical, and biological data. These techniques are useful for handling the big data generated by empirical and clinical studies. Over the last few years, computer-aided drug discovery methods such as virtual screening, pharmacophore modeling, quantitative structure-activity relationship analysis, and molecular docking have been employed by pharmaceutical companies and academic researchers for the development of pharmacologically active drugs. Toll-like receptors (TLRs) play a vital role in various inflammatory, autoimmune, and neurodegenerative disorders such as sepsis, rheumatoid arthritis, inflammatory bowel disease, Alzheimer’s disease, multiple sclerosis, cancer, and systemic lupus erythematosus. TLRs, particularly TLR4, have been identified as potential drug targets for the treatment of these diseases, and several relevant compounds are under preclinical and clinical evaluation. This review covers the reported computational studies and techniques that have provided insights into TLR4-targeting therapeutics. Furthermore, this article provides an overview of the computational methods that can benefit a broad audience in this field and help with the development of novel drugs for TLR-related disorders.