Examining Prenylated Xanthones as Potential Inhibitors Against Ketohexokinase C Isoform for the Treatment of Fructose-Driven Metabolic Disorders: An Integrated Computational Approach

Pharmaceuticals, Journal Year: 2025, Volume and Issue: 18(1), P. 126 - 126

Published: Jan. 18, 2025

Background/Objectives: Fructose-driven metabolic disorders, such as obesity, non-alcoholic fatty liver disease (NAFLD), dyslipidemia, and type 2 diabetes, are significant global health challenges. Ketohexokinase C (KHK-C), a key enzyme in fructose metabolism, is promising therapeutic target. α-Mangostin, naturally occurring prenylated xanthone, has been identified an effective KHK-C inhibitor, prompting exploration of its analogs for enhanced efficacy. This study aimed to identify α-Mangostin with improved inhibitory properties against address these disorders. Methods: A library 1383 was compiled from chemical databases the literature. Molecular docking, binding free energy calculations, pharmacokinetic assessments, molecular dynamics simulations, quantum mechani–cal analyses were used screen evaluate compounds. α-Mangostin’s affinity (37.34 kcal/mol) served benchmark. Results: Sixteen demonstrated affinities superior (from −45.51 −61.3 kcal/mol), LY-3522348 (−45.36 reported marine-derived inhibitors −22.74 −51.83 kcal/mol). Hits 7, 8, 9, 13, 15 not only surpassed benchmarks affinity, but also exhibited compared LY-3522348, inhibitors, indicating strong vivo potential. Among these, hit 8 emerged best performer, achieving −61.30 kcal/mol, 100% predicted oral absorption, stability, stable dynamics. Conclusions: Hit most candidate due favorable pharmacokinetics, interactions KHK-C. These findings highlight potential treating fructose-driven warranting further experimental validation.

Language: Английский

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The Physics-AI Dialogue in Drug Design

RSC Medicinal Chemistry, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 1, 2025

A long path has led from the determination of first protein structure in 1960 to recent breakthroughs science. Protein prediction and design methodologies based on machine learning (ML) have been recognized with 2024 Nobel prize Chemistry, but they would not possible without previous work input many domain scientists. Challenges remain application ML tools for structural ensembles their usage within software pipelines by crystallography or cryogenic electron microscopy. In drug discovery workflow, techniques are being used diverse areas such as scoring docked poses, generation molecular descriptors. As become more widespread, novel applications emerge which can profit large amounts data available. Nevertheless, it is essential balance potential advantages against environmental costs deployment decide if when best apply it. For hit lead optimization efficiently interpolate between compounds chemical series free energy calculations dynamics simulations seem be superior designing derivatives. Importantly, complementarity and/or synergism physics-based methods (e.g., force field-based simulation models) data-hungry growing strongly. Current evolved decades research. It now necessary biologists, physicists, computer scientists fully understand limitations ensure that exploited design.

Language: Английский

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Revolutionizing Molecular Design for Innovative Therapeutic Applications through Artificial Intelligence

Molecules, Journal Year: 2024, Volume and Issue: 29(19), P. 4626 - 4626

Published: Sept. 29, 2024

The field of computational protein engineering has been transformed by recent advancements in machine learning, artificial intelligence, and molecular modeling, enabling the design proteins with unprecedented precision functionality. Computational methods now play a crucial role enhancing stability, activity, specificity for diverse applications biotechnology medicine. Techniques such as deep reinforcement transfer learning have dramatically improved structure prediction, optimization binding affinities, enzyme design. These innovations streamlined process allowing rapid generation targeted libraries, reducing experimental sampling, rational tailored properties. Furthermore, integration approaches high-throughput techniques facilitated development multifunctional novel therapeutics. However, challenges remain bridging gap between predictions validation addressing ethical concerns related to AI-driven This review provides comprehensive overview current state future directions engineering, emphasizing their transformative potential creating next-generation biologics advancing synthetic biology.

Language: Английский

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The Need for Continuing Blinded Pose- and Activity Prediction Benchmarks

Journal of Chemical Information and Modeling, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 14, 2025

Computational tools for structure-based drug design (SBDD) are widely used in discovery and can provide valuable insights to advance projects an efficient cost-effective manner. However, despite the importance of SBDD field, underlying methodologies techniques have many limitations. In particular, binding pose activity predictions (P-AP) still not consistently reliable. We strongly believe that a limiting factor is lack accepted established community benchmarking process independently assesses performance drives development methods, similar CASP challenge protein structure prediction. Here, we overview P-AP, unblinded data sets, blinded initiatives (concluded ongoing) offer perspective on learnings future field. To accelerate breakthrough novel P-AP it necessary establish support long-term benchmark provides nonbiased training/test/validation systematic independent validation, forum scientific discussions.

Language: Английский

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High performance-oriented computer aided drug design approaches in the exascale era

Expert Opinion on Drug Discovery, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 15, 2025

Introduction In 2023, the first exascale supercomputer was opened to public in US. With a demonstrated 1.1 exaflops of performance, Frontier represents an unprecedented breakthrough high performance computing (HPC). Currently, more (and powerful) machines are being installed worldwide. Computer-aided drug design (CADD) is one fields computational science that can greatly benefit from for whole society. However, scaling CADD approaches exploit requires new algorithmic and software solutions.

Language: Английский

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Quantum natural language processing and its applications in bioinformatics: a comprehensive review of methodologies, concepts, and future directions

Frontiers in Computer Science, Journal Year: 2025, Volume and Issue: 7

Published: Feb. 18, 2025

Quantum Natural Language Processing (QNLP) is a relatively new subfield of research that extends the application principles natural language processing and quantum computing has enabled complex biological information to unprecedented levels. The present comprehensive review analyses potential QNLP in influencing many branches bioinformatics such as genomic sequence analysis, protein structure prediction, drug discovery design. To establish correct background techniques, this article going explore basics including qubits, entanglement, algorithms. next section devoted extraction material valuable knowledge related development, prediction assessment drug-target interactions. In addition, paper also explains structural by embedding, simulation, optimization for exploring sequence-structure relationship. However, study acknowledges future discussion challenges weaknesses hardware, data representation, encoding, construction enhancement This looks into real-life problems solved from industry applications, benchmarking criteria, comparison with other traditional NLP methods. Therefore, enunciates perspectives, well developmental implementation blueprint bioinformatics. plan follows: its function achieve objectives precision medicine, design, multi-omics, green chemistry.

Language: Английский

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Water in drug design: pitfalls and good practices

Expert Opinion on Drug Discovery, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 20

Published: April 27, 2025

Structure-based drug design relies on optimizing drug-target interactions and blocking harmful pathophysiological events at the atomic level. Such of human body are modulated by water acting either as a medium or an individual partner in molecular interactions. A precise understanding modulatory mechanisms is essential for successful design. The present review discusses different topographical networking situations that result radically roles water, root various pitfalls surveys good practices tackling problems determining structure resolution. Techniques quantifying effects bulk, networking, molecules stability complexes also discussed. article based literature search using PubMed, Web Science, Google Scholar databases. With advances rapid computational algorithms better physicochemical machinery complex formation, theoretical approaches have resulted elegant cost-effective tools fill knowledge gaps left limited experimental methods. Overcoming technical design, transforms from frustrating challenge into handy tool fine-tuning

Language: Английский

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Enthalpic Classification of Water Molecules in Target–Ligand Binding

Journal of Chemical Information and Modeling, Journal Year: 2024, Volume and Issue: 64(16), P. 6583 - 6595

Published: Aug. 13, 2024

Water molecules play various roles in target–ligand binding. For example, they can be replaced by the ligand and leave surface of binding pocket or stay conserved interface form bridges with target. While experimental techniques supply complex structures at an increasing rate, often have limitations measurement a detailed water structure. Moreover, measurements thermodynamics cannot distinguish between different individual molecules. However, such distinction classification role would key to their application drug design atomic resolution. In this study, we investigate quantitative approach for description during Starting from complete hydration free ligand-bound target molecules, enthalpy scores are calculated each molecule using quantum mechanical calculations. A statistical evaluation showed that displaced classes The system was calibrated tested on more than 1000 positions. practical tests enthalpic included important cases antiviral research HIV-1 protease inhibitors Influenza ion channel. methodology is based open source program packages, Gromacs, Mopac, MobyWat, freely available scientific community.

Language: Английский

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Machine Learning to Predict Potential Energy Surface of Resveratrol Drug: A Quantum-Level Calculation

ACS Medicinal Chemistry Letters, Journal Year: 2024, Volume and Issue: 15(11), P. 1979 - 1986

Published: Nov. 1, 2024

The ANI-1x neural network potential, trained on the density functional theory data set, as a quantum-level machine learning calculation has been investigated to forecast potential energy surfaces of Resveratrol (3,5,4′-trihydroxy-trans-stilbene) antiparkinsonian drug in very short computing time. A comprehensive validation deep technique was provided molecule using at wB97X/6-31G(d) level theory. results showcased this study will offer significant insights into pharmaceutical computational research, medicinal chemistry, discovery and design, thereby making valuable contribution.

Language: Английский

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In Silico Design of Peptide Inhibitors Targeting HER2 for Lung Cancer Therapy

Cancers, Journal Year: 2024, Volume and Issue: 16(23), P. 3979 - 3979

Published: Nov. 27, 2024

Background/Objectives: Human epidermal growth factor receptor 2 (HER2) is overexpressed in several malignancies, such as breast, gastric, ovarian, and lung cancers, where it promotes aggressive tumor proliferation unfavorable prognosis. Targeting HER2 has thus emerged a crucial therapeutic strategy, particularly for HER2-positive malignancies. The present study focusses on the design optimization of peptide inhibitors targeting HER2, utilizing machine learning to identify enhance candidates with elevated binding affinities. aim provide novel options malignancies linked overexpression. Methods: This started extraction structural examination protein, succeeded by designing sequences derived from essential interaction residues. A technique (XGBRegressor model) was employed predict affinities, identifying top 20 possibilities. underwent further screening via FreeSASA methodology free energy calculations, resulting selection four primary (pep-17, pep-7, pep-2, pep-15). Density functional theory (DFT) calculations were utilized evaluate molecular reactivity characteristics, while dynamics simulations performed investigate inhibitory mechanisms selectivity effects. Advanced computational methods, QM/MM simulations, offered more understanding peptide–protein interactions. Results: Among principal peptides, pep-7 exhibited most DFT values (−3386.93 kcal/mol) maximum dipole moment (10,761.58 Debye), whereas pep-17 had lowest value (−5788.49 minimal (2654.25 Debye). Molecular indicated that steady −12.88 kcal/mol consistently bound inside pocket during 300 ns simulation. showed overall total system, which combines both QM MM contributions, remained around −79,000 ± 400 kcal/mol, suggesting entire protein–peptide complex stable state, maintaining strong, well-integrated binding. Conclusions: Pep-7 promising peptide, displaying strong stability, favorable energy, stability HER2-overexpressing cancer models. These findings suggest viable candidate offering potential treatment strategy against HER2-driven

Language: Английский

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