Computer-Aided Drug Design and Drug Discovery: A Prospective Analysis

Pharmaceuticals, Journal Year: 2023, Volume and Issue: 17(1), P. 22 - 22

Published: Dec. 22, 2023

In the dynamic landscape of drug discovery, Computer-Aided Drug Design (CADD) emerges as a transformative force, bridging realms biology and technology. This paper overviews CADDs historical evolution, categorization into structure-based ligand-based approaches, its crucial role in rationalizing expediting discovery. As CADD advances, incorporating diverse biological data ensuring privacy become paramount. Challenges persist, demanding optimization algorithms robust ethical frameworks. Integrating Machine Learning Artificial Intelligence amplifies predictive capabilities, yet considerations scalability challenges linger. Collaborative efforts global initiatives, exemplified by platforms like Open-Source Malaria, underscore democratization The convergence with personalized medicine offers tailored therapeutic solutions, though dilemmas accessibility concerns must be navigated. Emerging technologies quantum computing, immersive technologies, green chemistry promise to redefine future CADD. trajectory CADD, marked rapid advancements, anticipates accuracy, addressing biases AI, sustainability metrics. concludes highlighting need for proactive measures navigating ethical, technological, educational frontiers shape healthier, brighter

Language: Английский

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Mpox (formerly monkeypox): pathogenesis, prevention and treatment

Signal Transduction and Targeted Therapy, Journal Year: 2023, Volume and Issue: 8(1)

Published: Dec. 27, 2023

In 2022, a global outbreak of Mpox (formerly monkeypox) occurred in various countries across Europe and America rapidly spread to more than 100 regions. The World Health Organization declared the be public health emergency international concern due rapid virus. Consequently, nations intensified their efforts explore treatment strategies aimed at combating infection its dissemination. Nevertheless, available therapeutic options for virus remain limited. So far, only few numbers antiviral compounds have been approved by regulatory authorities. Given high mutability virus, certain mutant strains shown resistance existing pharmaceutical interventions. This highlights urgent need develop novel drugs that can combat both drug potential threat bioterrorism. Currently, there is lack comprehensive literature on pathophysiology Mpox. To address this issue, we conducted review covering physiological pathological processes infection, summarizing latest progress anti-Mpox drugs. Our analysis encompasses currently employed clinical settings, as well newly identified small-molecule antibody displaying efficacy against Furthermore, gained valuable insights from process development, including repurposing drugs, discovery targets driven artificial intelligence, preclinical development. purpose provide readers with overview current knowledge

Language: Английский

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SSF-DDI: a deep learning method utilizing drug sequence and substructure features for drug–drug interaction prediction

BMC Bioinformatics, Journal Year: 2024, Volume and Issue: 25(1)

Published: Jan. 23, 2024

Abstract Background Drug–drug interactions (DDI) are prevalent in combination therapy, necessitating the importance of identifying and predicting potential DDI. While various artificial intelligence methods can predict identify DDI, they often overlook sequence information drug molecules fail to comprehensively consider contribution molecular substructures Results In this paper, we proposed a novel model for DDI prediction based on substructure features (SSF-DDI) address these issues. Our integrates structural from molecule graph, providing enhanced enabling more comprehensive accurate representation molecules. Conclusion The results experiments case studies have demonstrated that SSF-DDI significantly outperforms state-of-the-art models across multiple real datasets settings. performs better involving unknown drugs, resulting 5.67% improvement accuracy compared methods.

Language: Английский

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Exploring Citrus sinensis Phytochemicals as Potential Inhibitors for Breast Cancer Genes BRCA1 and BRCA2 Using Pharmacophore Modeling, Molecular Docking, MD Simulations, and DFT Analysis

ACS Omega, Journal Year: 2024, Volume and Issue: 9(2), P. 2161 - 2182

Published: Jan. 3, 2024

Background: Structure–activity relationship (SAR) is considered to be an effective in silico approach when discovering potential antagonists for breast cancer due gene mutation. Major challenges are faced by conventional SAR predicting novel the discovery of diverse antagonistic compounds. Methodologyand Results: In antagonists, a multistep screening phytochemicals isolated from seeds Citrus sinensis plant was applied using feasible complementary methodologies. A three-dimensional quantitative structure–activity (3D-QSAR) model developed through Flare project, which conformational analysis, pharmacophore generation, and compound alignment were done. Ten hit compounds obtained development 3D-QSAR model. For exploring mechanism action active against cocrystal inhibitors, molecular docking analysis done Molegro software (MVD) identify lead Three new proteins, namely, 1T15, 3EU7, 1T29, displayed best Moldock scores. The quality study assessed dynamics simulation. Based on binding affinities receptor studies, three (stigmasterol P8, epoxybergamottin P28, nobiletin P29) obtained, they passed absorption, distribution, metabolism, excretion (ADME) studies via SwissADME online service, proved that P28 P29 most allosteric inhibitors with lowest toxicity level cancer. Then, density functional theory (DFT) performed measure compound's reactivity, hardness, softness help Gaussian 09 software. Conclusions: This revealed high-reliability flare, DFT studies. present helps providing proper guideline BRCA1 BRCA2.

Language: Английский

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Practical Three-Component Regioselective Synthesis of Drug-Like 3-Aryl(or heteroaryl)-5,6-dihydrobenzo[h]cinnolines as Potential Non-Covalent Multi-Targeting Inhibitors To Combat Neurodegenerative Diseases

ACS Chemical Neuroscience, Journal Year: 2024, Volume and Issue: 15(9), P. 1828 - 1881

Published: April 22, 2024

Neurodegenerative diseases (NDs) are one of the prominent health challenges facing contemporary society, and many efforts have been made to overcome (or) control it. In this research paper, we described a practical one-pot two-step three-component reaction between 3,4-dihydronaphthalen-1(2H)-one (1), aryl(or heteroaryl)glyoxal monohydrates (2a–h), hydrazine monohydrate (NH2NH2•H2O) for regioselective preparation some 3-aryl(or heteroaryl)-5,6-dihydrobenzo[h]cinnoline derivatives (3a–h). After synthesis characterization mentioned cinnolines (3a–h), in silico multi-targeting inhibitory properties these heterocyclic scaffolds investigated upon various Homo sapiens-type enzymes, including hMAO-A, hMAO-B, hAChE, hBChE, hBACE-1, hBACE-2, hNQO-1, hNQO-2, hnNOS, hiNOS, hPARP-1, hPARP-2, hLRRK-2(G2019S), hGSK-3β, hp38α MAPK, hJNK-3, hOGA, hNMDA receptor, hnSMase-2, hIDO-1, hCOMT, hLIMK-1, hLIMK-2, hRIPK-1, hUCH-L1, hPARK-7, hDHODH, which confirmed their functions roles neurodegenerative (NDs), based on molecular docking studies, obtained results were compared with wide range approved drugs well-known (with IC50, EC50, etc.) compounds. addition, ADMET prediction analysis was performed examine prospective drug synthesized compounds The from studies ADMET-related data demonstrated that series heteroaryl)-5,6-dihydrobenzo[h]cinnolines especially hit ones, can really be turned into potent core new treatment and/or due having reactionable locations, they able further organic reactions (such as cross-coupling reactions), expansion (for example, using other types monohydrates) makes avenue designing novel efficient purpose.

Language: Английский

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Artificial intelligence in small molecule drug discovery from 2018 to 2023: Does it really work?

Bioorganic Chemistry, Journal Year: 2023, Volume and Issue: 141, P. 106894 - 106894

Published: Sept. 27, 2023

Language: Английский

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In silico drug design strategies for discovering novel tuberculosis therapeutics

Expert Opinion on Drug Discovery, Journal Year: 2024, Volume and Issue: 19(4), P. 471 - 491

Published: Feb. 19, 2024

Introduction Tuberculosis remains a significant concern in global public health due to its intricate biology and propensity for developing antibiotic resistance. Discovering new drugs is protracted expensive endeavor, often spanning over decade incurring costs the billions. However, computer-aided drug design (CADD) has surfaced as nimbler more cost-effective alternative. CADD tools enable us decipher interactions between therapeutic targets novel drugs, making them invaluable quest tuberculosis treatments.

Language: Английский

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Another string to your bow: machine learning prediction of the pharmacokinetic properties of small molecules

Expert Opinion on Drug Discovery, Journal Year: 2024, Volume and Issue: 19(6), P. 683 - 698

Published: May 10, 2024

Prediction of pharmacokinetic (PK) properties is crucial for drug discovery and development. Machine-learning (ML) models, which use statistical pattern recognition to learn correlations between input features (such as chemical structures) target variables PK parameters), are being increasingly used this purpose. To embed ML models prediction into workflows guide future development, a solid understanding their applicability, advantages, limitations, synergies with other approaches necessary.

Language: Английский

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Ligand based-design of potential schistosomiasis inhibitors through QSAR, homology modeling, molecular dynamics, pharmacokinetics, and DFT studies

Journal of Taibah University Medical Sciences, Journal Year: 2024, Volume and Issue: 19(2), P. 429 - 446

Published: Feb. 26, 2024

Schistosomiasis, a neglected tropical disease, is leading cause of mortality in affected geographic areas. Currently, because no vaccine for schistosomiasis available, control measures rely on widespread administration the drug praziquantel (PZQ). The mass PZQ has prompted concerns regarding emergence resistance. Therefore, new therapeutic targets and potential compounds are necessary to combat schistosomiasis.

Language: Английский

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Molecular Modeling and Drug Design

Published: Nov. 29, 2024

Molecular modeling offers a paradigm-shifting approach in understanding the small dimensions of atoms and molecules, bringing it at forefront scientific innovation discovery. This computational method researchers newfound strength by allowing them to accurately model, analyze, predict behavior molecules molecular systems range fields including chemistry, biology, pharmacology, physics, etc. By using studies streamline drug development process, contributes our science promotes technical advancements pharmaceutical sciences. In this age inquiry, shines light on discoveries helping unravel mysteries systems. book chapter talks about applications importance different aspects discovery design.

Language: Английский

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