Real-World Applications and Experiences of AI/ML Deployment for Drug Discovery

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

Published: Jan. 8, 2025

InfoMetricsFiguresRef. Journal of Medicinal ChemistryASAPArticle This publication is free to access through this site. Learn More CiteCitationCitation and abstractCitation referencesMore citation options ShareShare onFacebookX (Twitter)WeChatLinkedInRedditEmailJump toExpandCollapse EditorialJanuary 8, 2025Real-World Applications Experiences AI/ML Deployment for Drug DiscoveryClick copy article linkArticle link copied!Will R. Pitt*Will PittMolecular Architects, Evotec Ltd, Dorothy Crowfoot Hodgkin Campus, 114 Innovation Drive, Milton Park, Abingdon, Oxfordshire OX14 4RZ, U.K.*Email: [email protected]More by Will Pitthttps://orcid.org/0000-0001-8164-4550Jonathan BentleyJonathan BentleyDiscovery Chemistry, U.K.More Jonathan BentleyChristophe BoldronChristophe BoldronMolecular SAS, Campus Curie, 195, route d'Espagne, Toulouse 31095, FranceMore Christophe BoldronLionel ColliandreLionel Colliandrein silico R&D, 195 31100 Toulouse, Lionel ColliandreCarmen EspositoCarmen Espositoin Aptuit Srl, Via Alessandro Fleming, 4, 37135 Verona, ItalyMore Carmen EspositoElizabeth H. FrushElizabeth FrushMolecular Inc., 303B College Road East, Princeton, New Jersey 08540, United StatesMore Elizabeth Frushhttps://orcid.org/0000-0003-3611-132XJola KopecJola Kopecin Jola KopecStéphanie LabouilleStéphanie LabouilleMolecular Stéphanie LabouilleJerome MeneyrolJerome MeneyrolMolecular Jerome MeneyrolDavid A. PardoeDavid PardoeMolecular David Pardoehttps://orcid.org/0009-0005-0807-2994Ferruccio PalazzesiFerruccio Palazzesiin Ferruccio PalazzesiAlfonso PozzanAlfonso PozzanMolecular Alfonso PozzanJacob M. RemingtonJacob RemingtonMolecular Jacob RemingtonRené RexRené RexEvotec International GmbH, Marie-Curie-Str. 7, Göttingen D-37079, GermanyMore René RexMichelle SoutheyMichelle SoutheyMolecular Michelle SoutheySachin VishwakarmaSachin Vishwakarmain Sachin VishwakarmaPaul WalkerPaul WalkerCyprotex Discovery No. 24 Mereside, Alderley Macclesfield, Cheshire SK10 4TG, Paul WalkerOpen PDFJournal ChemistryCite this: J. Med. Chem. 2025, XXXX, XXX, XXX-XXXClick citationCitation copied!https://pubs.acs.org/doi/10.1021/acs.jmedchem.4c03044https://doi.org/10.1021/acs.jmedchem.4c03044Published January 2025 Publication History Received 11 December 2024Published online 8 2025editorialPublished American Chemical Society. available under these Terms Use. Request reuse permissionsThis licensed personal use The ACS PublicationsPublished SocietySubjectswhat are subjectsArticle subjects automatically applied from the Subject Taxonomy describe scientific concepts themes article.Bioinformatics computational biologyDrug discoveryMedicinal chemistryOptimizationStructure activity relationshipThe emergence artificial intelligence (AI) machine learning (ML) in field drug discovery has been propelled significant advances computer science, infrastructure, surge "big data". There also an expectation that AI-related progress other fields, such as virtual assistants, image generation, autonomous vehicles, protein structure prediction, can be replicated elsewhere. continuous desire bring novel treatments market driven companies, including large pharmaceutical firms, biotechs, contract research organizations (CROs), deploy technology both strengthen accelerate pipelines. These companies face decision whether build or buy, either invest internal staff infrastructure establish in-house capabilities collaborate with AI-enabled companies. (1) It noteworthy ML medicinal chemistry began more than 40 years ago. (2) However, recent field, particularly rise deep learning, methods now impacting every stage process, early target identification, hit finding lead optimization. Examples include screening (VS) ultralarge chemical databases models predict potency relevant end points, well generative design algorithms molecular structures scratch. In paper we will present our perspective a CRO involved (and development) partnerships. Given competitive landscape, ours need stay abreast technological advancements because potential partners seek advantage integrating tools into their projects guide generation exploitation high-quality experimental data. For us, commitment do crucial ensure comprehensive robust process.However, accurate prediction data remains challenging due intrinsic complexity biological systems, availability quality training data, limited ability descriptors fully capture nature interactions. cultural challenges adoption AI. (3) inherent biases decision-making within documented. (4,5) Such hinder prevent integration technologies they implicitly challenge well-established working practices. situation further complicated often-exaggerated claims regarding effectiveness impact accelerating process. premature draw definitive conclusions not yet witnessed introduction treatment developed solely methods. (6)In experience, blending approaches, technologies, human experience produces best outcomes. enhance was consistent company's ethos innovation. Building own provides cost-efficient opportunity evaluate and/or develop most appropriate foster talent development. Our organization covers whole process clinical trial support, broad range therapeutic modalities. work many ways, aiding antibody (7) small molecule targeted degrader design. focus on identification late optimization.AI/ML Methodologies ApplicationsClick section linkSection copied!Briefly summarized below others' experiences applications currently have greatest work. Machine Representations SpaceUsing represent space major development informatics. Compounds represented vectors, generated neural networks compound databases. representations termed latent derived mathematically set encapsulate its essential features. A given vector (position space) decoded structure, which great benefit over older like fingerprints. enables rapid compounds interest new regions. instance, interpolation between vectors allows exploration intermediate structures, way move patentable space.One pioneering examples Continuous Data-Driven Descriptors (CDDD), (8) used extensively generating designs (see ways Generative Design below). CDDD, autoencoder (AE), simultaneously trained SMILES (9) constrained properties (e.g., polar surface area lipophilicity) push chemically physically similar molecules subspaces. predisposes transfer (TL), i.e. changing task pretrained model adding new, project-specific thereby focusing objectives properties. (10,11) linkage similarity calculated provided AE architecture another fingerprints.We AE-based Seq2Seq (12) models, utilizing recurrent (RNNs) (13) transformer architectures. (14−17) By sets curated in-house, achieved improved performance flexibility downstream tasks. improvements coverage weight greater 600 Da, necessary some projects. They extraction features quantitative structure–activity relationship (QSAR) building. Combining QSAR (DGC) same space, employ optimization Bayesian (BO) (18,19) particle swarm (PSO) (20) perform inverse (21)/inverse means generate optimized against predictions.The critical, it directly impacts reliability accuracy subsequent applications. We validate representation based DGC validity, novelty, drug-likeness, along metrics quantifying smoothness objective functions. (22) Together, validations allow scientists make informed decisions confidence. Learning (ML)In section, briefly how absorption, distribution, metabolism, excretion, toxicity (ADMET) points (23) physicochemical ─ approaches commonly referred structure–property (QSPR) modeling, respectively.The predictive depends standardized assays, carefully remove unreliable inconsistent measurements. assays logD, aqueous solubility, Caco2 permeability, microsomal clearance, hERG channel inhibition. Specific curation processes implemented regression (continuous predictions) classification tasks (discrete predictions), ensuring only used. To streamline activities facilitate regular updating automated workflow encompasses preparation, calculation, selection, hyperparameter optimization, delivery. ML-generated predictions finally interpreted using explainability techniques, estimate contribution input decision. (24)In years, application techniques QSAR/QSPR modeling shown promise. Graph Neural Networks (GNN) particular, outperform traditional Random Forest (RF) certain points. (25,26) typically spanning few hundred ten thousand usually models. Nonetheless, GNNs proven useful robustness when larger sets.Predictive QSPR play pivotal role projects, idea selection prioritization. One context scoring functions tools. DesignThe recently emerged powerful approach chemistry. previous review (27) identified 100 de novo published 2017 2020. Since then, explosion topic made hard keep track all articles. find papers often lack real-world perspective, since researchers fortunate enough able synthesize test designs. routinely successfully state-of-the-art 2D 3D then tested. Due time constraints vetting tools, reputable sources.One tool adopted modified upon feedback REINVENT. (28,29) reinforcement method generates scores positive loop. findings suggest highly connected components drive toward project specific goals. pharmacophore-based matches docking scores, produce desired rapidly alone. (30) iterations, advanced ADMET standard improve compounds. agreement authors, (31) cannot simplified simple button-clicking exercise.Postprocessing results obtained any crucial, three main reasons. First, posteriori, cost. relative binding energies (RBFE) (32) fragment orbital (FMO) interaction energies. (33) Second, always optimize multiple simultaneously, reason, them must sequentially during postprocessing stage. grow ligands pocket enthalpic contributions potency, protein–ligand Finally, evolve time, so importance step, developing pipelines integrate conventional chemistry, AI/ML, physics-based calculations speed up Computational Pipeline Protein ModelingAn incredibly project. Usually, X-ray crystallography cryogenic electron microscopy (cryo-EM). Until very recently, non-AI were homology proteins available. AlphaFold 2 (AF2), member family predicting AI, demonstrated remarkable predictions. (34) local installation resource iterative construct preparation fitted experimentally density. combined AF2 ProteinMPNN (35) increase stability production yield. transform where possible isolate miniscule amounts protein. AF Multimer (36) protein–protein complexes helps structural biologists obtain initial targets. density refined. Novel modeled FoldDock, (37) optimizes sequence alignments multimer run, producing better score separating acceptable incorrect models.The AlphaFoldDB (34,38) database DeepMind hosted EBI, Multimer, tremendous resources aspects ligandability estimation VS docking. aim targets complex interest. When possible, classical presence known ligand side chains site suitable conformation docking.Recent enabled ligand-protein complexes. Methods RoseTTAFold-AllAtom, (39) Umol, (40) AF3 (41) claim details proteins' interactions ligands, metal ions, nucleic acids, covalent binders precision surpassing established watch developments Active LearningMedicinal operates especially true hit-to-lead phase Where thin ground expensive generate, active (AL) purpose sufficient efficient manner. precise, AL ML-based strategy aims maximize respect (objective function) minimal algorithm iteratively selects predefined pool unlabeled items (in case ideas) according so-called acquisition function, balances (selection promising, current knowledge) less unknown regions model's overall knowledge). (42) Analogously, BO seeks identify next defined parameter optimum objective, could multiparameter (MPO) score. MPO contain primary assay lipophilicity, metabolic stability, permeability measurements follow fewer off-target enzymes, receptors, transporters, depending requirements. informative vast space. (43,44) enable make-on-demand libraries Enamine REAL (45) reduce number needed reach goals.Traditional structure-based ligand-based too computationally time-consuming brute-force billions (46) Additionally, costs function. solution, built open source MolPal, combines dynamics (MD)-based highest performing compounds.The Design-Make-Test-Analyze (DMTA) cycle configured explores (47) way, assisting selecting experimentally. should ultimately reduction cycles. form, ranks list coming chemists' ideas. While limits exploratory capacity, acceptance proposed solutions designers search manageable size. proposes machine-based above). structures. mindset team avoid unwanted bias. easy synthesize. (8,48,49) Feedback highlight synergistic opportunities improvement, e.g., flagging single, outlier result arising synthetic improvements. Synthetic Tractability Retrosynthesis PredictionThe synthesis compounds, "Make" phase, rate-limiting step DMTA cycle. (50) Therefore, tractability key aspect "Design" phase. applies AI-generated alike. Currently explicitly encode criterion growing one exciting domain invention AI computer-aided planning (CASP) (51) filtering full blown retrosynthesis analysis faster (52) chemists mind at least mental difficulty involved. reached sophistication efficiency sharing expertise knowledge daily, example reactivity building blocks intermediates. addition electronic laboratory notebooks (ELNs) block inventories, does (53,54) increasingly being chemists, scaffold-hopping, inspiration, easier routes. As areas, output disappointing first impression, if parity users' expected. (50,54) commercial CASP via web interface inspiration cross-check planning; quick links background literature useful. Evaluation expensive, proved difficult perhaps had unrealistic expectations performance. workflows, utility, but apply manual assessment last steps. Safety AssessmentIn tractability, safety risks considered. concern programs. Often, become apparent after deployed. Hence, flag earlier cheaply, receiving considerable attention. (55) Pure probability Drug-Induced Liver Injury (DILI), carbon atoms sp3 hybridization. (56) desirable aid prior synthesizing potentially reducing associated assays. tend rule-based supervised algorithms. (56,57) performance, beneficial incorporate vitro bile salt export pump (BSEP) transporter inhibition cellular cytotoxicity data) sophisticated (58)In contrast individual cover toxicity, omics provide snapshot state response exposure. Fortunately, high-throughput creation size train (59) patterns profiles adverse outcomes resulting organ toxicity. Once trained, risk high accuracy, outperforming existing (60) Moreover, works equally modalities biologics. create model, utilized transcriptomics platform (ScreenSeq) cell hundreds well-characterized different types serve reference PipelinesThe advent methods, increased prioritize numbers done applying alongside simpler property scores. Each scored criteria (drug-likeness, predicted attributes, properties, etc.), aggregated ad-hoc, correctly parametrized, rank promising round synthesis. technical deploying pipeline orchestration tasks, diversity good orchestrator needs file formats, handle environments, manage efficiently, scale-up jobs robust. Because evolving rapidly, designed makes add change deployed on.Automation save resources, while encoding practices improving reproducibility, facilitates objectivity BRADSHAW (61) machine-generated ideas processed thus trying selection. several (62−64) (65−67) platforms automating mind. much influenced Green et al. Besnard automate workflows wherever Knime high-performance computing (HPC) pipelining solution. cited authors integration, robustness, simplicity, flexibility. adapted ever-changing reusable, parts, Context Chemistry ProjectsThe increasing lower HPC costs, led pharma explore working. (61,68) At Evotec, group R&D isRD) responsible adapting cutting-edge stack, operational (Molecular Architects MAs), who collaboration partners. concept MAs (illustrated Figure 1) fuse expertise, foundation science consider facilitator establishing trust, attaining ambitious goals, expediting candidates. (i) right used, irrespective origin not, (ii) clean understood, (iii) clear met, (iv) bespoke created hypothesis minimum compounds.Figure 1Figure 1. Secret sauce excellence Evotec.High Resolution ImageDownload MS PowerPoint SlideThe D2MTL (Design-Decide-Make-Test-Learn) introduced evolution well-establish

Language: Английский

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The Artificial Intelligence-Driven Pharmaceutical Industry: A Paradigm Shift in Drug Discovery, Formulation Development, Manufacturing, Quality Control, and Post-Market Surveillance

European Journal of Pharmaceutical Sciences, Journal Year: 2024, Volume and Issue: 203, P. 106938 - 106938

Published: Oct. 16, 2024

Language: Английский

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The Omics‐Driven Machine Learning Path to Cost‐Effective Precision Medicine in Chronic Kidney Disease

PROTEOMICS, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 10, 2025

ABSTRACT Chronic kidney disease (CKD) poses a significant and growing global health challenge, making early detection slowing progression essential for improving patient outcomes. Traditional diagnostic methods such as glomerular filtration rate proteinuria are insufficient to capture the complexity of CKD. In contrast, omics technologies have shed light on molecular mechanisms CKD, helping identify biomarkers assessment management. Artificial intelligence (AI) machine learning (ML) could transform CKD care, enabling biomarker discovery diagnosis risk prediction, personalized treatment. By integrating multi‐omics datasets, AI can provide real‐time, patient‐specific insights, improve decision support, optimize cost efficiency by avoidance unnecessary treatments. Multidisciplinary collaborations sophisticated ML advance therapeutic strategies in This review presents comprehensive overview pipeline translating data into treatment, covering recent advances research, role critical need clinical validation AI‐driven discoveries ensure their efficacy, relevance, cost‐effectiveness care.

Language: Английский

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The continuing importance of chemical intuition for the medicinal chemist in the era of artificial intelligence

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

Published: Jan. 14, 2025

Language: Английский

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AI In Action: Redefining Drug Discovery and Development

Clinical and Translational Science, Journal Year: 2025, Volume and Issue: 18(2)

Published: Feb. 1, 2025

The 2024 Nobel Prize in Chemistry was awarded to David Baker, Demis Hassabis, and John Jumper for their groundbreaking work using AI predict protein structures design functional proteins. development of the AlphaFold model has solved a long-standing challenge biology by accurately predicting complex proteins, which are crucial understanding function. enhances our ability new proteins with specific functions accelerates drug discovery providing detailed insights into behavior interactions. recognition this underscores transformative potential life sciences its critical role future research (R&D). revolutionized space recent years, applications ranging from highly accurate structure predictions [1], optimization both small large molecules [2]. Several foundational models have been developed encoding information powerful way support pipeline [3, 4]. Figure 1 highlights areas where now plays significant is poised disrupt traditional experimental techniques. culmination AI-driven de novo design, entire preclinical can be performed silico, resulting billions dollars R&D cost savings, translating reduced costs medications higher clinical success rates via safer more developable showing strong efficacy well-selected targets. While as-yet unproven, rate 21 AI-developed drugs that completed Phase I trials as December 2023 80%–90%, significantly than ~40% methods [5]. We continue see an increase number candidate enter stages, trend growing at exponential rate—from 3 2016 17 2020 67 intersection between high-quality data access across science modalities like imaging, multi-omics, DMRs, very repertoires, advancements scaling architecture deep learning led explosion healthcare. some publicly available, much it proprietary under control pharmaceutical companies, partly due regulatory privacy concerns. Conversely, innovation being academic industry laboratories, often funded spin-off ventures Genentech, Recursion, Absci, recently, Evolutionary Scale. Such AI-first companies found synergistic partnerships thereby gaining datasets upon apply expertise. Some these acquisitions such 2009 purchase Genentech Roche approximately $46.8 billion, highlighting value internalization brings companies. use cover full cycle product, including discovery, development, application assessment setting. Recent Food Drug Administration (FDA) included two distinct case studies. first exemplifies conventional machine (ML) approaches through project aimed decoding kinase–adverse event associations molecule kinase inhibitors (SMKIs). By constructing multi-domain dataset 4638 patients registrational 16 FDA-approved SMKIs, ML Random Survival Forests (RSF), Artificial Neural Networks (ANNs), DeepHit were utilized find 442 kinases 2145 adverse events. This made accessible interactive web application, "Identification Kinase-Specific Signal" (https://gongj.shinyapps.io/ml4ki). platform aids experimentalists identifying verifying kinase-inhibitor pairs serves precision-medicine tool mitigate individual patient safety risks forecasting signals [6]. In general, credibility extrapolation generalization heavily depends on diversity comprehensiveness training data. Future studies integrating richer genomic, phenotypic, demographic could further improve precision help refine applicability subgroups. For research, while Multi-Input not employed study, they represent promising heterogeneous datasets, activity, data, outcomes, unified predictive framework. Additionally, hybrid combining neural networks Markov Chains explored capture sequential dependencies disease progression robustness diverse cohorts. second study showcases generative PharmBERT, domain-specific language (LLM) labels [7]. Leveraging BERT architecture, PharmBERT pre-trained textual extracted 138,924 raw sourced DailyMed. pre-training text improved model's performance extracting pharmacokinetic labeling. demonstrated superior tasks reaction (ADR) detection ADME (absorption, distribution, metabolism, excretion) classification, surpassing other ClinicalBERT BioBERT. advancement LLMs enhance efficiency text-related extraction labels. Together, illustrate impact science. Traditional provide robust frameworks specific, structured analyses, offer expansive capabilities handling unstructured developing generalized intelligence. Both advancing personalized medicine optimizing processes. document authoring opportunity time saving last subject's visit filing. Generative Pre-trained Transformer (GPT) algorithm task. finding adequate set (consisting results, protocols, final reports). One general-purpose GPT-based documents described Bouton [8]. GPT promising, ensure does generate inaccuracies, commonly referred 'hallucinations,' given sensitivity high stakes documents. There remains consisting reports. Work code met variable success. Shin et al. [9] had modest initial coding NONMEM common platforms. However, all required correction errors humans. pyDarwin general approach PMX selection. makes available algorithms search optimal pharmacometrics model, pharmacokinetics pharmacodynamics. identifies combination user-defined features, compartments, covariate relationships, random effects, based criteria. method shown manual forward addition/backward elimination method, considerable savings [10]. 2 summarizes results surveys during "When Meets Development" session American Society Clinical Pharmacology Therapeutics Annual Meeting. question evaluates views AI's change R&D. Notably, 80% participants recognized impact, 12% unconvinced. No unaware R&D, suggesting level awareness within pharmacology community. A minority (6%) uncertain about current capabilities, 2% selected unspecified option. Regarding next 5–10 45% highlighted preference optimization, followed (28%), target validation (20%), testing screening (7%). highlight familiarity, usage, perceptions among community, indicating interest optimism development. Looking ahead, integration accelerate, driven leading tech NVIDIA's GPUs enabling faster efficient Google Health leveraging expertise analytics modeling analysis. Apple contributing health ecosystem, facilitating real-time monitoring. OpenAI's cutting-edge revolutionizing researchers hypotheses analyze scientific literature. These innovations collectively promise streamline pipeline, reduce costs, heralding era medicine. As global investment accelerates, so expectation outcomes programs. 2024, there no on-market pipeline. drivers AI, particularly healthcare, need show disruption existing business processes tangible financial gains. happen launch medication or AI-based improvements lead approval. content perspective presented intelligence (AI) field computer science, statistics, engineering develop systems capable performing typically require human healthcare span postmarket surveillance advanced manufacturing. provides translational, late-phase perspectives community survey impacts research. M.S. employee Certara. A.K. AstraZeneca. All authors declared competing interests work.

Language: Английский

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Artificial intelligence for life sciences: A comprehensive guide and future trends

The Innovation Life, Journal Year: 2024, Volume and Issue: unknown, P. 100105 - 100105

Published: Jan. 1, 2024

<p>Artificial intelligence has had a profound impact on life sciences. This review discusses the application, challenges, and future development directions of artificial in various branches sciences, including zoology, plant science, microbiology, biochemistry, molecular biology, cell developmental genetics, neuroscience, psychology, pharmacology, clinical medicine, biomaterials, ecology, environmental science. It elaborates important roles aspects such as behavior monitoring, population dynamic prediction, microorganism identification, disease detection. At same time, it points out challenges faced by application data quality, black-box problems, ethical concerns. The are prospected from technological innovation interdisciplinary cooperation. integration Bio-Technologies (BT) Information-Technologies (IT) will transform biomedical research into AI for Science paradigm.</p>

Language: Английский

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Data-centric challenges with the application and adoption of artificial intelligence for drug discovery

Expert Opinion on Drug Discovery, Journal Year: 2024, Volume and Issue: 19(11), P. 1297 - 1307

Published: Sept. 24, 2024

Artificial intelligence (AI) is exhibiting tremendous potential to reduce the massive costs and long timescales of drug discovery. There are however important challenges currently limiting impact scope AI models.

Language: Английский

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The Dawn of a New Pharmaceutical Epoch: Can AI and Robotics Reshape Drug Formulation?

Advanced Healthcare Materials, Journal Year: 2024, Volume and Issue: 13(29)

Published: Aug. 18, 2024

Abstract Over the last four decades, pharmaceutical companies’ expenditures on research and development have increased 51‐fold. During this same time, clinical success rates for new drugs remained unchanged at about 10 percent, predominantly due to lack of efficacy and/or safety concerns. This persistent problem underscores need innovate across entire drug process, particularly in formulation, which is often deprioritized under‐resourced.

Language: Английский

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Identification and Evaluation of Bioactive Compounds from Azadirachta indica as Potential Inhibitors of DENV-2 Capsid Protein: An Integrative Study Utilizing Network Pharmacology, Molecular Docking, Molecular Dynamics Simulations, and Machine Learning Techniques.

Heliyon, Journal Year: 2025, Volume and Issue: 11(4), P. e42594 - e42594

Published: Feb. 1, 2025

Dengue fever is a viral disease caused by the dengue flavivirus and transmitted through mosquito bites in humans. According to World Health Organization, severe causes approximately 40,000 deaths annually, nearly 4 billion people are at risk of infection. The urgent need for effective treatments against virus has led extensive research on potential bioactive compounds. In this study, we utilized network pharmacology approach identify DENV-2 capsid protein as an appropriate target intervention. Subsequently, selected library 537 phytochemicals derived from Azadirachta indica (Family: Meliaceae), known their anti-dengue properties, explore inhibitors protein. compound was subjected molecular docking potent with high binding affinity. We 81 hits based thorough analysis affinities, particularly those exhibiting higher energy than established inhibitor ST-148. After evaluating characteristics, identified two top-scored compounds them dynamics simulations assess stability properties. Additionally, predicted ADMET properties using silico methods. One inhibitors, [(5S,7R,8R,9R,10R,13R,17R)-17-[(2R)-2-hydroxy-5-oxo-2H-furan-4-yl]-4,4,8,10,13-pentamethyl-3-oxo-5,6,7,9,11,12,16,17-octahydrocyclopenta[a]phenanthren-7-yl] acetate (AI-59), showed highest affinity -10.4 kcal/mol. Another compound, epoxy-nimonol (AI-181), demonstrated number H-bonds score -9.5 During simulation studies, both have exhibited noteworthy outcomes. Through mechanics employing Generalized Born surface area (MM/GBSA) calculations, AI-59 AI-181 displayed negative ΔG_bind scores -74.99 -83.91 kcal/mol, respectively. hit present investigation hold developing drugs targeting infections. Furthermore, knowledge gathered study serves foundation structure- or ligand-based exploration

Language: Английский

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The Transformative Impact of Artificial Intelligence on Drug Discovery: A Technical Review

International Journal of Scientific Research in Computer Science Engineering and Information Technology, Journal Year: 2025, Volume and Issue: 11(1), P. 2772 - 2780

Published: Feb. 10, 2025

This comprehensive article examines the transformative impact of artificial intelligence on drug discovery and development processes. The explores traditional challenges in pharmaceutical development, including extended timelines, high costs, low success rates, which have prompted industry's shift toward AI-driven solutions. investigates how AI applications revolutionized early research stages, clinical trial management, validation Through a detailed examination recent implementations, demonstrates AI's significant improvements target identification, molecular screening, optimization. also addresses technical considerations, data quality requirements, algorithm challenges, resource implications for successful integration research. provides insights into emerging trends future directions while highlighting achievements limitations discovery.

Language: Английский

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