New strategies to enhance the efficiency and precision of drug discovery

Frontiers in Pharmacology, Год журнала: 2025, Номер 16

Опубликована: Фев. 11, 2025

Drug discovery plays a crucial role in medicinal chemistry, serving as the cornerstone for developing new treatments to address wide range of diseases. This review emphasizes significance advanced strategies, such Click Chemistry, Targeted Protein Degradation (TPD), DNA-Encoded Libraries (DELs), and Computer-Aided Design (CADD), boosting drug process. Chemistry streamlines synthesis diverse compound libraries, facilitating efficient hit lead optimization. TPD harnesses natural degradation pathways target previously undruggable proteins, while DELs enable high-throughput screening millions compounds. CADD employs computational methods refine candidate selection reduce resource expenditure. To demonstrate utility these methodologies, we highlight exemplary small molecules discovered past decade, along with summary marketed drugs investigational that exemplify their clinical impact. These examples illustrate how techniques directly contribute advancing chemistry from bench bedside. Looking ahead, Artificial Intelligence (AI) technologies interdisciplinary collaboration are poised growing complexity discovery. By fostering deeper understanding transformative this aims inspire innovative research directions further advance field chemistry.

Язык: Английский

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AlphaFold and what is next: bridging functional, systems and structural biology

Expert Review of Proteomics, Год журнала: 2025, Номер unknown

Опубликована: Янв. 17, 2025

The DeepMind's AlphaFold (AF) has revolutionized biomedical research by providing both experts and non-experts with an invaluable tool for predicting protein structures. However, while AF is highly effective structures of rigid globular proteins, it not able to fully capture the dynamics, conformational variability, interactions proteins ligands other biomacromolecules. In this review, we present a comprehensive overview latest advancements in 3D model predictions biomacromolecules using AF. We also provide detailed analysis its strengths limitations, explore more recent iterations, modifications, practical applications strategy. Moreover, map path forward expanding landscape toward every peptide proteome most physiologically relevant form. This discussion based on extensive literature search performed PubMed Google Scholar. While significant progress been made enhance AF's modeling capabilities, argue that combined approach integrating various silico vitro methods will be beneficial future structural biology, bridging gaps between static dynamic features their functions.

Язык: Английский

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Ligand-Conditioned Side Chain Packing for Flexible Molecular Docking

Journal of Chemical Theory and Computation, Год журнала: 2025, Номер unknown

Опубликована: Янв. 25, 2025

Molecular docking is a crucial technique for elucidating protein-ligand interactions. Machine learning-based methods offer promising advantages over traditional approaches, with significant potential further development. However, many current machine face challenges in ensuring the physical plausibility of generated poses. Additionally, accommodating protein flexibility remains difficult existing methods, limiting their effectiveness real-world scenarios. Herein, we present ApoDock, modular paradigm that combines learning-driven conditional side chain packing based on backbone and ligand information sampling to ensure physically realistic The poses are finally scored by developed mixture density network-based scoring function. With accurate packing, physical-based pose sampling, ranking ability, ApoDock demonstrates competitive performance across diverse applications, especially when using modeled structure (AlphaFold2 ESMFold) docking, exhibiting success rate 28.5% higher than other state art (SOTA), highlighting its as valuable tool binding studies related applications.

Язык: Английский

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DeepPath: Overcoming data scarcity for protein transition pathway prediction using physics-based deep learning

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2025, Номер unknown

Опубликована: Март 2, 2025

The structural dynamics of proteins play a crucial role in their function, yet most experimental and deep learning methods produce only static models. While molecular (MD) simulations provide atomistic insight into conformational transitions, they remain computationally prohibitive, particularly for large-scale motions. Here, we introduce DeepPath, deep-learning-based framework that rapidly generates physically realistic transition pathways between known protein states. Unlike conventional supervised approaches, DeepPath employs active to iteratively refine its predictions, leveraging mechanical force fields as an oracle guide pathway generation. We validated on three biologically relevant test cases: SHP2 activation, CdiB H1 secretion, the BAM complex lateral gate opening. accurately predicted all cases, reproducing key intermediate structures transient interactions observed previous studies. Notably, also inwardand outward-open states closely aligns with experimentally hybrid-barrel structure (TMscore = 0.91). Across achieved accurate predictions within hours, showcasing efficient alternative MD exploring transitions.

Язык: Английский

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UNC9426, a Potent and Orally Bioavailable TYRO3-Specific Inhibitor

Journal of Medicinal Chemistry, Год журнала: 2025, Номер unknown

Опубликована: Март 11, 2025

TYRO3 plays a critical role in platelet aggregation as response amplifier. Selective inhibition of may provide therapeutic benefits for treating thrombosis and related diseases without increasing bleeding risk. We employed structure-based approach discovered novel potent inhibitor UNC9426 (12) with an excellent Ambit selectivity score (S50 (1.0 μM) = 0.026) favorable pharmacokinetic properties mice. Treatment reduced time blocked TYRO3-dependent functions tumor cells macrophages, implicating its utility multiple indications.

Язык: Английский

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AI/ML methodologies and the future-will they be successful in designing the next generation of new chemical entities?

Journal of Cheminformatics, Год журнала: 2025, Номер 17(1)

Опубликована: Апрель 6, 2025

Cheminformatics and chemical databases are essential to drug discovery. However, machine learning (ML) artificial intelligence (AI) methodologies changing the way in which data is used. How will use of change discovery moving forward? do new ML methods molecular property prediction, hit lead target identification structure prediction differ compare with previous computational methods? Will improve diversity ligand design, offer enhancements. There still many advantages physics based they something lacking ML/ AI methods. Additionally, training often give best results when experimental assay measurements fed back into model. Often modeling not diametrically opposed but greatest advantage used complementary.

Язык: Английский

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Investigations on genomic, topological and structural properties of diguanylate cyclases involved in Vibrio cholerae biofilm signalling using in silico techniques: Promising drug targets in combating cholera

Current Research in Structural Biology, Год журнала: 2025, Номер unknown, С. 100166 - 100166

Опубликована: Апрель 1, 2025

During various stages of its life cycle, Vibrio cholerae initiate biofilm signalling cascade. Intercellular high level the nucleotide 3'-5' cyclic dimeric guanosine monophosphate (c-di-GMP), synthesized by diguanylate cyclases (DGCs) from precursor molecule GTP, is crucial for formation. Present study endeavours to in silico approaches evaluating genomic, physicochemical, topological and functional properties six c-di-GMP regulatory DGCs (CdgA, CdgH, CdgK, CdgL, CdgM, VpvC) V. cholerae. Genomic investigations unveiled that codon preferences were inclined towards AU ending over GC codons overall content ranged 44.6 49.5 with adaptation index ranging 0.707 0.783. Topological analyses deciphered presence transmembrane domains all proteins. All acidic, hydrophilic thermostable. Only CdgA, CdgH VpvC predicted be stable during vitro conditions. Non-polar amino acids leucine being most abundant acid among these α-helix as predominant secondary structure, responsible forming regions structure analysis. Tertiary structures proteins obtained computation using AlphaFold trRosetta. Predicted both servers compared aspects PROCHECK, ERRAT Modfold8 servers. Selected 3D refined GalaxyRefine. InterPro Scan revealed a conserved GGDEF domain active site residues domain. Molecular docking studies CB-DOCK 2 tool DGCs, exhibited highest affinity GTP (-5.6 kcal/mol), which was closely followed CdgL (-5.5 kcal/mol). MD simulations depicted DGC-GTP complexes due considerably low eigenvalues. Such are considered provide maiden insights into genomic structural actively involved systems, it projected beneficial discovery novel DGC inhibitors can target downregulate system develop anti-biofilm strategies against cholera pathogen.

Язык: Английский

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Research approaches for exploring the hidden conversations of GPCR Transactivation

Molecular Pharmacology, Год журнала: 2025, Номер 107(6), С. 100043 - 100043

Опубликована: Май 8, 2025

G protein-coupled receptor (GPCR) signaling is a crucial physiological mechanism that encompasses wide range of phenomena. Although traditional GPCR involves protein or arrestin-related activation, other modes such as biphasic dimer oligomeric and transactivation have also been observed. Herein, we focus on the increasingly recognized process GPCR-transactivation. Transactivation refers to ability GPCRs activate types, especially tyrosine kinases, without engaging their own specific ligands. This cross-talk between receptors facilitates integration multiple pathways, thereby regulating diverse cellular responses, which underscores its significance. In this review, provide comprehensive overview role GPCR-transactivation in physiology. We discuss growing interest field examine various tools available for studying transactivation. Additionally, highlight recent advancements emerging application research. Finally, propose future research directions consider potential impact new technologies rapidly evolving field. SIGNIFICANCE STATEMENT: plays key integrating pathways by activating proteins, like binding Here, significance approaches used study phenomenon.

Язык: Английский

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In Silico Discovery of Novel Potent Trace Amine-Associated Receptor TAAR1 Agonists as Promising Drug Candidates for the Treatment of Schizophrenia

Journal of Medicinal Chemistry, Год журнала: 2025, Номер unknown

Опубликована: Май 8, 2025

All currently available antipsychotics act primarily as dopamine D2 receptor blockers, demonstrating established efficacy for positive symptoms but showing limited effectiveness against negative and cognitive impairment, coupled with significant side effects. The latest studies have demonstrated that the coupling of trace amine-associated 1 (TAAR1) Gs or Gq proteins has a synergistic effect on addressing these schizophrenia symptoms. Inspired by this, we propose an innovative concept designing dual-pathway TAAR1-Gs/Gq agonists treatment. Through structure-based multilevel virtual screening rational optimization, identified novel compound ZH8965 excellent Gs/Gq activation potency. Subsequent molecular modeling explained its unique mechanism action. Oral treatment significantly alleviated antipsychotic-like phenotypes impairment without inducing catalepsy like haloperidol in MK-801-induced mice. Moreover, favorable PK safety profiles. Considering results, represents promising non-D2 receptor-targeting therapeutic candidate schizophrenia.

Язык: Английский

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Phylogenetic and toxicogenomic profiling of CYPomes to elucidate convergent and divergent insecticide resistance profiles in three rice planthopper species

Journal of Pest Science, Год журнала: 2025, Номер unknown

Опубликована: Май 14, 2025

Язык: Английский

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