The Role of AI in Drug Discovery

ChemBioChem, Год журнала: 2024, Номер 25(14)

Опубликована: Май 13, 2024

The emergence of Artificial Intelligence (AI) in drug discovery marks a pivotal shift pharmaceutical research, blending sophisticated computational techniques with conventional scientific exploration to break through enduring obstacles. This review paper elucidates the multifaceted applications AI across various stages development, highlighting significant advancements and methodologies. It delves into AI's instrumental role design, polypharmacology, chemical synthesis, repurposing, prediction properties such as toxicity, bioactivity, physicochemical characteristics. Despite promising advancements, also addresses challenges limitations encountered field, including data quality, generalizability, demands, ethical considerations. By offering comprehensive overview discovery, this underscores technology's potential significantly enhance while acknowledging hurdles that must be overcome fully realize its benefits.

Язык: Английский

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Decoding Neural Circuit Dysregulation in Bipolar Disorder: Toward an Advanced Paradigm for Multidimensional Cognitive, Emotional, and Psychomotor Treatment

Neuroscience & Biobehavioral Reviews, Год журнала: 2025, Номер 169, С. 106030 - 106030

Опубликована: Фев. 1, 2025

Bipolar disorder (BD) is characterized by a complex constellation of emotional, cognitive, and psychomotor disturbances, each deeply intertwined with underlying dysfunctions in large-scale brain networks neurotransmitter systems. This manuscript integrates recent advances neuroimaging, neuromodulation, pharmacological research to provide comprehensive view BD's pathophysiology, emphasizing the role network-specific their clinical manifestations. We explore how dysregulation within fronto-limbic network, particularly involving prefrontal cortex (PFC) amygdala, underpins emotional instability that defines both manic depressive episodes. Additionally, impairments central executive network (CEN) default mode (DMN) are linked cognitive deficits, hyperactivity DMN driving rumination inflexibility, while CEN underactivity contributes attentional lapses impaired function. Psychomotor symptoms, which oscillate between mania retardation depression, closely associated imbalances systems, dopamine serotonin, basal ganglia-thalamo-cortical motor pathway. Recent studies indicate these disturbances further exacerbated disruptions connectivity, leading control regulation. Emerging therapeutic strategies discussed, focus on neuromodulation techniques such as transcranial magnetic stimulation (TMS) deep (DBS), show promise restoring balance critical networks. Furthermore, interventions modulate synaptic functioning neuronal plasticity offer potential for addressing symptoms BD. underscores need an integrative treatment approach simultaneously targets neural circuits systems address full spectrum Drawing advancements neurobiological models frameworks, this proposal outlines pathway development precision-tailored interventions. These approaches designed optimize outcomes, ultimately striving elevate quality life individuals living bipolar (BD), remaining firmly grounded latest empirical evidence theoretical insights.

Язык: Английский

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Advanced AI and ML frameworks for Transforming Drug Discovery and Optimization: With Innovative insights in Polypharmacology, Drug Repurposing, Combination Therapy and Nanomedicine.

European Journal of Medicinal Chemistry, Год журнала: 2024, Номер 284, С. 117164 - 117164

Опубликована: Дек. 13, 2024

Язык: Английский

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Two heads are better than one: Unravelling the potential Impact of Artificial Intelligence in nanotechnology

Nano TransMed, Год журнала: 2024, Номер 3, С. 100041 - 100041

Опубликована: Июль 9, 2024

Artificial Intelligence (AI) and Nanotechnology are two cutting-edge fields that hold immense promise for revolutionizing various aspects of science, technology, everyday life. This review delves into the intersection these disciplines, highlighting synergistic relationship between AI Nanotechnology. It explores how techniques such as machine learning, deep neural networks being employed to enhance efficiency, precision, scalability nanotechnology applications. Furthermore, it discusses challenges, opportunities, future prospects integrating with nanotechnology, paving way transformative advancements in diverse domains ranging from healthcare materials science environmental sustainability beyond.

Язык: Английский

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Toward structure–multiple activity relationships (SMARts) using computational approaches: A polypharmacological perspective

Drug Discovery Today, Год журнала: 2024, Номер 29(7), С. 104046 - 104046

Опубликована: Май 27, 2024

Язык: Английский

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Allostery

Quarterly Reviews of Biophysics, Год журнала: 2025, Номер 58

Опубликована: Янв. 1, 2025

Abstract Allostery describes the ability of biological macromolecules to transmit signals spatially through molecule from an allosteric site – a that is distinct orthosteric binding sites primary, endogenous ligands functional or active site. This review starts with historical overview and description classical example allostery hemoglobin other well-known examples (aspartate transcarbamoylase, Lac repressor, kinases, G-protein-coupled receptors, adenosine triphosphate synthase, chaperonin). We then discuss fringe allostery, including intrinsically disordered proteins inter-enzyme influence dynamics, entropy, conformational ensembles landscapes on mechanisms, capture essence field. Thereafter, we give over central methods for investigating molecular covering experimental techniques as well simulations artificial intelligence (AI)-based methods. conclude allostery-based drug discovery, its challenges opportunities: recent advent AI-based methods, compounds are set revolutionize discovery medical treatments.

Язык: Английский

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Advanced AI Applications for Drug Discovery

Advances in medical technologies and clinical practice book series, Год журнала: 2024, Номер unknown, С. 42 - 86

Опубликована: Апрель 26, 2024

Addressing the critical challenge of lengthy and costly drug development, this chapter illuminates transformative role advanced artificial intelligence (AI) in discovery. It aims to dissect impact AI methodologies streamlining these traditionally complex processes. This begins by highlighting inefficiencies conventional discovery methods, emphasizing their resource-intensive nature. An in-depth discussion how technologies are revolutionizing identification novel targets, optimizing molecular structures candidates, accurately predicting efficacy toxicity is needed. exploration underscores AI's dual advantages: significantly reducing development timelines expenses while simultaneously enhancing precision predictions, leading safer more effective drugs. concludes with a vision future where AI-driven methods fully integrated personalized medicine genomics, signaling onset new era healthcare therapeutic innovation.

Язык: Английский

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The “Doorstop Pocket” In Thioredoxin Reductases─An Unexpected Druggable Regulator of the Catalytic Machinery

Journal of Medicinal Chemistry, Год журнала: 2024, Номер 67(18), С. 15947 - 15967

Опубликована: Сен. 9, 2024

Pyridine nucleotide-disulfide oxidoreductases are underexplored as drug targets, and thioredoxin reductases (TrxRs) stand out compelling pharmacological targets. Selective TrxR inhibition is challenging primarily due to the reliance on covalent strategies. Recent studies identified a regulatory druggable pocket in

Язык: Английский

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Developing a Semi-Supervised Approach Using a PU-Learning-Based Data Augmentation Strategy for Multitarget Drug Discovery

International Journal of Molecular Sciences, Год журнала: 2024, Номер 25(15), С. 8239 - 8239

Опубликована: Июль 28, 2024

Multifactorial diseases demand therapeutics that can modulate multiple targets for enhanced safety and efficacy, yet the clinical approval of multitarget drugs remains rare. The integration machine learning (ML) deep (DL) in drug discovery has revolutionized virtual screening. This study investigates synergy between ML/DL methodologies, molecular representations, data augmentation strategies. Notably, we found SVM match or even surpass performance state-of-the-art DL methods. However, conventional often involves a trade-off true positive rate false rate. To address this, introduce Negative-Augmented PU-bagging (NAPU-bagging) SVM, novel semi-supervised framework. By leveraging ensemble classifiers trained on resampled bags containing positive, negative, unlabeled data, our approach is capable managing rates while maintaining high recall rates. We applied this method to identification multitarget-directed ligands (MTDLs), where are critical compiling list interaction candidate compounds. Case studies demonstrate NAPU-bagging identify structurally MTDL hits ALK-EGFR with favorable docking scores binding modes, as well pan-agonists dopamine receptors. methodology should serve promising avenue screening, especially MTDLs.

Язык: Английский

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Advances in physiological and clinical relevance of hiPSC-derived brain models for precision medicine pipelines

Frontiers in Cellular Neuroscience, Год журнала: 2025, Номер 18

Опубликована: Янв. 6, 2025

Precision, or personalized, medicine aims to stratify patients based on variable pathogenic signatures optimize the effectiveness of disease prevention and treatment. This approach is favorable in context brain disorders, which are often heterogeneous their pathophysiological features, patterns progression treatment response, resulting limited therapeutic standard-of-care. Here we highlight transformative role that human induced pluripotent stem cell (hiPSC)-derived neural models poised play advancing precision for particularly emerging innovations improve relevance hiPSC physiology. hiPSCs derived from accessible patient somatic cells can produce various types tissues; current efforts increase complexity these models, incorporating region-specific tissues non-neural microenvironment, providing increasingly relevant insights into human-specific neurobiology. Continued advances tissue engineering combined with genomics, high-throughput screening imaging strengthen physiological thus ability uncover mechanisms, vulnerabilities, fluid-based biomarkers will have real impact neurological True understanding, however, necessitates integration hiPSC-neural biophysical data, including quantitative neuroimaging representations. We discuss recent cellular neuroscience provide direct connections through generative AI modeling. Our focus great potential synergy between pave way personalized becoming a viable option suffering neuropathologies, rare epileptic neurodegenerative disorders.

Язык: Английский

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