AIMC Topic: Drug Discovery

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Machine-learning approaches in drug discovery: methods and applications.

Drug discovery today
During the past decade, virtual screening (VS) has evolved from traditional similarity searching, which utilizes single reference compounds, into an advanced application domain for data mining and machine-learning approaches, which require large and ...

Multiomics Profiling of T-cell Leukemia and Lymphoma Enables Targeted Therapeutic Discovery.

Cancer research
UNLABELLED: T-cell leukemias and lymphomas (TCL) form a heterogeneous group of rare and often aggressive malignancies. Because of the rarity and heterogeneity of TCL subtypes, clinical trials are challenging to conduct, making pharmacogenomic studies...

Integrative strategies in drug discovery: Harnessing genomics, deep learning, and computer-aided drug design.

Computational biology and chemistry
The development of novel drugs increasingly relies on advanced omics technologies, including genomics, transcriptomics, proteomics, and metabolomics. These approaches provide insights into genetic mutations, biomarkers, and disease pathways. However,...

Anaesthesia in 2050: how emerging technologies will transform our practice: The Sir Robert Macintosh Lecture 2025.

European journal of anaesthesiology
In the next 25 years, we will see unprecedented technological progress due to the artificial intelligence (AI) revolution. AI-driven advances will be made in five strongly interconnected domains: reenergised drug discovery, continuous real-time monit...

Unravelling the multifaceted actions of neurosteroids: Machine learning and in vitro screening for novel target discovery.

British journal of pharmacology
BACKGROUND AND PURPOSE: Neurosteroids (NS) modulate neuronal function and are promising therapeutic agents for neuropsychiatric disorders. NS analogues are approved for treating postpartum depression and are of interest in other disorders. Gamma-amin...

Discovery of novel GluN1/GluN3A NMDA receptor inhibitors using a deep learning-based method.

Acta pharmacologica Sinica
Ligand-based drug discovery methods typically utilize pharmacophore similarities among molecules to screen for potential active compounds. Among these, scaffold hopping is a widely used ligand-based lead identification strategy that facilitates clini...

Harnessing Allostery to Modulate Protein-Protein Interactions: From Function to Therapeutic Innovations.

Journal of molecular biology
Protein-protein interactions (PPIs) are ubiquitous mediators of cellular functions, and their dysregulation is central to numerous pathological conditions. Traditional drug discovery strategies targeting PPIs directly have faced considerable obstacle...

Unlocking the metabolic potential of endophytic fungi through epigenetics: a paradigm shift for natural product discovery and plant-microbe interactions.

Natural product reports
Covering: up to December 2024Microbial metabolic pathways, including those of endophytic fungi, offer significant potential for synthesizing secondary metabolites, regardless of their ecological niche. These pathways can be modulated at the molecular...

How generative Artificial Intelligence can transform drug discovery?

European journal of medicinal chemistry
Generative Artificial Intelligence (Generative AI) is transforming drug discovery by enabling advanced analysis of complex biological and chemical data. This review explores key Generative AI models, including Generative Adversarial Networks (GANs), ...

Machine learning-driven discovery of antimicrobial peptides targeting the GAPDH-TPI protein-protein interaction in Schistosoma mansoni for novel antischistosomal therapeutics.

Computational biology and chemistry
Schistosomiasis, caused by Schistosoma mansoni, remains a significant public health burden, particularly in endemic regions with limited access to effective treatment. The emergence of resistance to praziquantel necessitates the urgent discovery of n...