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Drug Design

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Accelerating antimicrobial peptide design: Leveraging deep learning for rapid discovery.

PloS one
Antimicrobial peptides (AMPs) are excellent at fighting many different infections. This demonstrates how important it is to make new AMPs that are even better at eliminating infections. The fundamental transformation in a variety of scientific discip...

Estimating protein-ligand interactions with geometric deep learning and mixture density models.

Journal of biosciences
Understanding the interactions between a ligand and its molecular target is crucial in guiding the optimization of molecules for any drug design workflow. Multiple experimental and computational methods have been developed to better understand these...

Antiviral Peptide-Generative Pre-Trained Transformer (AVP-GPT): A Deep Learning-Powered Model for Antiviral Peptide Design with High-Throughput Discovery and Exceptional Potency.

Viruses
Traditional antiviral peptide (AVP) discovery is a time-consuming and expensive process. This study introduces AVP-GPT, a novel deep learning method utilizing transformer-based language models and multimodal architectures specifically designed for AV...

Rational design of potent phosphopeptide binders to endocrine Snk PBD domain by integrating machine learning optimization, molecular dynamics simulation, binding energetics rescoring, and in vitro affinity assay.

European biophysics journal : EBJ
Human Snk is an evolutionarily conserved serine/threonine kinase essential for the maintenance of endocrine stability. The protein consists of a N-terminal catalytic domain and a C-terminal polo-box domain (PBD) that determines subcellular localizati...

AI-directed formulation strategy design initiates rational drug development.

Journal of controlled release : official journal of the Controlled Release Society
Rational drug development would be impossible without selecting the appropriate formulation route. However, pharmaceutical scientists often rely on limited personal experiences to perform trial-and-error tests on diverse formulation strategies. Such ...

Strategies for the design of biomimetic cell-penetrating peptides using AI-driven in silico tools for drug delivery.

Biomaterials advances
Cell-penetrating peptides (CPP) have gained rapid attention over the last 25 years; this is attributed to their versatility, customisation, and 'Trojan horse' delivery that evades the immune system. However, the current CPP rational design process is...

Ligand identification in CryoEM and X-ray maps using deep learning.

Bioinformatics (Oxford, England)
MOTIVATION: Accurately identifying ligands plays a crucial role in the process of structure-guided drug design. Based on density maps from X-ray diffraction or cryogenic-sample electron microscopy (cryoEM), scientists verify whether small-molecule li...

Generative AI: driving productivity and scientific breakthroughs in pharmaceutical R&D.

Drug discovery today
The rapid advancement of generative artificial intelligence (AI) is reshaping pharmaceutical research and development (R&D), offering opportunities across drug discovery and development. Generative AI (GenAI) enhances productivity by enabling virtual...

Combined usage of ligand- and structure-based virtual screening in the artificial intelligence era.

European journal of medicinal chemistry
Drug design has always been pursuing techniques with time- and cost-benefits. Virtual screening, generally classified as ligand-based (LBVS) and structure-based (SBVS) approaches, could identify active compounds in the large chemical library to reduc...

Toward Dose Prediction at Point of Design.

Journal of medicinal chemistry
Human dose prediction (HDP) is a useful tool for compound optimization in preclinical drug discovery. We describe here our exclusively in silico HDP strategy to triage compound designs for synthesis and experimental profiling. Our goal is a model tha...