Revolutionizing Drug Discovery: Harnessing Machine Learning Algorithms DOI Creative Commons
Tushar Khinvasara

International Journal For Multidisciplinary Research, Journal Year: 2024, Volume and Issue: 6(2)

Published: April 11, 2024

Drug discovery is a crucial element of biomedical research, with the goal finding and creating new medical treatments for variety illnesses. Yet, conventional process drugs frequently impeded by its intrinsic difficulties, such as expensive expenses, long durations, poor success rates in trials patients. Recently, incorporation machine learning (ML) algorithms has become revolutionary method to streamline improve different phases drug discovery. This summary offers glimpse into rapidly growing area using algorithms, emphasizing potential transform developing treatments. The usual discovering involves various stages identifying target, lead compounds, conducting preclinical tests, undergoing clinical trials, obtaining regulatory approval. All these require lot labor, time, resources, leading high attrition limited turning compounds approved therapies. Nevertheless, researchers can enhance speed up parts ML algorithms. use data aid utilizing computational models examine large quantities biological, chemical, data. These learn from types data, genomic chemical structures, protein interactions, outcomes, discover hidden patterns, find targets drugs, forecast effectiveness safety Moreover, allow investigation intricate connections between molecular structures biological effects, making it easier create improved candidates better specificity. Important uses pharmaceutical research involve confirming targets, screening improving leads, repurposing tailoring individuals. Commonly used classification regression tasks, supervised like support vector machines random forests predict compound activity, toxicity, pharmacokinetic properties. Clustering dimensionality reduction techniques utilized unsupervised help analyze vast datasets drug-target interactions. Advanced abilities analyzing virtual screening, designing are provided deep convolutional neural networks recurrent networks. Multiple case studies demonstrate how significantly impact Collaboration among academia, industry, institutions resulted creation ML-based methods development, categorizing there challenges accompanying widespread In healthcare, address ethical considerations, hurdles, privacy concerns ensure responsible transforming therapeutic development immense Through data-driven methods, treatments, ultimately results Ongoing innovation, teamwork, cross-disciplinary fully leverage revolutionizing precision medicine.

Language: Английский

Antibody Immunotherapies for Personalized Opioid Addiction Treatment DOI
Eric H. Rosenn,

Miriam Korlansky,

Shahin Benyaminpour

et al.

Journal of Pharmacology and Experimental Therapeutics, Journal Year: 2025, Volume and Issue: 392(4), P. 103522 - 103522

Published: Feb. 25, 2025

Approved therapies for managing opioid addiction involve intensive treatment regimens which remain both costly and ineffective. As pharmaceutical interventions have achieved variable success treating substance use disorders (SUD), alternative therapeutics must be considered. Antidrug antibodies induced by vaccination or introduced as monoclonal antibody formulations can neutralize destroy opioids in circulation before they reach their central nervous system targets act enzymes to deactivate receptors, preventing the physiologic psychoactive effects of substance. A lack "reward" those suffering from SUD has been shown result cessation promote long-term abstinence. Decreased production costs advent novel gene that stimulate vivo renewed interest this strategy. Furthermore, advances understanding immunopathogenesis revealed distinct mechanisms neuroimmune dysregulation underlying disorder. Beyond assisting with drug use, could treat reverse pathophysiologic hallmarks contribute cause chronic cognitive defects resulting use. In review, we synthesize key current literature regarding efficacy immunotherapies SUD. We will explore neuropharmacology these treatments relating evidence studies on counteract various behaviors drawing parallels similar immunopathology observed neurodegenerative disorders. Finally, discuss implications immunization technologies application computational methods developing personalized treatments. SIGNIFICANCE STATEMENT: Significant new contributing our recently emerged leading a paradigm shift concerning role immunology neuropathogenesis Concurrently, immunotherapeutic such advanced capabilities applications take advantage principles. This article reviews antibody-based being studied highlights directions further research may management

Language: Английский

Citations

0
Harnessing machine learning for rational drug design DOI

Sandhya Chaudhary,

Kalpana Pravin Rahate,

Sachin Mishra

et al.

Advances in pharmacology, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 1, 2025

Language: Английский

Citations

0
Advancements in nanobody generation: Integrating conventional, in silico, and machine learning approaches DOI Creative Commons

D. Jagadeeswara Reddy,

Girijasankar Guntuku, Mary Sulakshana Palla

et al.

Biotechnology and Bioengineering, Journal Year: 2024, Volume and Issue: 121(11), P. 3375 - 3388

Published: July 25, 2024

Nanobodies, derived from camelids and sharks, offer compact, single-variable heavy-chain antibodies with diverse biomedical potential. This review explores their generation methods, including display techniques on phages, yeast, or bacteria, computational methodologies. Integrating experimental approaches enhances understanding of nanobody structure function. Future trends involve leveraging next-generation sequencing, machine learning, artificial intelligence for efficient candidate selection predictive modeling. The convergence traditional methods promises revolutionary advancements in precision applications such as targeted drug delivery diagnostics. Embracing these technologies accelerates development, driving transformative breakthroughs biomedicine paving the way medicine innovation.

Language: Английский

Citations

3
Revolutionizing Drug Discovery: Harnessing Machine Learning Algorithms DOI Creative Commons
Tushar Khinvasara

International Journal For Multidisciplinary Research, Journal Year: 2024, Volume and Issue: 6(2)

Published: April 11, 2024

Drug discovery is a crucial element of biomedical research, with the goal finding and creating new medical treatments for variety illnesses. Yet, conventional process drugs frequently impeded by its intrinsic difficulties, such as expensive expenses, long durations, poor success rates in trials patients. Recently, incorporation machine learning (ML) algorithms has become revolutionary method to streamline improve different phases drug discovery. This summary offers glimpse into rapidly growing area using algorithms, emphasizing potential transform developing treatments. The usual discovering involves various stages identifying target, lead compounds, conducting preclinical tests, undergoing clinical trials, obtaining regulatory approval. All these require lot labor, time, resources, leading high attrition limited turning compounds approved therapies. Nevertheless, researchers can enhance speed up parts ML algorithms. use data aid utilizing computational models examine large quantities biological, chemical, data. These learn from types data, genomic chemical structures, protein interactions, outcomes, discover hidden patterns, find targets drugs, forecast effectiveness safety Moreover, allow investigation intricate connections between molecular structures biological effects, making it easier create improved candidates better specificity. Important uses pharmaceutical research involve confirming targets, screening improving leads, repurposing tailoring individuals. Commonly used classification regression tasks, supervised like support vector machines random forests predict compound activity, toxicity, pharmacokinetic properties. Clustering dimensionality reduction techniques utilized unsupervised help analyze vast datasets drug-target interactions. Advanced abilities analyzing virtual screening, designing are provided deep convolutional neural networks recurrent networks. Multiple case studies demonstrate how significantly impact Collaboration among academia, industry, institutions resulted creation ML-based methods development, categorizing there challenges accompanying widespread In healthcare, address ethical considerations, hurdles, privacy concerns ensure responsible transforming therapeutic development immense Through data-driven methods, treatments, ultimately results Ongoing innovation, teamwork, cross-disciplinary fully leverage revolutionizing precision medicine.

Language: Английский

Citations

0