AIMC Topic: Molecular Docking Simulation

Clear Filters Showing 381 to 390 of 854 articles

A suite of designed protein cages using machine learning and protein fragment-based protocols.

Structure (London, England : 1993)
Designed protein cages and related materials provide unique opportunities for applications in biotechnology and medicine, but their creation remains challenging. Here, we apply computational approaches to design a suite of tetrahedrally symmetric, se...

Deep Learning-Based construction of a Drug-Like compound database and its application in virtual screening of HsDHODH inhibitors.

Methods (San Diego, Calif.)
The process of virtual screening relies heavily on the databases, but it is disadvantageous to conduct virtual screening based on commercial databases with patent-protected compounds, high compound toxicity and side effects. Therefore, this paper uti...

Drug Repositioning via Graph Neural Networks: Identifying Novel JAK2 Inhibitors from FDA-Approved Drugs through Molecular Docking and Biological Validation.

Molecules (Basel, Switzerland)
The increasing utilization of artificial intelligence algorithms in drug development has proven to be highly efficient and effective. One area where deep learning-based approaches have made significant contributions is in drug repositioning, enabling...

In-depth discovery and taste presentation mechanism studies on umami peptides derived from fermented sea bass based on peptidomics and machine learning.

Food chemistry
Umami peptides originating from fermented sea bass impart a distinctive flavor to food. Nevertheless, large-scale and rapid screening for umami peptides using conventional techniques is challenging because of problems such as prolonged duration and c...

Computational discovery of novel FYN kinase inhibitors: a cheminformatics and machine learning-driven approach to targeted cancer and neurodegenerative therapy.

Molecular diversity
In this study, we explored the potential of novel inhibitors for FYN kinase, a critical target in cancer and neurodegenerative disorders, by integrating advanced cheminformatics, machine learning, and molecular simulation techniques. Our approach inv...

Integrating network pharmacology, molecular docking and simulation approaches with machine learning reveals the multi-target pharmacological mechanism of against diabetic nephropathy.

Journal of biomolecular structure & dynamics
Diabetic nephropathy (DN) is one of the most feared complications of diabetes and key cause of end-stage renal disease (ESRD). has been widely used to treat diabetic complications, but exact molecular mechanism is yet to be discovered. Data on activ...

Machine learning assisted methods for the identification of low toxicity inhibitors of Enoyl-Acyl Carrier Protein Reductase (InhA).

Computational biology and chemistry
Tuberculosis (TB) is one of the life-threatening infectious diseases with prehistoric origins and occurs in almost all habitable parts of the world. TB mainly affects the lungs, and its etiological agent is Mycobacterium tuberculosis (Mtb). In 2022, ...

Investigation of bacterial DNA gyrase Inhibitor classification models and structural requirements utilizing multiple machine learning methods.

Molecular diversity
Infections from multidrug-resistant (MDR) bacteria have emerged as a paramount global health concern, and the therapeutic effectiveness of current treatments is swiftly diminishing. An urgent need exists to explore innovative strategies for counterin...

Identification of characteristic genes and herbal compounds for the treatment of psoriasis based on machine learning and molecular dynamics simulation.

Journal of biomolecular structure & dynamics
Psoriasis brings economic and mental burdens to patients, the exact etiology and pathogenesis of psoriasis are still unclear. Compounds of herbal medicine have the potential for psoriasis treatment. This study aims to explore the characteristic genes...

Engineering novel scaffolds for specific HDAC11 inhibitors against metabolic diseases exploiting deep learning, virtual screening, and molecular dynamics simulations.

International journal of biological macromolecules
The prevalence of metabolic diseases is increasing at a frightening rate year by year. The burgeoning development of deep learning enables drug design to be more efficient, selective, and structurally novel. The critical relevance of Histone deacetyl...