AIMC Topic: Molecular Docking Simulation

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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...

Screening Targets and Therapeutic Drugs for Alzheimer's Disease Based on Deep Learning Model and Molecular Docking.

Journal of Alzheimer's disease : JAD
BACKGROUND: Alzheimer's disease (AD) is a neurodegenerative disorder caused by a complex interplay of various factors. However, a satisfactory cure for AD remains elusive. Pharmacological interventions based on drug targets are considered the most co...

Assessment of Protein-Protein Docking Models Using Deep Learning.

Methods in molecular biology (Clifton, N.J.)
Protein-protein interactions are involved in almost all processes in a living cell and determine the biological functions of proteins. To obtain mechanistic understandings of protein-protein interactions, the tertiary structures of protein complexes ...

Machine Learning Methods in Protein-Protein Docking.

Methods in molecular biology (Clifton, N.J.)
An exponential increase in the number of publications that address artificial intelligence (AI) usage in life sciences has been noticed in recent years, while new modeling techniques are constantly being reported. The potential of these methods is va...

In-silico prediction of anti-breast cancer activity of ginger (Zingiber officinale) using machine learning techniques.

Breast disease
INTRODUCTION: Indonesian civilization extensively uses traditional medicine to cure illnesses and preserve health. The lack of knowledge on the security and efficacy of medicinal plants is still a significant concern. Although the precise chemicals r...

Peptidic Compound as DNA Binding Agent: Fragment-based Design, Machine Learning, Molecular Modeling, Synthesis, and DNA Binding Evaluation.

Protein and peptide letters
BACKGROUND: Cancer remains a global burden, with increasing mortality rates. Current cancer treatments involve controlling the transcription of malignant DNA genes, either directly or indirectly. DNA exhibits various structural forms, including the G...

Identifying Hub Genes for Glaucoma based on Bulk RNA Sequencing Data and Multi-machine Learning Models.

Current medicinal chemistry
AIMS: The aims of this study were to determine hub genes in glaucoma through multiple machine learning algorithms.

Heterocyclic-Based Analogues against Sarcine-Ricin Loop RNA from : Molecular Docking Study and Machine Learning Classifiers.

Medicinal chemistry (Shariqah (United Arab Emirates))
BACKGROUND: Heterocyclic-based drugs have strong bioactivities, are active pharmacophores, and are used to design several antibacterial drugs. Due to the diverse biodynamic properties of well-known heterocyclic cores, such as quinoline, indole, and i...

A Strategy based on Bioinformatics and Machine Learning Algorithms Reveals Potential Mechanisms of Shelian Capsule against Hepatocellular Carcinoma.

Current pharmaceutical design
BACKGROUND: Hepatocellular carcinoma (HCC) is a prevalent and life-threatening form of cancer, with Shelian Capsule (SLC), a traditional Chinese medicine (TCM) formulation, being recommended for clinical treatment. However, the mechanisms underlying ...

Determination of Novel SARS-CoV-2 Inhibitors by Combination of Machine Learning and Molecular Modeling Methods.

Medicinal chemistry (Shariqah (United Arab Emirates))
INTRODUCTION: Within the scope of the project, this study aimed to find novel inhibitors by combining computational methods. In order to design inhibitors, it was aimed to produce molecules similar to the RdRp inhibitor drug Favipiravir by using the ...