AIMC Topic: Molecular Docking Simulation

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Unveiling key drivers of hepatocellular carcinoma: a synergistic approach with network pharmacology, machine learning-driven ligand discovery and dynamic simulations.

SAR and QSAR in environmental research
Hepatocellular carcinoma (HCC) ranks fourth in cancer-related mortality worldwide. This study aims to uncover the genes and pathways involved in HCC through network pharmacology (NP) and to discover potential drugs via machine learning (ML)-based lig...

Deep learning-based discovery of compounds for blood pressure lowering effects.

Scientific reports
The hypotensive side effects caused by drugs during their use have been a vexing issue. Recent studies have found that deep learning can effectively predict the biological activity of compounds by mining patterns and rules in the data, providing a po...

In silico design of dehydrophenylalanine containing peptide activators of glucokinase using pharmacophore modelling, molecular dynamics and machine learning: implications in type 2 diabetes.

Journal of computer-aided molecular design
Diabetes represents a significant global health challenge associated with substantial healthcare costs and therapeutic complexities. Current diabetes therapies often entail adverse effects, necessitating the exploration of novel agents. Glucokinase (...

LC-MS profiling and cytotoxic activity of Angiopteris helferiana against HepG2 cell line: Molecular insight to investigate anticancer agent.

PloS one
Liver cancer is one of the most prevalent malignant diseases in humans and the second leading cause of cancer-related mortality globally. Angiopteris helferiana was mentioned as a possible anticancer herb according to ethnomedicinal applications. How...

A machine learning-based immune response signature to facilitate prognosis prediction in patients with endometrial cancer.

Scientific reports
Endometrial cancer is the most prevalent form of gynecologic malignancy, with a significant surge in incidence among youngsters. Although the advent of the immunotherapy era has profoundly improved patient outcomes, not all patients benefit from immu...

Evaluations of the Perturbation Resistance of the Deep-Learning-Based Ligand Conformation Optimization Algorithm.

Journal of chemical information and modeling
In recent years, the deep learning (DL) technique has rapidly developed and shown great success in scoring the protein-ligand binding affinities. The protein-ligand conformation optimization based on DL-derived scoring functions holds broad applicati...

HDAC3_VS_assistant: cheminformatics-driven discovery of histone deacetylase 3 inhibitors.

Molecular diversity
Histone deacetylase 3 (HDAC3) inhibitors keep significant therapeutic promise for treating oncological, neurodegenerative, and inflammatory diseases. In this work, we developed robust QSAR regression models for HDAC3 inhibitory activity and acute tox...

Artificial intelligence-enabled discovery of a RIPK3 inhibitor with neuroprotective effects in an acute glaucoma mouse model.

Chinese medical journal
BACKGROUND: Retinal ganglion cell (RGC) death caused by acute ocular hypertension is an important characteristic of acute glaucoma. Receptor-interacting protein kinase 3 (RIPK3) that mediates necroptosis is a potential therapeutic target for RGC deat...

Enhanced prediction of beta-secretase inhibitory compounds with mol2vec technique and machine learning algorithms.

SAR and QSAR in environmental research
A comprehensive computational strategy that combined QSAR modelling, molecular docking, and ADMET analysis was used to discover potential inhibitors for β-secretase 1 (BACE-1). A dataset of 1,138 compounds with established BACE-1 inhibitory activitie...