AIMC Topic: Molecular Docking Simulation

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Residue-Level Multiview Deep Learning for ATP Binding Site Prediction and Applications in Kinase Inhibitors.

Journal of chemical information and modeling
Accurate identification of adenosine triphosphate (ATP) binding sites is crucial for understanding cellular functions and advancing drug discovery, particularly in targeting kinases for cancer treatment. Existing methods face significant challenges d...

ChemNTP: Advanced Prediction of Neurotoxicity Targets for Environmental Chemicals Using a Siamese Neural Network.

Environmental science & technology
Environmental chemicals can enter the human body through various exposure pathways, potentially leading to neurotoxic effects that pose significant health risks. Many such chemicals have been identified as neurotoxic, but the molecular mechanisms und...

Integrating traditional QSAR and read-across-based regression models for predicting potential anti-leishmanial azole compounds.

Molecular diversity
Leishmaniasis, a neglected tropical disease caused by various Leishmania species, poses a significant global health challenge, especially in resource-limited regions. Visceral Leishmaniasis (VL) stands out among its severe manifestations, and current...

Docking-Informed Machine Learning for Kinome-wide Affinity Prediction.

Journal of chemical information and modeling
Kinase inhibitors are an important class of anticancer drugs, with 80 inhibitors clinically approved and >100 in active clinical testing. Most bind competitively in the ATP-binding site, leading to challenges with selectivity for a specific kinase, r...

Natural compounds for Alzheimer's prevention and treatment: Integrating SELFormer-based computational screening with experimental validation.

Computers in biology and medicine
BACKGROUND: This study aimed to develop and apply a novel computational pipeline combining SELFormer, a transformer architecture-based chemical language model, with advanced deep learning techniques to predict natural compounds (NCs) with potential i...

iDCNNPred: an interpretable deep learning model for virtual screening and identification of PI3Ka inhibitors against triple-negative breast cancer.

Molecular diversity
Triple-negative breast cancer (TNBC) lacks estrogen, progesterone, and HER2 expression, accounting for 15-20% of breast cancer cases. It is challenging due to low therapeutic response, heterogeneity, and aggressiveness. The PI3Ka isoform is a promisi...

Effective analysis of thyroid toxicity and mechanisms of acetyltributyl citrate using network toxicology, molecular docking, and machine learning strategies.

Toxicology
The growing prevalence of environmental pollutants has raised concerns about their potential role in thyroid dysfunction and related disorders. Previous research suggests that various chemicals, including plasticizers like acetyl tributyl citrate (AT...

Deep learning based predictive modeling to screen natural compounds against TNF-alpha for the potential management of rheumatoid arthritis: Virtual screening to comprehensive in silico investigation.

PloS one
Rheumatoid arthritis (RA) affects an estimated 0.1% to 2.0% of the world's population, leading to a substantial impact on global health. The adverse effects and toxicity associated with conventional RA treatment pathways underscore the critical need ...

Machine learning-based analysis of programmed cell death types and key genes in intervertebral disc degeneration.

Apoptosis : an international journal on programmed cell death
Intervertebral disc degeneration (IVDD) is intricately associated with various forms of programmed cell death (PCD). Identifying key PCD types and associated genes is essential for understanding the molecular mechanisms underlying IVDD and discoverin...