AIMC Topic: Deep Learning

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Deep Learning-Guided Discovery of Celestolide as a Natural Allosteric Inhibitor Targeting CYP51 and Its Application in Strawberry Preservation.

Journal of agricultural and food chemistry
Most CYP51 inhibitors act competitively and are prone to resistance, whereas allosteric inhibitors hold promise but are difficult to develop. In this study, we employed the neural relational inference framework alongside the Gaussian network-based de...

Deep learning generalization study on optical coherence tomography image denoising.

Physics in medicine and biology
Noise is a key factor determining imaging quality for optical coherence tomography (OCT). Although deep learning has emerged as an effective denoising method, its generalization capability remains limited, especially when test noise levels deviate fr...

Photon-counting micro-CT scanner for deep learning-enabled small animal perfusion imaging.

Physics in medicine and biology
In this work, we introduce a benchtop, turn-table photon-counting (PC) micro-computed tomography (CT) scanner and highlight its application for dynamic small animal perfusion imaging.Built on recently published hardware, the system now features a CdT...

Recent advancements in feature extraction and classification based bone cancer detection - a systematic review.

Biomedical physics & engineering express
Cancer is a deadly disease that occurs due to the uncontrolled growth of abnormal cells. Bone cancer is the third most occurring disease; approximately 10,000 patients suffer from bone cancer in India annually. It can lead to death if not diagnosed i...

AIoptamer: Artificial Intelligence-Driven Aptamer Optimization Pipeline for Targeted Therapeutics in Healthcare.

Molecular pharmaceutics
Aptamers are short, single-stranded DNA or RNA molecules known for their high specificity and affinity toward target biomolecules, making them powerful tools in drug discovery, diagnostics, and biosensing. However, conventional aptamer selection meth...

ASOptimizer: optimizing chemical diversity of antisense oligonucleotides through deep learning.

Nucleic acids research
Antisense oligonucleotides (ASOs) are a promising class of gene therapies that can modulate the gene expression. However, designing ASOs manually is resource-intensive and time-consuming. To address this, we introduce a user-friendly web server for A...

DEMO-EMol: modeling protein-nucleic acid complex structures from cryo-EM maps by coupling chain assembly with map segmentation.

Nucleic acids research
Atomic structure modeling is a crucial step in determining the structures of protein complexes using cryo-electron microscopy (cryo-EM). This work introduces DEMO-EMol, an improved server that integrates deep learning-based map segmentation and chain...

InDeepNet: a web platform for predicting functional binding sites in proteins using InDeep.

Nucleic acids research
Predicting functional binding sites in proteins is crucial for understanding protein-protein interactions (PPIs) and identifying drug targets. While various computational approaches exist, many fail to assess PPI ligandability, which often involves c...

HawkDock version 2: an updated web server to predict and analyze the structures of protein-protein complexes.

Nucleic acids research
Protein-protein interactions (PPIs) are fundamental to cellular functions, yet predicting and analyzing their 3D structures remains a critical and computationally demanding challenge. To address this, the HawkDock web server was developed as an integ...

DeepMolecules: a web server for predicting enzyme and transporter-small molecule interactions.

Nucleic acids research
DeepMolecules is an easily accessible web server for predicting protein-small molecule interactions. It integrates four state-of-the-art models: ESP and SPOT for identifying substrates of enzymes and transporters, respectively, TurNuP for predicting ...