AI Medical Compendium Journal:
Journal of chemical information and modeling

Showing 71 to 80 of 934 articles

Join Persistent Homology (JPH)-Based Machine Learning for Metalloprotein-Ligand Binding Affinity Prediction.

Journal of chemical information and modeling
With the crucial role of metalloproteins in respiration, oxidative stress protection, photosynthesis, and drug metabolism, the design and discovery of drugs that can target metalloproteins are extremely important. Recently, enormous potential has bee...

Multidependency Graph Convolutional Networks and Contrastive Learning for Drug Repositioning.

Journal of chemical information and modeling
The goal of drug repositioning is to expedite the drug development process by finding novel therapeutic applications for approved drugs. Using multifeature learning, different computational drug repositioning techniques have recently been introduced ...

scMDCL: A Deep Collaborative Contrastive Learning Framework for Matched Single-Cell Multiomics Data Clustering.

Journal of chemical information and modeling
Single-cell multiomics clustering integrates multiple omics data to analyze cellular heterogeneity and is crucial for uncovering complex biological processes and disease mechanisms. However, existing matched single-cell multiomics clustering methods ...

MOLGAECL: Molecular Graph Contrastive Learning via Graph Auto-Encoder Pretraining and Fine-Tuning Based on Drug-Drug Interaction Prediction.

Journal of chemical information and modeling
Drug-drug interactions influence drug efficacy and patient prognosis, providing substantial research value. Some existing methods struggle with the challenges posed by sparse networks or lack the capability to integrate data from multiple sources. In...

ASVirus: A Comprehensive Knowledgebase for the Viral Alternative Splicing.

Journal of chemical information and modeling
Viruses are significant human pathogens responsible for pandemic outbreaks and seasonal epidemics. Viral infectious diseases impose a devastating global burden and have a profound impact on public health systems. During viral infections, alternative ...

DeepMVD: A Novel Multiview Dynamic Feature Fusion Model for Accurate Protein Function Prediction.

Journal of chemical information and modeling
Proteins, as the fundamental macromolecules of life, play critical roles in various biological processes. Recent advancements in intelligent protein function prediction methods leverage sequences, structures, and biomedical literature data. Among the...

From NMR to AI: Do We Need H NMR Experimental Spectra to Obtain High-Quality logD Prediction Models?

Journal of chemical information and modeling
This study presents a novel approach to H NMR-based machine learning (ML) models for predicting logD using computer-generated H NMR spectra. Building on our previous work, which integrated experimental H NMR data, this study addresses key limitations...

Enhancing Blood-Brain Barrier Penetration Prediction by Machine Learning-Based Integration of Novel and Existing, In Silico and Experimental Molecular Parameters from a Standardized Database.

Journal of chemical information and modeling
Predicting blood-brain barrier (BBB) penetration is crucial for developing central nervous system (CNS) drugs, representing a significant hurdle in successful clinical phase I studies. One of the most valuable properties for this prediction is the po...

iScore: A ML-Based Scoring Function for De Novo Drug Discovery.

Journal of chemical information and modeling
In the quest for accelerating de novo drug discovery, the development of efficient and accurate scoring functions represents a fundamental challenge. This study introduces iScore, a novel machine learning (ML)-based scoring function designed to predi...

Bidirectional Long Short-Term Memory (BiLSTM) Neural Networks with Conjoint Fingerprints: Application in Predicting Skin-Sensitizing Agents in Natural Compounds.

Journal of chemical information and modeling
Skin sensitization, or allergic contact dermatitis, represents a critical end point in toxicity assessment, with profound implications for drug safety and regulatory decision-making. This study aims to develop a robust deep-learning-based quantitativ...