Computational Drug Discovery Methods

A Deep Learning-based Method for Drug Molecule Representation and Property Prediction

DrugDL is a deep learning framework that improves drug molecule representation and multi-task prediction, outperforming existing methods in cold-start scenarios and aiding drug discovery.

Leveraging AlphaFold2 structural space exploration for generating drug target structures in structure-based virtual screening

The paper improves structure-based virtual screening by modifying AlphaFold2-predicted protein structures to better capture ligand-induced conformations, enhancing screening accuracy.

Fragment-based development of small molecule inhibitors targeting Mycobacterium tuberculosis cholesterol metabolism

This paper develops small molecule inhibitors targeting Mtb cholesterol oxidases CYP125 and CYP142, showing potent antimicrobial activity against drug-susceptible and MDR-TB strains.

Improving ADMET prediction with descriptor augmentation of Mol2Vec embeddings

This paper improves ADMET prediction by augmenting Mol2Vec embeddings with classical descriptors and feature selection, achieving top results on 10 of 16 TDC benchmarks.

MGMG: Cell Morphology-Guided Molecule Generation for Drug Discovery

MGMG generates bioactivity-aware molecules using cell morphology and textual descriptions without target information, achieving comparable binding affinities to reference compounds.

SCOPE: Revealing Hidden Mechanisms in Phenotypic Screens Through Target and Pathway Enrichment

SCOPE identifies molecular mechanisms in phenotypic screens by linking compound data to pathways and targets, revealing calcium signaling and HRH1 as key in exodosis regulation.

Structure-Based Design of Small-Molecule Inhibitors of Human Interleukin-6

The paper designs small-molecule inhibitors of hIL-6 using computational screening and validates one compound's inhibitory effect on IL6-induced STAT3 activity.

Discovery of Novel Inhibitors of HMG-CoA Reductase using Bioactive Compounds isolated from Cochlospermum Species through Computational Methods

This study identifies 10 phytochemicals from Cochlospermum species as potential HMGR inhibitors with binding affinities comparable to statins.

In Silico Evaluation and Therapeutic Targeting of LVDD9B Protein for WSSV Inhibition: Molecular and Ecological Insights for Aquaculture Solutions

The paper evaluates the structural dynamics and interactions of LVDD9B protein with WSSV VP26 and identifies potential therapeutic compounds for antiviral therapy in aquaculture.

Interdisciplinary Study on Drug-Induced-Phospholipidosis of Repurposing Libraries through Machine Learning and Experimental Evaluation in Different Cell Lines

The paper develops a machine learning model to predict drug-induced phospholipidosis using a large dataset of repurposed drugs tested across multiple cell lines.

Utilizing data imbalance to enhance compound-protein interaction prediction models

This paper introduces FilmCPI, a sequence-based model that leverages data imbalance to improve compound-protein interaction prediction, showing better performance and generalization across datasets.

DREAM-GNN: Dual-route embedding-aware graph neural networks for drug repositioning

DREAM-GNN improves drug repositioning by integrating heterogeneous biomedical data through a dual-route graph neural network framework, outperforming existing methods in recovering repositioning candidates.

Network Pharmacognosy of Galphimia glauca: Mapping the Molecular Landscape of a Traditional Mexican Medicinal Plant

This study uses network pharmacognosy to analyze the molecular interactions of Galphimia glauca bioactive compounds with human proteins, identifying key targets and pathways involved in its therapeutic effects.

Understanding the Sources of Performance in Deep Drug Response Models Reveals Insights and Improvements

The paper evaluates deep learning models for drug response prediction, finds transcriptomics data drives performance, and introduces new models that learn chemical features.

Top-DTI: Integrating Topological Deep Learning and Large Language Models for Drug Target Interaction Prediction

Top-DTI predicts drug-target interactions by combining topological features and large language model embeddings, outperforming existing methods on benchmark datasets.

Pharmacokinetic-Pharmacodynamic Trade-offs in SARS-CoV-2 Main Protease Inhibitors Unveiled through Machine Learning and Molecular Dynamics Simulations

SVM and logistic regression classifiers of Mpro inhibitor potency reach test ROC AUC 0.86 and 0.83; potency favours hydrophilic compounds, trading off against drug-likeness.

Unlocking Antiviral Potentials of Traditional Plants: A Multi-Method Computational Study against Human Metapneumovirus (HMPV)

This study identifies antiviral compounds from traditional plants against HMPV using computational methods, finding Glycyrrhizin and Withaferin A as promising candidates with strong binding and pharmacokinetic properties.

Quantum analysis of protein-ligand binding by integrating structural resolution, sequence homology, and ligand properties

This paper predicts protein-ligand binding affinity using classical and quantum-enhanced models, finding that hybrid quantum-classical models achieve high accuracy on curated datasets.

Predicting Affinity Through Homology (PATH): Interpretable Binding Affinity Prediction with Persistent Homology

The paper introduces PATH, an interpretable algorithm for predicting protein-ligand binding affinity using persistent homology, achieving comparable performance with reduced computational complexity.

Design and Optimization of Multifunctional Peptide Candidates for Cosmeceutical Applications: Combining Anti-Inflammatory and Collagen-Boosting Properties

This study designs multifunctional peptides targeting collagen remodelling, anti-inflammation, and ECM stability using computational methods, identifying candidates with potential dual or pan-target applications.

Kinase-Inhibitor Binding Affinity Prediction with Pretrained Graph Encoder and Language Model

This paper introduces Kinhibit, a framework that uses pre-trained molecular encoders and protein language models to predict kinase-inhibitor binding affinity, achieving high accuracy on MAPK pathway kinases and a large kinase dataset.

DrugTar Improves Druggability Prediction by Integrating Large Language Models and Gene Ontologies

DrugTar improves druggability prediction by integrating sequence embeddings and gene ontologies, achieving high performance in target discovery.

RNA-ligand interaction scoring via data perturbation and augmentation modeling

RNAsmol is a sequence-based deep learning framework that predicts RNA-small molecule interactions using data perturbation and augmentation, achieving improved performance over existing methods.

MFPLI: A Computational Framework for Assessing Biological Authenticity of Protein-Ligand Interactions Using Molecular Fingerprints and Structural Features

MFPLI evaluates protein-ligand binding authenticity using deep learning with physicochemical and structural features, achieving high accuracy in distinguishing native conformations from docking poses.

Accelerating Drug Repurposing with AI: The Role of Large Language Models in Hypothesis Validation

This study evaluates four large language models for validating drug repurposing hypotheses using structured prompts and benchmark cases, finding GPT-4o and DeepSeek most reliable with improved accuracy and precision.

Ensemble docking for intrinsically disordered proteins

This study introduces ensemble docking methods to predict small molecule binding affinities and dynamics for intrinsically disordered proteins, showing agreement with experimental data.

Benchmarking Docking Tools on Experimental and Artificial Intelligence-Predicted Protein Structures

This study benchmarks docking tools on experimental and AI-predicted protein structures to assess their accuracy in simulating protein-ligand interactions.

Combining crystallographic fragment screening, algorithmic merging and digitized chemical synthesis efficiently yields scaffolds that target diverse NCS-1 protein-protein interactions implicated in neurological disorders

This paper combines crystallographic fragment screening, algorithmic merging, and automated synthesis to generate scaffolds targeting NCS-1 PPIs involved in neurological disorders.

Prioritization of Potential Drugs Through Pathway-Based Drug Repurposing and Network Proximity Analysis

This paper prioritizes potential drugs for repurposing by analyzing pathway-disease-drug relationships and network proximity using transcriptomic and PPI data.

SAIR: Enabling deep learning for protein-ligand lnteractions with a synthetic structural dataset

The paper introduces SAIR, a synthetic dataset of protein-ligand structures with activity data, to improve deep learning models for binding affinity prediction.