Computational Drug Discovery Methods

Bioinformatics Approach on Hypothetical Protein from Clostridioides difficile: Structural, Functional and Molecular Docking Analysis

The paper analyzes a hypothetical protein from C. difficile to identify potential ligands for therapeutic development through physicochemical properties, structural prediction, and molecular docking studies.

Directed Chemical Evolution via Navigating Molecular Encoding Space

This paper introduces Ouroboros, a framework that improves drug discovery by integrating molecular representation learning with generative modeling, outperforming traditional methods in multiple tasks.

Discovery of Natural MCL1 Inhibitors using Pharmacophore modelling, QSAR, Docking, ADMET, Molecular Dynamics, and DFT Analysis

The paper identifies two potential MCL1 inhibitors through computational methods including pharmacophore modeling, QSAR, docking, and DFT analysis, showing favorable ADMET properties and binding stability.

A hybrid machine learning framework leveraging biophysicochemical insights for scalable discovery of protein-ligand interactions

The paper introduces a hybrid machine learning framework that improves protein-ligand interaction prediction by combining biophysicochemical insights with large-scale representations, achieving faster and more accurate virtual screening.

Drug Repurposing through Meta-path Reconstruction using Relationship Embedding

The paper introduces PREDR, a model that predicts drug-disease relationships by reconstructing meta-paths from heterogeneous biological knowledge graphs, outperforming existing methods in drug repurposing.

The Specification Game: Rethinking the Evaluation of DrugResponse Prediction for Precision Oncology

This paper identifies flaws in current Drug Response Prediction evaluation methods and proposes new strategies to improve their reliability and generalization.

Context-Aware Hierarchical Fusion for Drug Relational Learning

This study introduces a context-aware hierarchical fusion model for drug relational learning, showing improved performance in predicting drug-drug interactions and side effects across different contexts.

Benchmarking HelixFold3-Predicted Holo Structures for Relative Free Energy Perturbation Calculations

This study benchmarks HelixFold3's ability to predict holo structures for free energy perturbation calculations, showing improved performance over AlphaFold2 and existing methods.

Harnessing Uniform Design to Enhance AI-Driven Predictions of Physicochemical Properties of Short Peptides

This paper uses uniform design with AI to predict physicochemical properties of short peptides, identifying key factors affecting aggregation and providing predictive models and datasets.

Fine-Tuned Deep Transfer Learning Models for Large Screenings of Safer Drugs Targeting Class A G Protein-Coupled Receptors

The paper develops fine-tuned deep learning models for predicting safer drugs targeting class A GPCRs by leveraging transfer learning and sequence-based analysis.

Prospective evaluation of structure-based simulations reveal their ability to predict the impact of kinase mutations on inhibitor binding

The paper evaluates structure-based methods to predict how kinase mutations affect inhibitor binding, finding they accurately classify mutations as resistant or sensitizing.

Expanding the Synthome: Generating and integrating novel reactions into retrosynthesis tools.

This paper introduces a tool that integrates novel chemical reactions into retrosynthesis tools, improving performance in 70% of cases and reducing synthesis costs by 20%.

High-Throughput Empirical and Virtual Screening to Discover Novel Inhibitors of Polyploid Giant Cancer Cells in Breast Cancer

This paper develops a high-throughput screening method to identify drugs targeting polyploid giant cancer cells in breast cancer, combining empirical assays with a machine learning model for virtual screening.

Encoding Drug-Target-Pathway-Disease profiles for Drug-Cancer Association Prediction using Graph Transformer-Convolution Networks

The paper proposes a Graph Transformer-Convolution Network (GTCN) to predict drug-cancer associations by integrating heterogeneous biological data and enhancing interpretability.

In silico PK predictions in Drug Discovery: Benchmarking of Strategies to Integrate Machine Learning with Empiric and Mechanistic PK modelling

This study benchmarks ML-integrated approaches for predicting PK profiles in rats, showing comparable accuracy across multiple methods for prioritizing drug candidates.

A molecular representation to identify isofunctional molecules

The paper introduces IFPP, a molecular representation for identifying isofunctional molecules, and proposes Metric Learning to improve scalability for large libraries.

Targeting D-Ribose-Binding Proteins in Brucella melitensis: A Novel Frontier Against Antibiotic Resistance

This paper identifies D-Talopyranose as a potential drug candidate targeting Brucella melitensis DBP to combat antibiotic resistance through computational methods.

GPCRVS - AI-driven decision support system for GPCR virtual screening

GPCRVS predicts compound activity against GPCRs using machine learning, validated on ChEMBL datasets for secretin-like and chemokine receptors.

Smart Distributed Data Factory: Volunteer Computing Platform for Active Learning-Driven Molecular Data Acquisition

The paper introduces SDDF, a platform using volunteer computing and active learning to efficiently generate molecular conformation datasets for drug discovery.

SensitiveCancerGPT: Leveraging Generative Large Language Model on Structured Omics Data to Optimize Drug Sensitivity Prediction

This paper evaluates the use of GPT for drug sensitivity prediction using pharmacogenomics data, showing improved performance through prompt engineering and fine-tuning.

BaCNet: Deep Learning Accelerates Novel Antibiotic Discovery Against Resistant Pathogens

BaCNet uses deep learning to predict antibiotic-target interactions and discover novel compounds against drug-resistant pathogens.

Scaling up drug combination surface prediction

This paper develops an improved machine learning model for predicting drug combination dose-response surfaces, showing better performance than scalar-valued synergy score methods.

DrugDiff - small molecule diffusion model with flexible guidance towards molecular properties

DrugDiff generates novel small molecules with desired properties using a diffusion model guided by predictors.

Machine Learning-Based Drug Response Prediction Identifies Novel Therapeutic Candidates for Colorectal Cancer Cell Line KM-12

The paper uses machine learning to predict drug responses in colorectal cancer cells, identifying novel compounds with cytotoxic or cytostatic effects.

Predicting antimicrobial class specificity of small molecules using machine learning

The paper develops ML models to predict antimicrobial activity of small molecules using a knowledge graph, achieving 75.9% accuracy in identifying active compounds.

MolGene-E: Inverse Molecular Design to Modulate Single Cell Transcriptomics

MolGene-E generates high-quality drug molecules from single-cell transcriptomics data, outperforming baselines in zero-shot molecule generation.

Enhanced identification of small molecules binding to hnRNPA1 via cryptic pockets mapping coupled with X-Ray fragment screening

This study identifies small molecule binders to hnRNPA1 using MD simulations and fragment screening, revealing cryptic pockets and conformational changes.

T-ALPHA: A Hierarchical Transformer-Based Deep Neural Network for Protein-Ligand Binding Affinity Prediction With Uncertainty-Aware Self-Learning for Protein-Specific Alignment

T-ALPHA predicts protein-ligand binding affinity using a hierarchical transformer model and uncertainty-aware self-learning, outperforming existing methods and working with predicted structures.

NRGRank: Coarse-grained structurally-informed ultra-massive virtual screening

NRGRank is a fast, accurate virtual screening tool that outperforms docking methods on some targets and complements them on others, enabling efficient large-scale screening.

AI-Augmented R-Group Exploration in Medicinal Chemistry

The paper develops an enhanced Free-Wilson QSAR model for R-group exploration using atom-centric pharmacophoric features, showing good predictivity across 12 datasets.