Computational Drug Discovery Methods

The paper develops a database of docking results and experimental data to improve benchmarking and chemical space exploration.

This study uses thermodynamic cycles and FEP simulations to predict the effects of resistance mutations on DHFR's competitive inhibition with TMP, revealing structural and dynamical impacts on enzyme function.

This study identifies small molecule inhibitors targeting the MEIG1-PACRG complex using virtual screening, docking, and MD simulations, highlighting distinct binding determinants for each protein.

The paper identifies Posaconazole as a CYP11A1 inhibitor that enhances anti-tumour immunity in a mouse model of lung metastasis.

MolSnapper improves structure-based drug design by conditioning diffusion models with 3D pharmacophores, generating more valid molecules with higher structural and chemical similarity.

The paper assesses similarity between drug targets and human microbiome metaproteomes to identify potential off-target effects and pharmacological promiscuity.

This paper introduces a deep learning framework for antimicrobial discovery that identifies new compounds with antimicrobial potential using pre-trained molecular representations.

This study introduces a framework that predicts protein-ligand binding affinity by folding proteins, determining binding conformations, and using 3D structures, achieving performance comparable to existing docking-free methods.

This study uses deep modeling to predict gain-of-function mutations in the androgen receptor and their drug responses, achieving 80% accuracy in identifying agonist/antagonist relationships.

This study uses computational methods to design novel anti-Ebola agents targeting the virus polymerase.

The paper uses steered MD simulations to screen small molecules targeting MKK3-MYC interactions, identifying compounds with promising binding affinities.

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.

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

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.

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

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

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

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.

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

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

This paper addresses data imbalance in drug response prediction by introducing a multi-objective optimization approach using a custom loss function to improve model performance.

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

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

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

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

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

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.

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

The paper develops interpretable models to predict cancer drug sensitivity using transcriptomics data and enhances predictions with large language models, validated on clinical samples.