Computational Drug Discovery Methods

CASTER-DTA predicts drug-target affinity using equivariant graph neural networks, achieving state-of-the-art performance and generating a resource of binding affinities for all FDA-approved drugs against the human proteome.

AdapToR improves QSAR modeling by enhancing interpretability and efficiency in drug response prediction.

This study identifies potential drug targets in Streptococcus agalactiae serotype V using in-silico subtractive genomics, highlighting two proteins as promising candidates for therapeutic development.

CombDNF predicts disease-specific drug combinations using network-based features and clinically validated data, outperforming existing methods by 84% in AUPR across four diseases.

SCORCH2 improves virtual screening performance and interpretability by using interaction features, showing better accuracy and transferability than its predecessor.

This study designs non-covalent CDK7 inhibitors using computational methods to improve selectivity and safety compared to THZ1.

This study uses a Transformer model to design novel succinate dehydrogenase inhibitors against agricultural fungi, resulting in potent antifungal compounds through virtual screening and optimization.

DockFormer predicts protein-small molecule binding affinity and structure using a flexible docking approach, outperforming existing methods on benchmark datasets.

This paper predicts novel drug-drug interactions (DDIs) in women's reproductive health, identifying 2,991 new interactions and their clinical implications.

This study uses machine learning and molecular docking to identify 14 anti-infective drugs that may be repurposed for onchocerciasis treatment, showing binding affinities similar to existing drugs.

This paper combines geometric pocket detection with large language models to prioritize protein binding pockets for drug discovery, using a curated dataset and automated pipeline.

The paper screens a compound library to identify leukemia-selective chemotypes using single-cell profiling and finds that certain rocaglate derivatives selectively induce DNA damage in leukemia cells.

This paper introduces MotifGen, a deep learning model that predicts molecular interaction motifs from receptor structures, enabling peptide binder design and small-molecule binding site prediction.

This study evaluates a hybrid model combining protein language models and graph neural networks for predicting protein-ligand binding sites, showing that structural information improves performance but complex pLMs can achieve good results without it.

This study identifies potential antiviral drug candidates against MPXV DNA polymerase and A42R protein using in-silico methods, highlighting compounds with high docking scores and stable ligand-protein complexes.

This paper proposes a framework that transfers knowledge from multi-modal genomic data to gene expression-only models to improve drug response prediction.

This study enhances a computational protocol for discovering covalent inhibitors of AdoMetDC by integrating quantum chemistry calculations, achieving a 70.6% hit rate in screening 17 compounds.

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

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

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

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

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

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.

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.

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

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.

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.

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

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.