Computational Drug Discovery Methods

The paper computationally identifies peptides and acetic-polymers targeting the Listeria monocytogenes prfA virulence regulon using evoluteca generation, docking, and Boltz2 for ligand affinity prediction.

PDDRNet-MH predicts personalized drug responses in breast cancer by integrating multi-omics data and drug information, achieving high accuracy for several FDA-approved drugs.

This study identifies novel marine-derived inhibitors of tetracycline destructase in Acinetobacter Baumannii using computational methods to enhance tetracycline efficacy.

Simpatico improves virtual drug screening by using atomic-level embeddings to rapidly and accurately identify potential ligands for protein targets.

This paper identifies three potential Plasmodium falciparum GSK3β inhibitors through in silico screening of CHEMBL database compounds, with S56 showing the best in silico performance.

PBCNet2.0 predicts protein-ligand binding affinity using a cartesian tensor-based Siamese Neural Network, achieving high accuracy and efficiency compared to physics-based simulations.

SAKE-PP improves PPI model evaluation by predicting interface RMSD without native references, outperforming AF3 on docking decoys and antibody-antigen complexes.

OriGene identifies therapeutic targets by integrating diverse data modalities and outperforms human experts and models in accuracy and robustness.

The paper develops ConMeter, a metabolite prediction model, and uses it to design an orally bioavailable RSK4 inhibitor with improved bioavailability and anti-tumor efficacy against esophageal cancer.

The paper identifies F0F1 ATP synthase subunit C as a potential drug target in multidrug-resistant bacteria using a subtractive genomics approach.

The paper integrates Cell Painting and Thermal Proteome Profiling to infer targets and mechanisms of action for compounds, accurately identifying known MoAs for four out of five tested compounds.

The paper presents an ensemble learning model that integrates chemical and protein features to predict selective kinase inhibitors, achieving 93.6% accuracy on an independent test set.

YuelDesign generates molecules for flexible protein pockets using a diffusion framework, producing drug-like molecules with diverse functional groups and comparable docking energies to native ligands.

KODA uses LLMs and knowledge graphs to identify antimicrobial drug targets in the gut microbiome by analyzing metabolic pathways and microbial interactions.

This study evaluates AlphaFold3-derived models for drug binding affinity ranking, showing they outperform existing methods and require fewer resources.

This paper evaluates drug response prediction models and finds that deep learning models perform similarly to simple baselines, highlighting issues in current model evaluation practices.

This paper evaluates how design choices in chemical language models affect their performance and interpretability, finding that molecular representation and tokenization strategies significantly influence internal representations.

DELTA provides a balanced ligand-target database for AI drug discovery, showing active ligands are larger than inactive ones and similar targets bind similar ligands.

The paper develops ChemBERTaPolyPharm to model polypharmacy side effects using ChemBERTa and PubMed encoders, achieving an F1 score of 0.93.

SyntheMol-RL generates novel and synthesizable antibiotics by optimizing for antibacterial activity and solubility, outperforming prior models and showing in vitro and in vivo efficacy.

Riemann-GNN uses hyperbolic geometry and causal inference to predict drug-disease associations with high accuracy and interpretability.

This paper develops an AI-driven drug discovery pipeline for LDHA inhibitors, identifying 100+ novel compounds with high inhibition potential and drug-like properties.

The paper screens over one million compounds to identify GIRK2-PIP2 modulators using docking, MM-GBSA, ADMET, and molecular dynamics.

This study uses in silico methods to analyze Forskolin's anticancer mechanisms, revealing its targeting of the PI3K/Akt pathway and related oncogenic networks.

MedSAGE improves small molecule drug design by generating synthesizable, drug-like molecules with high predicted affinity and selectivity for therapeutic targets.

The paper identifies eight small molecules that bind to NPSL2, a regulatory element in oncomiR-1, using virtual screening and experimental validation.

The paper introduces ConfSeq, a conformation description language for 3D molecular structures, enabling sequence-based modeling and achieving state-of-the-art results in 3D molecular tasks.

EVA predicts drug responses in immunology and inflammation diseases using a pre-trained foundation model, improving early trial outcome prediction and guiding drug development strategies.

This paper predicts chemical compound taste profiles using machine learning on a labeled dataset, finding that binary relevance models with Morgan fingerprints outperform other methods.

SynVerse evaluates DL models for drug synergy prediction, finding none outperform a naive baseline and highlighting poor generalization to unseen drugs and cell lines.