drug-target-interaction

A Deep Learning Toolkit for DTI, Drug Property, PPI, DDI, Protein Function Prediction (Bioinformatics)

SSM-DTA: Breaking the Barriers of Data Scarcity in Drug-Target Affinity Prediction (Briefings in Bioinformatics 2023)

ExplainBind: Explainable Physicochemical Determinants of Protein–Ligand Binding via Non-Covalent Interactions

TAG-DTA: Binding Region-Guided Strategy to Predict Drug-Target Affinity Using Transformers

Calibrated abstention benchmark for drug–target interaction prediction, grounded in physical difficulty coordinates

GENNDTI is a machine learning method that predicts drug-target interactions using a graph neural network enhanced by router nodes, effectively integrating biological properties of drugs and targets.

A state-of-the-art Drug-Target Affinity (DTA) prediction model combining Graph Isomorphism Networks (GIN) with Gated Bilinear Fusion and end-to-end target protein CNN representations, outperforming the GraphDTA baseline.

GenLoop — a closed generative drug-design loop on dtSFM; directed evolution of small molecules (Reddy 2026). Generate → encoder-rerank → AlphaFold-3 verify → LoRA-refine.

Drug-Target Interaction prediction using unifying of graph regularized nuclear norm with bilinear factorization

An ensemble method implementation to predict drug–target interactions using embeddings and metadata

Publication-ready release of a two-phase graph ML thesis: Phase 1 edge-aware graph transformer for KIBA drug-target affinity; Phase 2 HGDR heterogeneous-graph medication recommendation on MIMIC-III with DDI control.

MVP de cribado virtual asistido por IA y docking molecular con biblioteca botanica de Ecuador y Amazonia.

Reproducible NPPI-Net research code, retained metrics, fixed splits, and paper workflows

A weighted average ensemble method implementation to predict drug–target interactions