Drug Repurposing / Target Interaction on Vecura: Models, Methods & Use Cases

Drug Repurposing / Target Interaction on Vecura: Models, Methods & Use Cases

Introduction

The fastest path to a new medicine is often through an old one. Identifying which approved or investigational molecules bind a new target — and how tightly — is the central computational challenge of drug repurposing. On Vecura, scientists get one-click access to a full, integrated stack of drug-target interaction and repurposing tools — from ultrafast genome-scale DTI screening to physics-grade binding free energy calculations — without leaving the browser.

Background

Why It Matters

Drug repurposing — finding new therapeutic indications for compounds whose safety and pharmacokinetic profiles are already characterised — dramatically compresses development timelines and reduces attrition risk. Artificial intelligence now spans the full arc of this process, from protein structure prediction to knowledge-graph reasoning, accelerating candidate identification at every step[1]. Repurposing approved drugs reduces development cost and risk, and is an increasingly critical strategy for filling pipeline gaps — particularly against antimicrobial resistance — where conventional de novo discovery timelines are untenable[2]. Across the broader drug-discovery landscape, AI/ML approaches have curtailed candidate attrition by up to 30% and compressed timelines by 20–40%, with modern ML models predicting drug-target interactions at accuracies approaching ~85%[3]. Integration of multi-omics data with AI further accelerates target identification, drug repositioning, and de novo molecular design[4].

What It Is

Drug-target interaction (DTI) prediction is the computational estimation of whether — and how strongly — a small molecule or biologic binds a protein (or nucleic acid) target. DTI prediction underpins repurposing directly: running the same affinity machinery in reverse (one known drug screened against many targets, or many drugs screened against one target) surfaces new indications and flags off-target liabilities simultaneously. The methodological toolbox spans three complementary paradigms:ligand-basedmethods (chemical similarity, fingerprints),sequence-basedmethods (protein and molecular language-model embeddings that require no 3D structure), andstructure-basedmethods (molecular docking and end-point or alchemical free-energy calculations). Natural-product target discovery is a canonical motivating example where the target is entirely unknown and reverse screening is the only entry point[5].

Drug Repurposing / Target Interaction Models on Vecura

Vecura’s catalog spans every stage of the repurposing and target-interaction pipeline; the table below maps scientific use cases to the specific models available in-app and the rationale for each choice.

| | | | | ------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | |Use Case|Model(s)|Why| |Binding Affinity Prediction & Rescoring| | | | Sequence-based affinity (pKd) from protein + ligand |BALM,DTIGN| Language-model / GNN affinity from sequence + SMILES — no 3D pose required; ideal for fast triage across large drug–target pair matrices | | Structure-based affinity / pose rescoring |GEMS,PandaDock(score_protein_ligand_interaction) | GNN scoring on the 3D complex to re-rank docked poses; PandaDock achieves Pearson *R* ≈ 0.88 on PDBbind | | Physics-based binding free energy |Uni-GBSA| MM/GB(PB)SA endpoint free energies for tight, physically grounded ranking of a short-listed compound set | | Unsupervised binding energy |DSMBind| SE(3) denoising score-matching energy without labelled affinity data; covers protein–ligand and protein–protein interfaces | |Drug-Target Interaction & Virtual Screening| | | | Ultrafast DTI / large-library screening |SPRINT(panspecies-dti) | Co-embeds structure-aware protein representations and ligand fingerprints for genome- or library-scale DTI in a single pass | | General DTI toolkit |DeepPurpose| Unified encoder-decoder framework supporting DTI, DDI, molecular property, and PPI prediction — a flexible, well-validated baseline | | Ligand shape / fingerprint similarity screening |ROSHAMBO,FPSim2| GPU Gaussian shape overlap (ROSHAMBO) and fast bit-vector fingerprint similarity (FPSim2) to identify repurposing analogs of a confirmed binder | | Ultra-large combinatorial library search |Thompson Sampling for Virtual Screening| Active-learning bandit search over billions of enumerable products without exhaustive enumeration | |Target Identification & Off-Target / Repurposing| | | | Predict likely targets of a compound |LigTMap,ReverseLigQ| Reverse screening: LigTMap combines similarity + PSOVina docking + IF-ML scoring across 17 target classes; ReverseLigQ maps ligand → Pfam → protein to surface novel/off-targets | | Binding-site / druggable-residue detection |AF2BIND| Pinpoints small-molecule-binding residues on a target structure without a co-crystallised ligand, enabling druggability assessment | | Small-molecule–RNA interactions |SMRTnet| Extends target space to RNA, predicting binding from RNA secondary structure — important for emerging RNA-targeted repurposing campaigns | |Docking (Pose Generation for Interaction Analysis)| | | | Classical / CNN docking |AutoDock-Vina,gnina| Gold-standard search algorithm with optional CNN rescoring (gnina); produces poses suitable as input for any of the rescoring models above | | Deep-learning / flexible docking |DiffDock,DynamicBind,SurfDock,FlowDock| Diffusion- and flow-based blind and flexible docking; DynamicBind explicitly models apo→holo protein conformational change; FlowDock additionally outputs a predicted affinity | | Ligand / receptor preparation |Meeko,MolScrub| PDBQT conversion, 3D conformer generation, tautomer/protonation-state enumeration — essential for robust, reproducible docking inputs | |Complex Structure + Affinity (Co-Folding)| | | | Co-fold complex and predict affinity |Boltz-2,Boltz-2.1,AlphaFold3| Jointly predict the 3D protein–ligand (or protein/nucleic acid) complex structure; Boltz-2/2.1 additionally output a predicted binding affinity — structure and score in one run | |Drug-Drug Interaction & Combinations (Repurposing Safety)| | | | Typed DDI prediction |DeepDDI2,DeepDDI,DeepPurpose(DDI) | Predict dozens to hundreds of human-readable DDI types to flag pharmacological combination risks when repositioning a compound into a new therapeutic context | | Polypharmacy side effects |Decagon| Multi-relational GCN predicts side-effect types arising from specific drug pairs — critical for polypharmacy-heavy repurposing scenarios | | Drug-combination synergy |SynergyFinder| HSA/Loewe/Bliss/ZIP synergy scoring for evaluating whether repurposed drug combinations are synergistic, additive, or antagonistic | |Protein-Protein Affinity (Biologics Repurposing)| | | | PPI binding affinity / ΔΔG |PRODIGY,PPAP,StaB-ddG| Contact-based (PRODIGY) and ESM2-based (PPAP, StaB-ddG) affinity and mutation ΔΔG prediction for protein–protein interfaces; supports biologics repurposing and engineering |

Drug Repurposing / Target Interaction on Vecura

Notes

•Start with what you have.If you have only sequence and SMILES, begin with BALM or SPRINT. If you have a 3D complex (experimental or co-folded), proceed directly to GEMS, Uni-GBSA, or DSMBind for higher-fidelity scoring.

•Run a computational funnel.The recommended workflow is: cheap DTI/similarity screening (SPRINT, FPSim2) → docking for pose generation (Vina, DiffDock) → GNN/physics rescoring on a short-list (GEMS, Uni-GBSA). Each stage eliminates non-starters before committing compute.

•Predicted affinities are relative, not absolute.Rankings from any in silico model should be treated as prioritisation scores; experimental validation (binding assay, SPR, ITC) of top hits remains essential before advancing candidates.

•Chain models into Vecura Workflows.These modules compose natively — for example: AF2BIND (druggability) → SPRINT (library triage) → DiffDock (pose) → GEMS (rescore) → DeepDDI2 (safety check) — running end-to-end without manual file transfers.

•The catalog evolves.New models are added regularly; always consult the live model catalog in-app for the most current list, versioning, and parameter options.

Conclusion

Vecura transforms drug repurposing from a fragmented, multi-tool effort into a single connected environment where you can reason about target druggability, screen compound libraries, generate and score binding poses, and assess combination safety — all within the browser and all on the same data. Use the table above as your entry point: identify the use case that matches your current data and scientific question, select the appropriate model tier, and chain the outputs into a Workflow to move from hypothesis to ranked, safety-checked repurposing candidates in hours rather than weeks.

References

1. Artificial intelligence in drug research and development: a review of methods and applications in drug repurposing — <https://doi.org/10.1093/bib/bbag203>

1. Old drugs, new weapons: current trends in repurposing therapies against antimicrobial resistance — <https://doi.org/10.3389/jpps.2026.16158>

1. Advancing Drug Discovery with AI: Machine and Deep Learning Strategies for Target Identification and Precision Nanomedicine — <https://doi.org/10.2147/ijn.s600651>

1. Multi-omics and artificial intelligence for precision drug discovery and potential clinical applications — <https://doi.org/10.1038/s41392-026-02631-6>

1. Progress in Natural Products Target Discovery Technology — <https://doi.org/10.1002/mco2.70777>