- FINDSITEcomb: A tool for large scale virtual ligand screening. It offers the advantage that comparable results are obtained when predicted or experimental structures are used. The user can either provide a protein structure in PDB format or a protein sequence whose structure will first be predicted prior to its use in virtual ligand screening.
- FINDSITEcomb2.0: An upgrade of the FINDSITEcomb algorithm that was compared to several commercially and freely available docking programs against the DUD set.
- FRAGSITE: A fragment-based machine learning algorithm that scores protein-ligand binding for ligand virtual screening.
- ENTPRISE: An algorithm for predicting human disease-associated amino acid mutations from sequence entropy and predicted protein structures.
- ENTPRISE-X: An algorithm for predicting human disease-associated frameshift & nonsense mutations.
- DR. PRODIS: A comprehensive prediction of drug-protein interactions, side effects, toxicity and disease associations for the human proteome.
- Know-GENE: A knowledge-based approach to predict gene–disease associations.
- MEDICASCY: A multi-label based boosted random forest machine learning method that predicts small molecule’s side effects, indications, efficacy and mode of action proteins.
- MOATAI-VIR: An AI algorithm that predicts severe adverse events and molecular features for COVID-19’s complications
Protein Structure Prediction
- DESTINI: A deep-learning based contact-driven protein structure prediction tool.