A foundation model for protein-ligand affinity prediction through jointly optimizing virtual screening and hit-to-lead optimization
1. LigUnity is a novel foundation model that jointly optimizes protein-ligand virtual screening and hit-to-lead optimization, leveraging the synergy between these two tasks to enhance the overall drug discovery pipeline.
2. The model uses contrastive learning to distinguish between active and inactive ligands during virtual screening and a listwise ranking approach to optimize the affinity prediction for hit-to-lead optimization, improving performance across various benchmarks.
3. LigUnity outperforms 24 competing methods in virtual screening tasks on several benchmarks, such as DUD-E, Dekois 2.0, and LIT-PCBA, with significant improvements in the enrichment factor and faster screening speed.
4. The model also excels in hit-to-lead optimization tasks, achieving superior performance compared to traditional computational methods like free energy perturbation (FEP), particularly in zero-shot and few-shot settings.
5. The integration of LigUnity in an active learning framework shows its ability to identify optimal binding ligands for TYK2, a therapeutic target for autoimmune diseases, achieving over 40% improved prediction performance.
6. LigUnity's versatility is highlighted through its application to diverse settings, including split-by-time, split-by-scaffold, and split-by-unit settings, where it consistently outperforms other methods and generalizes well to unseen proteins and novel chemical scaffolds.
7. The ability to handle different assay types, including those using percentage units and real-world datasets, makes LigUnity an ideal tool for drug discovery, offering significant improvements over existing methods in both speed and accuracy.
📜Paper:
biorxiv.org/content/10.1101/…
#DrugDiscovery #MachineLearning #VirtualScreening #Bioinformatics #AIinPharma #ComputationalChemistry #ProteinLigandAffinity #DeepLearning #HitToLead #ActiveLearning #ProteinModeling #LigandOptimization