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With the recent introduction of deep learning techniques into the prediction of biomolecular structures, structure prediction performance has significantly improved, and the potential for biomedical applications has increased considerably. The prediction of protein–ligand complex structures, applicable to the atomistic understanding of biomolecular functions and the effective design of drug molecules, has also improved with the introduction of deep learning. In this paper, it is demonstrated that docking performance can be greatly enhanced by training an energy function that encapsulates physical effects using deep learning within the framework of the traditional protein–ligand docking method. The advantage of this method, called GalaxyDock-DL, lies in its minimal overfitting to the training data compared to several existing deep learning-based protein–ligand docking methods. Unlike some recent deep learning methods, it does not use information about known binding pocket center positions. Instead, the results of this docking method show a systematic dependence on the physical properties of the target protein–ligand complexes such as atomic thermal fluctuations and binding affinity. GalaxyDock-DL utilizes the global optimization technique of the conventional protein–ligand docking method, GalaxyDock, and a neural network energy function trained to stabilize the native state compared to non-native states, just as physical free energy does. This physical principle-based approach suggests directions not only for future structure prediction involving structurally flexible biomolecular complexes but also for predicting binding affinity, thereby providing guidance for the effective design of biofunctional ligands.

Computational techniques for predicting interactions of proteins and druglike molecules have often been used to search for compounds that bind a given protein with high affinity. More recently, such tools have also been applied to the reverse procedure of searching protein targets for a given compound. Among methods for predicting protein–ligand interactions, ligand-based methods relying on similarity to ligands of known interactions are effective only when similar protein–ligand interactions are known. Receptor-based methods predicting protein–ligand interactions by molecular docking are effective only when high-accuracy receptor structures and binding sites are available. Moreover, the computational cost of molecular docking tends to be too high to be applied to the entire protein structure database. In this paper, an effective target prediction method, which combines ligand similarity-based and receptor structure-based approaches, is introduced. In this method, protein–ligand docking is performed after efficient structure- and similarity-based screening. The enriched protein target database by predicted binding ligands and sites allows detection of protein targets with previously unknown ligand interactions. The method, called GalaxySagittarius, is freely available as a web server at http://galaxy.seoklab.org/sagittarius.