AQDnet: Deep Neural Network for Protein–Ligand Docking Simulation
Koji Shiota, Akira Suma, Hiroyuki Ogawa, Takuya Yamaguchi, Akio Iida, Takahiro Hata, Mutsuyoshi Matsushita, Tatsuya Akutsu, Masaru Tateno · ACS Omega · 2023
We have developed an innovative system, AI QM Docking Net (AQDnet), which utilizes the three-dimensional structure of protein-ligand complexes to predict binding affinity. This system is novel in two respects: first, it significantly expands the training dataset by generating thousands of diverse ligand configurations for each protein-ligand complex and subsequently determining the binding energy of each configuration through quantum computation. Second, we have devised a method that incorporates the atom-centered symmetry function (ACSF), highly effective in describing molecular energies, for the prediction of protein-ligand interactions. These advancements have enabled us to effectively train a neural network to learn the protein-ligand quantum energy landscape (P-L QEL). Consequently, we have achieved a 92.6% top 1 success rate in the CASF-2016 docking power, placing first among all models assessed in the CASF-2016, thus demonstrating the exceptional docking performance of our model.